"""DO NOT EDIT THIS FILE DIRECTLY, INSTEAD RUN `make package-data`""" import datetime from decimal import Decimal from .types import * __all__ = ('providers',) providers: list[Provider] = [ Provider( id='anthropic', name='Anthropic', api_pattern='https://api\\.anthropic\\.com', pricing_urls=['https://www.anthropic.com/pricing#api'], model_match=ClauseContains(contains='claude'), provider_match=ClauseContains(contains='anthropic'), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=True), UsageExtractorMapping(path='cache_creation_input_tokens', dest='input_tokens', required=False), UsageExtractorMapping(path='cache_read_input_tokens', dest='input_tokens', required=False), UsageExtractorMapping( path='cache_creation_input_tokens', dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['cache_creation', 'ephemeral_5m_input_tokens'], dest='cache_write_5m_tokens', required=False, ), UsageExtractorMapping( path=['cache_creation', 'ephemeral_1h_input_tokens'], dest='cache_write_1h_tokens', required=False, ), UsageExtractorMapping(path='cache_read_input_tokens', dest='cache_read_tokens', required=False), UsageExtractorMapping( path=['server_tool_use', 'web_search_requests'], dest='web_searches', required=False ), UsageExtractorMapping( path=['output_tokens_details', 'thinking_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping(path='cached_tokens', dest='cache_read_tokens', required=False), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), ], models=[ ModelInfo( id='claude-2', match=ClauseOr(or_=[ClauseStartsWith(starts_with='claude-2'), ClauseContains(contains='claude-v2')]), name='Claude 2.0 / 2.1', description="Claude 2 is Anthropic's previous generation model, offering reliable performance for various tasks. This includes Claude 2.0 and Claude 2.1.\n", context_window=200000, prices=ModelPrice(input_mtok=Decimal('8'), output_mtok=Decimal('24')), ), ModelInfo( id='claude-3-5-haiku-latest', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='claude-3-5-haiku'), ClauseStartsWith(starts_with='claude-3.5-haiku'), ] ), name='Claude Haiku 3.5', description='Fastest, most cost-effective model', context_window=200000, price_comments='One-hour cache writes cost 2x the base input price. Ref: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing', prices=ModelPrice( input_mtok=Decimal('0.8'), cache_write_mtok=Decimal('1'), cache_read_mtok=Decimal('0.08'), output_mtok=Decimal('4'), cache_write_1h_mtok=Decimal('1.6'), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='claude-3-5-sonnet', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='claude-3-5-sonnet'), ClauseStartsWith(starts_with='claude-3.5-sonnet'), ] ), name='Claude Sonnet 3.5', description='Claude 3.5 Sonnet is an ideal balance of intelligence and speed for enterprise workloads. Maximum utility at a lower price, dependable, balanced for scaled deployments.', context_window=200000, price_comments='One-hour cache writes cost 2x the base input price. Ref: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing', prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), cache_write_1h_mtok=Decimal('6'), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='claude-3-7-sonnet-latest', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='claude-3-7-sonnet'), ClauseStartsWith(starts_with='claude-3.7-sonnet'), ClauseStartsWith(starts_with='claude-sonnet-3.7'), ClauseStartsWith(starts_with='claude-sonnet-3-7'), ] ), name='Claude Sonnet 3.7', description='Claude 3.7 Sonnet is an advanced large language model with improved reasoning, coding, and problem-solving capabilities.', context_window=200000, price_comments='One-hour cache writes cost 2x the base input price. Ref: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing', prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), cache_write_1h_mtok=Decimal('6'), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='claude-3-haiku', match=ClauseStartsWith(starts_with='claude-3-haiku'), name='Claude Haiku 3', description='Fastest, most cost-effective model', context_window=200000, price_comments='One-hour cache writes cost 2x the base input price. Ref: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing', prices=ModelPrice( input_mtok=Decimal('0.25'), cache_write_mtok=Decimal('0.3'), cache_read_mtok=Decimal('0.03'), output_mtok=Decimal('1.25'), cache_write_1h_mtok=Decimal('0.5'), ), ), ModelInfo( id='claude-3-opus-latest', match=ClauseStartsWith(starts_with='claude-3-opus'), name='Claude Opus 3', description="Claude 3 Opus was Anthropic's most powerful model for highly complex tasks. It boasts top-level performance, intelligence, fluency, and understanding.", context_window=200000, price_comments='One-hour cache writes cost 2x the base input price. Ref: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing', prices=ModelPrice( input_mtok=Decimal('15'), cache_write_mtok=Decimal('18.75'), cache_read_mtok=Decimal('1.5'), output_mtok=Decimal('75'), cache_write_1h_mtok=Decimal('30'), ), ), ModelInfo( id='claude-3-sonnet', match=ClauseStartsWith(starts_with='claude-3-sonnet'), name='Claude 3 Sonnet', description='Claude 3 Sonnet is an ideal balance of intelligence and speed for enterprise workloads. Maximum utility at a lower price, dependable, balanced for scaled deployments.', context_window=200000, price_comments='One-hour cache writes cost 2x the base input price. Ref: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing', prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), cache_write_1h_mtok=Decimal('6'), ), ), ModelInfo( id='claude-fable-5', match=ClauseOr( or_=[ClauseEquals(equals='claude-fable-5'), ClauseRegex(regex='^claude-fable-5-\\d{8}$')] ), name='Claude Fable 5', description='For demanding reasoning and long-horizon agentic work', context_window=1000000, price_comments='Flat pricing across full 1M context window (no tiered pricing). Ref: https://platform.claude.com/docs/en/about-claude/pricing#long-context-pricing Prompt caching ref: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing', prices=ModelPrice( input_mtok=Decimal('10'), cache_write_mtok=Decimal('12.5'), cache_read_mtok=Decimal('1'), output_mtok=Decimal('50'), cache_write_1h_mtok=Decimal('20'), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='claude-fable-5-1', match=ClauseOr( or_=[ClauseEquals(equals='claude-fable-5-1'), ClauseRegex(regex='^claude-fable-5-1-\\d{8}$')] ), name='Claude Fable 5.1', description="Anthropic's most capable widely released model for demanding reasoning and long-horizon agentic work", context_window=1000000, price_comments='Flat pricing across full 1M context window (no tiered pricing). Cache hits are 0.025x base input (not the usual 0.1x), unique to Fable 5.1 and Mythos 5.1. Ref: https://platform.claude.com/docs/en/about-claude/pricing#model-pricing Prompt caching ref: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing', prices=ModelPrice( input_mtok=Decimal('10'), cache_write_mtok=Decimal('12.5'), cache_read_mtok=Decimal('0.25'), output_mtok=Decimal('50'), cache_write_1h_mtok=Decimal('20'), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='claude-haiku-4-5', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='claude-haiku-4-5'), ClauseStartsWith(starts_with='claude-haiku-4.5'), ClauseStartsWith(starts_with='claude-4-5-haiku'), ClauseStartsWith(starts_with='claude-4.5-haiku'), ] ), name='Claude Haiku 4.5', description='Fastest and most intelligent Haiku model', context_window=200000, price_comments='One-hour cache writes cost 2x the base input price. Ref: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing', prices=ModelPrice( input_mtok=Decimal('1'), cache_write_mtok=Decimal('1.25'), cache_read_mtok=Decimal('0.1'), output_mtok=Decimal('5'), cache_write_1h_mtok=Decimal('2'), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='claude-opus-4-0', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='claude-opus-4-0'), ClauseStartsWith(starts_with='claude-4-opus'), ClauseEquals(equals='claude-opus-4'), ClauseEquals(equals='claude-opus-4-20250514'), ] ), name='Claude Opus 4', description='Most intelligent model for complex tasks', context_window=200000, price_comments='One-hour cache writes cost 2x the base input price. Ref: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing', prices=ModelPrice( input_mtok=Decimal('15'), cache_write_mtok=Decimal('18.75'), cache_read_mtok=Decimal('1.5'), output_mtok=Decimal('75'), cache_write_1h_mtok=Decimal('30'), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='claude-opus-4-1', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='claude-opus-4-1'), ClauseStartsWith(starts_with='claude-opus-4.1'), ] ), name='Claude Opus 4.1', description='Most intelligent model for complex tasks', context_window=200000, price_comments='One-hour cache writes cost 2x the base input price. Ref: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing', prices=ModelPrice( input_mtok=Decimal('15'), cache_write_mtok=Decimal('18.75'), cache_read_mtok=Decimal('1.5'), output_mtok=Decimal('75'), cache_write_1h_mtok=Decimal('30'), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='claude-opus-4-5', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='claude-opus-4-5'), ClauseStartsWith(starts_with='claude-opus-4.5'), ClauseStartsWith(starts_with='claude-4-5-opus'), ClauseStartsWith(starts_with='claude-4.5-opus'), ] ), name='Claude Opus 4.5', description='Premium model combining maximum intelligence with practical performance', context_window=200000, price_comments='One-hour cache writes cost 2x the base input price. Ref: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing', prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), cache_write_1h_mtok=Decimal('10'), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='claude-opus-4-6', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='claude-opus-4-6'), ClauseStartsWith(starts_with='claude-opus-4.6'), ClauseStartsWith(starts_with='claude-4-6-opus'), ClauseStartsWith(starts_with='claude-4.6-opus'), ] ), name='Claude Opus 4.6', description='Our most intelligent model for building agents and coding', context_window=1000000, price_comments='One-hour cache writes cost 2x the base input price. The 1M context window is generally available. Refs: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing, https://platform.claude.com/docs/en/build-with-claude/context-windows#context-window-sizes-by-model', prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('5'), tiers=[Tier(start=200000, price=Decimal('10'))]), cache_write_mtok=TieredPrices( base=Decimal('6.25'), tiers=[Tier(start=200000, price=Decimal('12.5'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.5'), tiers=[Tier(start=200000, price=Decimal('1'))] ), output_mtok=TieredPrices( base=Decimal('25'), tiers=[Tier(start=200000, price=Decimal('37.5'))] ), cache_write_1h_mtok=TieredPrices( base=Decimal('10'), tiers=[Tier(start=200000, price=Decimal('20'))] ), web_searches_kcount=Decimal('10'), ) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2026, 3, 13)), prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), cache_write_1h_mtok=Decimal('10'), web_searches_kcount=Decimal('10'), ), ), ], ), ModelInfo( id='claude-opus-4-7', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='claude-opus-4-7'), ClauseStartsWith(starts_with='claude-opus-4.7'), ClauseStartsWith(starts_with='claude-4-7-opus'), ClauseStartsWith(starts_with='claude-4.7-opus'), ] ), name='Claude Opus 4.7', description='Our most capable model for complex reasoning and agentic coding', context_window=1000000, price_comments='Flat pricing across full 1M context window (no tiered pricing). Ref: https://platform.claude.com/docs/en/about-claude/pricing#long-context-pricing Prompt caching ref: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing', prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), cache_write_1h_mtok=Decimal('10'), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='claude-opus-4-8', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='claude-opus-4-8'), ClauseStartsWith(starts_with='claude-opus-4.8'), ClauseStartsWith(starts_with='claude-4-8-opus'), ClauseStartsWith(starts_with='claude-4.8-opus'), ] ), name='Claude Opus 4.8', description='Our most capable model for complex reasoning and agentic coding', context_window=1000000, price_comments='Flat pricing across full 1M context window (no tiered pricing). Ref: https://platform.claude.com/docs/en/about-claude/pricing#long-context-pricing Prompt caching ref: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing', prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), cache_write_1h_mtok=Decimal('10'), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='claude-opus-5', match=ClauseOr( or_=[ ClauseEquals(equals='claude-opus-5'), ClauseRegex(regex='^claude-opus-5-\\d{8}$'), ClauseStartsWith(starts_with='claude-opus-5.0'), ClauseStartsWith(starts_with='claude-5-opus'), ClauseStartsWith(starts_with='claude-5.0-opus'), ] ), name='Claude Opus 5', description='For complex agentic coding and enterprise work', context_window=1000000, price_comments='Flat pricing across full 1M context window (no tiered pricing). Refs: https://platform.claude.com/docs/en/about-claude/pricing#long-context-pricing and https://platform.claude.com/docs/en/agents-and-tools/tool-use/web-search-tool Prompt caching ref: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing', prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), cache_write_1h_mtok=Decimal('10'), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='claude-opus-5-5', match=ClauseOr( or_=[ ClauseEquals(equals='claude-opus-5-5'), ClauseRegex(regex='^claude-opus-5-5-\\d{8}$'), ClauseStartsWith(starts_with='claude-opus-5.5'), ClauseStartsWith(starts_with='claude-5-5-opus'), ClauseStartsWith(starts_with='claude-5.5-opus'), ] ), name='Claude Opus 5.5', description='For long-running agentic coding and knowledge work', context_window=1000000, price_comments='Flat pricing across full 1M context window (no tiered pricing). Cache hits are 0.05x base input (not the usual 0.1x), unique to Opus 5.5. Ref: https://platform.claude.com/docs/en/about-claude/pricing#model-pricing Prompt caching ref: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing', prices=ModelPrice( input_mtok=Decimal('4'), cache_write_mtok=Decimal('5'), cache_read_mtok=Decimal('0.2'), output_mtok=Decimal('20'), cache_write_1h_mtok=Decimal('8'), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='claude-sonnet-4-0', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='claude-sonnet-4-2025'), ClauseStartsWith(starts_with='claude-sonnet-4-0'), ClauseStartsWith(starts_with='claude-sonnet-4@'), ClauseEquals(equals='claude-sonnet-4'), ClauseStartsWith(starts_with='claude-4-sonnet'), ] ), name='Claude Sonnet 4', description='Optimal balance of intelligence, cost, and speed', context_window=200000, price_comments='One-hour cache writes cost 2x the base input price. Ref: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing', prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), cache_write_1h_mtok=Decimal('6'), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='claude-sonnet-4-5', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='claude-sonnet-4-5'), ClauseStartsWith(starts_with='claude-sonnet-4.5'), ] ), name='Claude Sonnet 4.5', description='Our best combination of speed and intelligence', context_window=200000, price_comments='One-hour cache writes cost 2x the base input price. The 1M context beta was retired on 2026-04-30; requests over 200k now error. The >200k tiers describe historical usage from before retirement. Refs: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing, https://platform.claude.com/docs/en/release-notes/overview', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('3'), tiers=[Tier(start=200000, price=Decimal('6'))]), cache_write_mtok=TieredPrices( base=Decimal('3.75'), tiers=[Tier(start=200000, price=Decimal('7.5'))] ), cache_read_mtok=TieredPrices(base=Decimal('0.3'), tiers=[Tier(start=200000, price=Decimal('0.6'))]), output_mtok=TieredPrices(base=Decimal('15'), tiers=[Tier(start=200000, price=Decimal('22.5'))]), cache_write_1h_mtok=TieredPrices( base=Decimal('6'), tiers=[Tier(start=200000, price=Decimal('12'))] ), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='claude-sonnet-4-6', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='claude-sonnet-4-6'), ClauseStartsWith(starts_with='claude-sonnet-4.6'), ] ), name='Claude Sonnet 4.6', description='Our best combination of speed and intelligence', context_window=1000000, price_comments='One-hour cache writes cost 2x the base input price. Ref: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing', prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('3'), tiers=[Tier(start=200000, price=Decimal('6'))]), cache_write_mtok=TieredPrices( base=Decimal('3.75'), tiers=[Tier(start=200000, price=Decimal('7.5'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.3'), tiers=[Tier(start=200000, price=Decimal('0.6'))] ), output_mtok=TieredPrices( base=Decimal('15'), tiers=[Tier(start=200000, price=Decimal('22.5'))] ), cache_write_1h_mtok=TieredPrices( base=Decimal('6'), tiers=[Tier(start=200000, price=Decimal('12'))] ), web_searches_kcount=Decimal('10'), ) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2026, 3, 13)), prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), cache_write_1h_mtok=Decimal('6'), web_searches_kcount=Decimal('10'), ), ), ], ), ModelInfo( id='claude-sonnet-5', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='claude-sonnet-5'), ClauseStartsWith(starts_with='claude-sonnet-5.0'), ClauseStartsWith(starts_with='claude-5-sonnet'), ClauseStartsWith(starts_with='claude-5.0-sonnet'), ] ), name='Claude Sonnet 5', description='Our most agentic Sonnet model, approaching Opus 4.8 capability at lower cost', context_window=1000000, price_comments='Flat pricing across the full 1M context window (no tiered pricing). Anthropic made the introductory $2/$10 per MTok rates permanent and cancelled the previously scheduled 2026-09-01 increase. Ref: https://platform.claude.com/docs/en/about-claude/pricing Prompt caching ref: https://platform.claude.com/docs/en/build-with-claude/prompt-caching#pricing', prices=ModelPrice( input_mtok=Decimal('2'), cache_write_mtok=Decimal('2.5'), cache_read_mtok=Decimal('0.2'), output_mtok=Decimal('10'), cache_write_1h_mtok=Decimal('4'), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='claude-v1', match=ClauseEquals(equals='claude-v1'), description='Retired, here to match price sources', prices=ModelPrice(input_mtok=Decimal('8'), output_mtok=Decimal('24')), ), ], ), Provider( id='arcee', name='Arcee', api_pattern='https://api\\.arcee\\.ai(?:/|$)', pricing_urls=['https://docs.arcee.ai/get-started/pricing', 'https://api.arcee.ai/api/v1/models'], description='OpenAI-compatible hosted inference through the Arcee Platform API.', price_comments='Covers the complete public Text Models pricing table. The table publishes input, cached-input, and output token rates. Context windows and rates for images, requests, and cache writes are omitted because the public pricing page does not publish them. Model IDs use third-party namespaces, so they do not infer the Arcee provider without an Arcee provider ID or API URL.', provider_match=ClauseContains(contains='arcee'), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), ], models=[ ModelInfo( id='deepseek/deepseek-v4-flash-latest', match=ClauseEquals(equals='deepseek/deepseek-v4-flash-latest'), name='DeepSeek V4 Flash', prices=ModelPrice( input_mtok=Decimal('0.14'), cache_read_mtok=Decimal('0.028'), output_mtok=Decimal('0.28') ), ), ModelInfo( id='deepseek/deepseek-v4-pro', match=ClauseEquals(equals='deepseek/deepseek-v4-pro'), name='DeepSeek V4 Pro', prices=ModelPrice( input_mtok=Decimal('1.74'), cache_read_mtok=Decimal('0.2'), output_mtok=Decimal('3.48') ), ), ModelInfo( id='moonshotai/kimi-k3', match=ClauseEquals(equals='moonshotai/kimi-k3'), name='Kimi K3', prices=ModelPrice(input_mtok=Decimal('3'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15')), ), ModelInfo( id='thinkingmachines/inkling-small', match=ClauseEquals(equals='thinkingmachines/inkling-small'), name='Inkling Small', prices=ModelPrice( input_mtok=Decimal('0.5'), cache_read_mtok=Decimal('0.1'), output_mtok=Decimal('1.2') ), ), ModelInfo( id='trinity-large-thinking', match=ClauseEquals(equals='trinity-large-thinking'), name='Trinity Large Thinking', prices=ModelPrice( input_mtok=Decimal('0.25'), cache_read_mtok=Decimal('0.06'), output_mtok=Decimal('0.8') ), ), ModelInfo( id='zai-org/glm-5.2', match=ClauseEquals(equals='zai-org/glm-5.2'), name='GLM 5.2', prices=ModelPrice( input_mtok=Decimal('1.4'), cache_read_mtok=Decimal('0.26'), output_mtok=Decimal('4.4') ), ), ], ), Provider( id='avian', name='Avian', api_pattern='https://api\\.avian\\.io', pricing_urls=['https://avian.io/pricing/'], price_comments="Prices and model IDs checked against https://avian.io/docs/ on 2026-08-25. Cache prices use Avian's documented cache-read rates. The previously listed Meta Llama models are no longer in Avian's catalog and are deprecated.", models=[ ModelInfo( id='Meta-Llama-3.1-405B-Instruct', match=ClauseEquals(equals='Meta-Llama-3.1-405B-Instruct'), price_comments="No longer listed in Avian's model catalog as of 2026-08-25.", deprecated=True, prices=ModelPrice(input_mtok=Decimal('1.5'), output_mtok=Decimal('1.5')), ), ModelInfo( id='Meta-Llama-3.1-70B-Instruct', match=ClauseEquals(equals='Meta-Llama-3.1-70B-Instruct'), price_comments="No longer listed in Avian's model catalog as of 2026-08-25.", deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.45'), output_mtok=Decimal('0.45')), ), ModelInfo( id='Meta-Llama-3.1-8B-Instruct', match=ClauseEquals(equals='Meta-Llama-3.1-8B-Instruct'), price_comments="No longer listed in Avian's model catalog as of 2026-08-25.", deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ), ModelInfo( id='Meta-Llama-3.3-70B-Instruct', match=ClauseEquals(equals='Meta-Llama-3.3-70B-Instruct'), price_comments="No longer listed in Avian's model catalog as of 2026-08-25.", deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.45'), output_mtok=Decimal('0.45')), ), ModelInfo( id='deepseek/deepseek-v3.2', match=ClauseEquals(equals='deepseek/deepseek-v3.2'), name='DeepSeek V3.2 (Legacy)', context_window=163000, prices=ModelPrice( input_mtok=Decimal('0.23'), cache_read_mtok=Decimal('0.012'), output_mtok=Decimal('0.33') ), ), ModelInfo( id='deepseek/deepseek-v4-flash', match=ClauseEquals(equals='deepseek/deepseek-v4-flash'), name='DeepSeek V4 Flash', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('0.0805'), cache_read_mtok=Decimal('0.0165'), output_mtok=Decimal('0.161') ), ), ModelInfo( id='deepseek/deepseek-v4-pro', match=ClauseEquals(equals='deepseek/deepseek-v4-pro'), name='DeepSeek V4 Pro', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('1.305'), cache_read_mtok=Decimal('0.10875'), output_mtok=Decimal('2.61') ), ), ModelInfo( id='deepseek/deepseek-v4-pro-0813', match=ClauseEquals(equals='deepseek/deepseek-v4-pro-0813'), name='DeepSeek V4 Pro 0813', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('0.594'), cache_read_mtok=Decimal('0.0198'), output_mtok=Decimal('1.782') ), ), ModelInfo( id='minimax/minimax-m2.5', match=ClauseEquals(equals='minimax/minimax-m2.5'), name='MiniMax M2.5', context_window=196000, prices=ModelPrice( input_mtok=Decimal('0.27'), cache_read_mtok=Decimal('0.15'), output_mtok=Decimal('1.08') ), ), ModelInfo( id='moonshotai/kimi-k2.5', match=ClauseEquals(equals='moonshotai/kimi-k2.5'), name='Kimi K2.5', context_window=262000, prices=ModelPrice( input_mtok=Decimal('0.45'), cache_read_mtok=Decimal('0.225'), output_mtok=Decimal('2.2') ), ), ModelInfo( id='moonshotai/kimi-k2.6', match=ClauseEquals(equals='moonshotai/kimi-k2.6'), name='Kimi K2.6', context_window=262000, prices=ModelPrice( input_mtok=Decimal('0.95'), cache_read_mtok=Decimal('0.16'), output_mtok=Decimal('4') ), ), ModelInfo( id='xiaomi/mimo-v2.5', match=ClauseEquals(equals='xiaomi/mimo-v2.5'), name='MiMo-V2.5 Small', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('0.2'), cache_read_mtok=Decimal('0.05'), output_mtok=Decimal('0.4') ), ), ModelInfo( id='xiaomi/mimo-v2.5-pro', match=ClauseEquals(equals='xiaomi/mimo-v2.5-pro'), name='MiMo-V2.5 Pro', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('0.435'), cache_read_mtok=Decimal('0.0036'), output_mtok=Decimal('0.87') ), ), ModelInfo( id='z-ai/glm-4.7', match=ClauseEquals(equals='z-ai/glm-4.7'), name='GLM-4.7', context_window=202000, prices=ModelPrice( input_mtok=Decimal('0.388'), cache_read_mtok=Decimal('0.097'), output_mtok=Decimal('1.806') ), ), ModelInfo( id='z-ai/glm-5', match=ClauseEquals(equals='z-ai/glm-5'), name='GLM-5', context_window=205000, prices=ModelPrice( input_mtok=Decimal('0.516'), cache_read_mtok=Decimal('0.129'), output_mtok=Decimal('2.322') ), ), ModelInfo( id='z-ai/glm-5.1', match=ClauseEquals(equals='z-ai/glm-5.1'), name='GLM-5.1', context_window=202000, prices=ModelPrice( input_mtok=Decimal('0.743'), cache_read_mtok=Decimal('0.186'), output_mtok=Decimal('2.971') ), ), ModelInfo( id='z-ai/glm-5.2', match=ClauseEquals(equals='z-ai/glm-5.2'), name='GLM-5.2', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('0.495'), cache_read_mtok=Decimal('0.124'), output_mtok=Decimal('1.733') ), ), ], ), Provider( id='aws', name='AWS Bedrock', api_pattern='https://bedrock-runtime\\.[a-z0-9-]+\\.amazonaws\\.com(/|$)', pricing_urls=['https://aws.amazon.com/bedrock/pricing/'], provider_match=ClauseOr(or_=[ClauseContains(contains='bedrock'), ClauseContains(contains='amazon')]), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='inputTokens', dest='input_tokens', required=True), UsageExtractorMapping(path='cacheReadInputTokens', dest='input_tokens', required=False), UsageExtractorMapping(path='cacheWriteInputTokens', dest='input_tokens', required=False), UsageExtractorMapping(path='cacheReadInputTokens', dest='cache_read_tokens', required=False), UsageExtractorMapping(path='cacheWriteInputTokens', dest='cache_write_tokens', required=False), UsageExtractorMapping(path='outputTokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=True), UsageExtractorMapping(path='cache_creation_input_tokens', dest='input_tokens', required=False), UsageExtractorMapping(path='cache_read_input_tokens', dest='input_tokens', required=False), UsageExtractorMapping( path='cache_creation_input_tokens', dest='cache_write_tokens', required=False ), UsageExtractorMapping(path='cache_read_input_tokens', dest='cache_read_tokens', required=False), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=True), ], api_flavor='anthropic', model_path='model', ), ], models=[ ModelInfo( id='amazon.nova-2-sonic-v1:0', match=ClauseContains(contains='amazon.nova-2-sonic'), name='Nova 2 Sonic', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('0.33'), output_mtok=Decimal('2.75'), input_audio_mtok=Decimal('3'), output_audio_mtok=Decimal('12'), ), ), ModelInfo( id='amazon.nova-lite-v1:0', match=ClauseContains(contains='amazon.nova-lite'), name='Nova Lite', description='Amazon Nova Lite 1.0 is a very low-cost multimodal model from Amazon that focused on fast processing of image, video, and text inputs to generate text output. Amazon Nova Lite can handle real-time customer interactions, document analysis, and visual question-answering tasks with high accuracy.', context_window=300000, prices=ModelPrice( input_mtok=Decimal('0.06'), cache_read_mtok=Decimal('0.015'), output_mtok=Decimal('0.24') ), ), ModelInfo( id='amazon.nova-micro-v1:0', match=ClauseContains(contains='amazon.nova-micro'), name='Nova Micro', description='Amazon Nova Micro 1.0 is a text-only model that delivers the lowest latency responses in the Amazon Nova family of models at a very low cost. With a context length of 128K tokens and optimized for speed and cost, Amazon Nova Micro excels at tasks such as text summarization, translation, content classification, interactive chat, and brainstorming. It has simple mathematical reasoning and coding abilities.', context_window=128000, prices=ModelPrice( input_mtok=Decimal('0.035'), cache_read_mtok=Decimal('0.00875'), output_mtok=Decimal('0.14') ), ), ModelInfo( id='amazon.nova-premier-v1:0', match=ClauseContains(contains='amazon.nova-premier'), name='Nova Premier', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('2.5'), cache_read_mtok=Decimal('0.625'), output_mtok=Decimal('12.5') ), ), ModelInfo( id='amazon.nova-pro-v1:0', match=ClauseContains(contains='amazon.nova-pro'), name='Nova Pro', description='Amazon Nova Pro 1.0 is a capable multimodal model from Amazon focused on providing a combination of accuracy, speed, and cost for a wide range of tasks. As of December 2024, it achieves state-of-the-art performance on key benchmarks including visual question answering (TextVQA) and video understanding (VATEX).', context_window=300000, prices=ModelPrice( input_mtok=Decimal('0.8'), cache_read_mtok=Decimal('0.2'), output_mtok=Decimal('3.2') ), ), ModelInfo( id='amazon.nova-sonic-v1:0', match=ClauseContains(contains='amazon.nova-sonic'), name='Nova Sonic', prices=ModelPrice( input_mtok=Decimal('0.06'), output_mtok=Decimal('0.24'), input_audio_mtok=Decimal('3.4'), output_audio_mtok=Decimal('13.6'), ), ), ModelInfo( id='amazon.titan-embed-text-v1', match=ClauseContains(contains='amazon.titan-embed-text'), name='Titan Embeddings G1 - Text', context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.1')), ), ModelInfo( id='amazon.titan-text-express-v1', match=ClauseContains(contains='titan-text-express'), name='Titan Text G1 - Express', prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.6')), ), ModelInfo( id='amazon.titan-text-lite-v1', match=ClauseContains(contains='titan-text-lite'), name='Titan Text G1 - Lite', prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.2')), ), ModelInfo( id='deepseek.r1-v1:0', match=ClauseContains(contains='deepseek.r1'), name='DeepSeek-R1', context_window=128000, prices=ModelPrice(input_mtok=Decimal('1.35'), output_mtok=Decimal('5.4')), ), ModelInfo( id='global.amazon.nova-2-lite-v1:0', match=ClauseContains(contains='global.amazon.nova-2-lite'), name='Nova 2 Lite', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('0.3'), cache_read_mtok=Decimal('0.075'), output_mtok=Decimal('2.5') ), ), ModelInfo( id='global.anthropic.claude-fable-5-1-v1:0', match=ClauseOr( or_=[ ClauseEndsWith(ends_with='global.anthropic.claude-fable-5-1'), ClauseContains(contains='global.anthropic.claude-fable-5-1-v1'), ] ), context_window=1000000, price_comments='Global endpoint (no premium). Cache hits are 0.025x base input (not the usual 0.1x), unique to Fable 5.1 and Mythos 5.1. Ref: https://platform.claude.com/docs/en/about-claude/pricing#model-pricing Model ID ref: https://platform.claude.com/docs/en/build-with-claude/claude-in-amazon-bedrock', prices=ModelPrice( input_mtok=Decimal('10'), cache_write_mtok=Decimal('12.5'), cache_read_mtok=Decimal('0.25'), output_mtok=Decimal('50'), ), ), ModelInfo( id='global.anthropic.claude-fable-5-v1:0', match=ClauseOr( or_=[ ClauseEndsWith(ends_with='global.anthropic.claude-fable-5'), ClauseContains(contains='global.anthropic.claude-fable-5-v1'), ] ), context_window=1000000, prices=ModelPrice( input_mtok=Decimal('10'), cache_write_mtok=Decimal('12.5'), cache_read_mtok=Decimal('1'), output_mtok=Decimal('50'), ), ), ModelInfo( id='global.anthropic.claude-haiku-4-5-20251001-v1:0', match=ClauseContains(contains='global.anthropic.claude-haiku-4-5-20251001'), context_window=200000, prices=ModelPrice( input_mtok=Decimal('1'), cache_write_mtok=Decimal('1.25'), cache_read_mtok=Decimal('0.1'), output_mtok=Decimal('5'), ), ), ModelInfo( id='global.anthropic.claude-opus-4-5-v1:0', match=ClauseContains(contains='global.anthropic.claude-opus-4-5'), context_window=200000, prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), ), ), ModelInfo( id='global.anthropic.claude-opus-4-6-v1:0', match=ClauseContains(contains='global.anthropic.claude-opus-4-6'), context_window=1000000, prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), ), ), ModelInfo( id='global.anthropic.claude-opus-4-7-v1:0', match=ClauseContains(contains='global.anthropic.claude-opus-4-7'), context_window=1000000, prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), ), ), ModelInfo( id='global.anthropic.claude-opus-4-8-v1:0', match=ClauseContains(contains='global.anthropic.claude-opus-4-8'), context_window=1000000, prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), ), ), ModelInfo( id='global.anthropic.claude-opus-5', match=ClauseOr( or_=[ ClauseEndsWith(ends_with='global.anthropic.claude-opus-5'), ClauseContains(contains='global.anthropic.claude-opus-5-v1'), ] ), context_window=1000000, prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), ), ), ModelInfo( id='global.anthropic.claude-opus-5-5', match=ClauseOr( or_=[ ClauseEndsWith(ends_with='global.anthropic.claude-opus-5-5'), ClauseContains(contains='global.anthropic.claude-opus-5-5-v1'), ] ), context_window=1000000, price_comments='Global endpoint (no premium). Cache hits are 0.05x base input (not the usual 0.1x), unique to Opus 5.5. Ref: AWS price list API, AmazonBedrockFoundationModels "Claude Opus 5.5 (Amazon Bedrock Edition)" (https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/us-east-1/index.json) Model ID ref: https://platform.claude.com/docs/en/build-with-claude/claude-in-amazon-bedrock', prices=ModelPrice( input_mtok=Decimal('4'), cache_write_mtok=Decimal('5'), cache_read_mtok=Decimal('0.2'), output_mtok=Decimal('20'), cache_write_1h_mtok=Decimal('8'), ), ), ModelInfo( id='global.anthropic.claude-sonnet-4-20250514-v1:0', match=ClauseContains(contains='global.anthropic.claude-sonnet-4-20250514'), context_window=200000, prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), ), ), ModelInfo( id='global.anthropic.claude-sonnet-4-5-20250929-v1:0', match=ClauseContains(contains='global.anthropic.claude-sonnet-4-5-20250929'), context_window=200000, prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), ), ), ModelInfo( id='global.anthropic.claude-sonnet-4-6-v1:0', match=ClauseContains(contains='global.anthropic.claude-sonnet-4-6'), context_window=1000000, prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), ), ), ModelInfo( id='global.anthropic.claude-sonnet-5-v1:0', match=ClauseContains(contains='global.anthropic.claude-sonnet-5'), context_window=1000000, price_comments='Flat pricing across the full 1M context window (no tiered pricing). The $2/$10 per MTok launch rates are now permanent, with no 2026-09-01 increase. Refs: https://aws.amazon.com/bedrock/pricing/, https://platform.claude.com/docs/en/about-claude/pricing', prices=ModelPrice( input_mtok=Decimal('2'), cache_write_mtok=Decimal('2.5'), cache_read_mtok=Decimal('0.2'), output_mtok=Decimal('10'), ), ), ModelInfo( id='global.openai.gpt-5.6-luna', match=ClauseContains(contains='global.openai.gpt-5.6-luna'), name='GPT-5.6 Luna (global)', context_window=1000000, price_comments="Global cross-Region inference, priced 10% below in-Region and Geo, and identical to OpenAI's own list price. Cross-Region inference for the GPT-5.6 models launched on 2026-08-17. Cache writes (30m TTL) are billed at 1.25x the input rate. At 272K input tokens and above, input and cache are 2x and output 1.5x. Tier starts are encoded as 271999 because the pricing engines select a tier when the token count is greater than start. The 80% Luna price cut of 2026-07-30 predates global availability, so there is no earlier global rate to record. Refs: https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-56-luna.html, https://aws.amazon.com/about-aws/whats-new/2026/08/amazon-bedrock-cross-region-openai-v2/, https://aws.amazon.com/about-aws/whats-new/2026/08/gpt-sol-terra-luna-long-context-bedrock/", prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('0.2'), tiers=[Tier(start=271999, price=Decimal('0.4'))]), cache_write_mtok=TieredPrices( base=Decimal('0.25'), tiers=[Tier(start=271999, price=Decimal('0.5'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.02'), tiers=[Tier(start=271999, price=Decimal('0.04'))] ), output_mtok=TieredPrices(base=Decimal('1.2'), tiers=[Tier(start=271999, price=Decimal('1.8'))]), ), ), ModelInfo( id='global.openai.gpt-5.6-sol', match=ClauseContains(contains='global.openai.gpt-5.6-sol'), name='GPT-5.6 Sol (global)', context_window=1000000, price_comments="Global cross-Region inference, priced 10% below in-Region and Geo, and identical to OpenAI's own list price. Cross-Region inference launched 2026-08-17. Cache writes (30m TTL) are billed at 1.25x the input rate. At 272K input tokens and above, input and cache are 2x and output 1.5x. Tier starts are encoded as 271999 because the pricing engines select a tier when the token count is greater than start. AWS cut Sol input prices by 20% and output prices by 33.3% on 2026-08-21, quoting the global rates ($4 input, $20 output); the unconstrained entry keeps the four days of rates between global availability and the cut. Refs: https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-56-sol.html, https://aws.amazon.com/about-aws/whats-new/2026/08/bedrock-openai-gpt-56-sol-reduced-pricing/, https://aws.amazon.com/about-aws/whats-new/2026/08/amazon-bedrock-cross-region-openai-v2/, https://aws.amazon.com/about-aws/whats-new/2026/08/gpt-sol-terra-luna-long-context-bedrock/", prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('5'), tiers=[Tier(start=271999, price=Decimal('10'))]), cache_write_mtok=TieredPrices( base=Decimal('6.25'), tiers=[Tier(start=271999, price=Decimal('12.5'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.5'), tiers=[Tier(start=271999, price=Decimal('1'))] ), output_mtok=TieredPrices( base=Decimal('30'), tiers=[Tier(start=271999, price=Decimal('45'))] ), ) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2026, 8, 21)), prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('4'), tiers=[Tier(start=271999, price=Decimal('8'))]), cache_write_mtok=TieredPrices( base=Decimal('5'), tiers=[Tier(start=271999, price=Decimal('10'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.4'), tiers=[Tier(start=271999, price=Decimal('0.8'))] ), output_mtok=TieredPrices( base=Decimal('20'), tiers=[Tier(start=271999, price=Decimal('30'))] ), ), ), ], ), ModelInfo( id='global.openai.gpt-5.6-terra', match=ClauseContains(contains='global.openai.gpt-5.6-terra'), name='GPT-5.6 Terra (global)', context_window=1000000, price_comments="Global cross-Region inference, priced 10% below in-Region and Geo, and identical to OpenAI's own list price. Cross-Region inference for the GPT-5.6 models launched on 2026-08-17. Cache writes (30m TTL) are billed at 1.25x the input rate. At 272K input tokens and above, input and cache are 2x and output 1.5x. Tier starts are encoded as 271999 because the pricing engines select a tier when the token count is greater than start. The 20% Terra price cut of 2026-07-30 predates global availability, so there is no earlier global rate to record. Refs: https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-56-terra.html, https://aws.amazon.com/about-aws/whats-new/2026/08/amazon-bedrock-cross-region-openai-v2/, https://aws.amazon.com/about-aws/whats-new/2026/08/gpt-sol-terra-luna-long-context-bedrock/", prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('2'), tiers=[Tier(start=271999, price=Decimal('4'))]), cache_write_mtok=TieredPrices(base=Decimal('2.5'), tiers=[Tier(start=271999, price=Decimal('5'))]), cache_read_mtok=TieredPrices(base=Decimal('0.2'), tiers=[Tier(start=271999, price=Decimal('0.4'))]), output_mtok=TieredPrices(base=Decimal('12'), tiers=[Tier(start=271999, price=Decimal('18'))]), ), ), ModelInfo( id='google.gemma-3-12b-it', match=ClauseContains(contains='google.gemma-3-12b-it'), name='Gemma 3 12B IT', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.09'), output_mtok=Decimal('0.29')), ), ModelInfo( id='google.gemma-3-27b-it', match=ClauseContains(contains='google.gemma-3-27b-it'), name='Gemma 3 27B IT', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.23'), output_mtok=Decimal('0.38')), ), ModelInfo( id='google.gemma-3-4b-it', match=ClauseContains(contains='google.gemma-3-4b-it'), name='Gemma 3 4B IT', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.04'), output_mtok=Decimal('0.08')), ), ModelInfo( id='meta.llama3-1-70b-instruct-v1:0', match=ClauseContains(contains='meta.llama3-1-70b-instruct'), name='Llama 3.1 70B Instruct', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.72'), output_mtok=Decimal('0.72')), ), ModelInfo( id='meta.llama3-1-8b-instruct-v1:0', match=ClauseContains(contains='meta.llama3-1-8b-instruct'), name='Llama 3.1 8B Instruct', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.22'), output_mtok=Decimal('0.22')), ), ModelInfo( id='meta.llama3-2-11b-instruct-v1:0', match=ClauseContains(contains='meta.llama3-2-11b-instruct'), name='Llama 3.2 11B Instruct', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.16'), output_mtok=Decimal('0.16')), ), ModelInfo( id='meta.llama3-2-1b-instruct-v1:0', match=ClauseContains(contains='meta.llama3-2-1b-instruct'), name='Llama 3.2 1B Instruct', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ), ModelInfo( id='meta.llama3-2-3b-instruct-v1:0', match=ClauseContains(contains='meta.llama3-2-3b-instruct'), name='Llama 3.2 3B Instruct', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.15')), ), ModelInfo( id='meta.llama3-2-90b-instruct-v1:0', match=ClauseContains(contains='meta.llama3-2-90b-instruct'), name='Llama 3.2 90B Instruct', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.72'), output_mtok=Decimal('0.72')), ), ModelInfo( id='meta.llama3-3-70b-instruct-v1:0', match=ClauseContains(contains='meta.llama3-3-70b-instruct'), name='Llama 3.3 70B Instruct', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.72'), output_mtok=Decimal('0.72')), ), ModelInfo( id='meta.llama3-70b-instruct-v1:0', match=ClauseContains(contains='meta.llama3-70b-instruct'), name='Llama 3 70B Instruct', context_window=8192, prices=ModelPrice(input_mtok=Decimal('2.65'), output_mtok=Decimal('3.5')), ), ModelInfo( id='meta.llama3-8b-instruct-v1:0', match=ClauseContains(contains='meta.llama3-8b-instruct'), name='Llama 3 8B Instruct', context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('0.6')), ), ModelInfo( id='meta.llama4-maverick-17b-instruct-v1:0', match=ClauseContains(contains='meta.llama4-maverick-17b-instruct'), name='Llama 4 Maverick 17B Instruct', context_window=1000000, prices=ModelPrice(input_mtok=Decimal('0.24'), output_mtok=Decimal('0.97')), ), ModelInfo( id='meta.llama4-scout-17b-instruct-v1:0', match=ClauseContains(contains='meta.llama4-scout-17b-instruct'), name='Llama 4 Scout 17B Instruct', context_window=10000000, prices=ModelPrice(input_mtok=Decimal('0.17'), output_mtok=Decimal('0.66')), ), ModelInfo( id='mistral.devstral-2-123b', match=ClauseContains(contains='mistral.devstral-2-123b'), name='Devstral 2 123B', context_window=256000, prices=ModelPrice(input_mtok=Decimal('0.4'), output_mtok=Decimal('2')), ), ModelInfo( id='mistral.magistral-small-2509', match=ClauseContains(contains='mistral.magistral-small-2509'), name='Magistral Small 2509', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('1.5')), ), ModelInfo( id='mistral.ministral-3-14b-instruct', match=ClauseContains(contains='mistral.ministral-3-14b-instruct'), name='Ministral 14B 3.0', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='mistral.ministral-3-3b-instruct', match=ClauseContains(contains='mistral.ministral-3-3b-instruct'), name='Ministral 3B 3.0', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ), ModelInfo( id='mistral.ministral-3-8b-instruct', match=ClauseContains(contains='mistral.ministral-3-8b-instruct'), name='Ministral 8B 3.0', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.15')), ), ModelInfo( id='mistral.mistral-7b-instruct-v0:2', match=ClauseContains(contains='mistral.mistral-7b-instruct-v0'), name='Mistral 7B Instruct', context_window=32000, prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.2')), ), ModelInfo( id='mistral.mistral-large-2402-v1:0', match=ClauseContains(contains='mistral.mistral-large-2402'), name='Mistral Large (24.02)', context_window=32000, prices=ModelPrice(input_mtok=Decimal('4'), output_mtok=Decimal('12')), ), ModelInfo( id='mistral.mistral-large-3-675b-instruct', match=ClauseContains(contains='mistral.mistral-large-3-675b-instruct'), name='Mistral Large 3', context_window=256000, prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('1.5')), ), ModelInfo( id='mistral.mistral-small-2402-v1:0', match=ClauseContains(contains='mistral.mistral-small-2402'), name='Mistral Small (24.02)', context_window=32000, prices=ModelPrice(input_mtok=Decimal('1'), output_mtok=Decimal('3')), ), ModelInfo( id='mistral.mixtral-8x7b-instruct-v0:1', match=ClauseContains(contains='mistral.mixtral-8x7b-instruct-v0'), name='Mixtral 8x7B Instruct', context_window=32000, prices=ModelPrice(input_mtok=Decimal('0.45'), output_mtok=Decimal('0.7')), ), ModelInfo( id='mistral.pixtral-large-2502-v1:0', match=ClauseContains(contains='mistral.pixtral-large-2502'), name='Pixtral Large (25.02)', context_window=128000, prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('6')), ), ModelInfo( id='mistral.voxtral-mini-3b-2507', match=ClauseContains(contains='mistral.voxtral-mini-3b-2507'), name='Voxtral Mini 3B 2507', context_window=32000, prices=ModelPrice(input_mtok=Decimal('0.04'), output_mtok=Decimal('0.04')), ), ModelInfo( id='mistral.voxtral-small-24b-2507', match=ClauseContains(contains='mistral.voxtral-small-24b-2507'), name='Voxtral Small 24B 2507', context_window=32000, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.3')), ), ModelInfo( id='nvidia.nemotron-nano-3-30b:0', match=ClauseContains(contains='nvidia.nemotron-nano-3-30b'), name='Nemotron 3 Nano 30B', context_window=256000, prices=ModelPrice(input_mtok=Decimal('0.06'), output_mtok=Decimal('0.24')), ), ModelInfo( id='nvidia.nemotron-nano-9b-v2:0', match=ClauseContains(contains='nvidia.nemotron-nano-9b-v2'), name='Nemotron 2 Nano 9B', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.06'), output_mtok=Decimal('0.23')), ), ModelInfo( id='nvidia.nemotron-super-3-120b:0', match=ClauseContains(contains='nvidia.nemotron-super-3-120b'), name='Nemotron 3 Super 120B', context_window=256000, prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.65')), ), ModelInfo( id='openai.gpt-5.4', match=ClauseEquals(equals='openai.gpt-5.4'), name='GPT-5.4', prices=ModelPrice( input_mtok=Decimal('2.75'), cache_read_mtok=Decimal('0.275'), output_mtok=Decimal('16.5') ), ), ModelInfo( id='openai.gpt-5.5', match=ClauseEquals(equals='openai.gpt-5.5'), name='GPT-5.5', prices=ModelPrice( input_mtok=Decimal('5.5'), cache_read_mtok=Decimal('0.55'), output_mtok=Decimal('33') ), ), ModelInfo( id='openai.gpt-oss-120b-1:0', match=ClauseContains(contains='openai.gpt-oss-120b-1'), name='gpt-oss-120b', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.6')), ), ModelInfo( id='openai.gpt-oss-20b-1:0', match=ClauseContains(contains='openai.gpt-oss-20b-1'), name='gpt-oss-20b', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.07'), output_mtok=Decimal('0.3')), ), ModelInfo( id='qwen.qwen3-32b-v1:0', match=ClauseContains(contains='qwen.qwen3-32b'), name='Qwen3 32B (dense)', context_window=32000, prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.6')), ), ModelInfo( id='qwen.qwen3-coder-30b-a3b-v1:0', match=ClauseContains(contains='qwen.qwen3-coder-30b-a3b'), name='Qwen3-Coder-30B-A3B-Instruct', context_window=256000, prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.6')), ), ModelInfo( id='qwen.qwen3-coder-480b-a35b-v1:0', match=ClauseContains(contains='qwen.qwen3-coder-480b-a35b'), name='Qwen3-Coder-480B-A35B-Instruct', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.45'), output_mtok=Decimal('1.8')), ), ModelInfo( id='qwen.qwen3-vl-235b-a22b-v1:0', match=ClauseContains(contains='qwen.qwen3-vl-235b-a22b'), name='Qwen3-VL-235B-A22B-Instruct', context_window=256000, prices=ModelPrice(input_mtok=Decimal('0.53'), output_mtok=Decimal('2.66')), ), ModelInfo( id='regional.amazon.nova-2-lite-v1:0', match=ClauseOr( or_=[ ClauseContains(contains='us.amazon.nova-2-lite'), ClauseContains(contains='eu.amazon.nova-2-lite'), ClauseContains(contains='jp.amazon.nova-2-lite'), ] ), name='Nova 2 Lite', description='Amazon Nova 2 Lite is an advanced multimodal reasoning model that intelligently balances performance and efficiency by dynamically adjusting reasoning depth based on task complexity. With flexible controls for developers to adjust the reasoning process, Nova 2 Lite delivers superior results for agentic workflows across software development, consumer experiences and enterprise application.', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('0.33'), cache_read_mtok=Decimal('0.0825'), output_mtok=Decimal('2.75') ), ), ModelInfo( id='regional.anthropic.claude-3-5-haiku-20241022-v1:0', match=ClauseContains(contains='claude-3-5-haiku-20241022'), context_window=200000, prices=ModelPrice( input_mtok=Decimal('0.8'), cache_write_mtok=Decimal('1'), cache_read_mtok=Decimal('0.08'), output_mtok=Decimal('4'), ), ), ModelInfo( id='regional.anthropic.claude-3-5-sonnet-20240620-v1:0', match=ClauseContains(contains='claude-3-5-sonnet-20240620'), context_window=200000, prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), ), ), ModelInfo( id='regional.anthropic.claude-3-5-sonnet-20241022-v2:0', match=ClauseContains(contains='claude-3-5-sonnet-20241022'), context_window=200000, prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), ), ), ModelInfo( id='regional.anthropic.claude-3-7-sonnet-20250219-v1:0', match=ClauseContains(contains='claude-3-7-sonnet-20250219'), context_window=200000, prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), ), ), ModelInfo( id='regional.anthropic.claude-3-haiku-20240307-v1:0', match=ClauseContains(contains='claude-3-haiku-20240307'), context_window=200000, prices=ModelPrice(input_mtok=Decimal('0.25'), output_mtok=Decimal('1.25')), ), ModelInfo( id='regional.anthropic.claude-3-opus-20240229-v1:0', match=ClauseContains(contains='claude-3-opus-20240229'), context_window=200000, prices=ModelPrice(input_mtok=Decimal('15'), output_mtok=Decimal('75')), ), ModelInfo( id='regional.anthropic.claude-3-sonnet-20240229-v1:0', match=ClauseContains(contains='claude-3-sonnet-20240229'), context_window=200000, prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), ), ), ModelInfo( id='regional.anthropic.claude-fable-5-1-v1:0', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic.claude-fable-5-1'), ClauseEquals(equals='claude-fable-5-1'), ClauseStartsWith(starts_with='anthropic.claude-fable-5-1-v1'), ClauseStartsWith(starts_with='claude-fable-5-1-v1'), ClauseContains(contains='us.anthropic.claude-fable-5-1-v1'), ] ), context_window=1000000, price_comments='Regional endpoint: 10% premium over the global endpoint. Fable 5.1 regional is currently us-east-1 only. Cache hits are 0.025x base input (not the usual 0.1x), unique to Fable 5.1 and Mythos 5.1. Ref: https://platform.claude.com/docs/en/build-with-claude/claude-in-amazon-bedrock#regions', prices=ModelPrice( input_mtok=Decimal('11'), cache_write_mtok=Decimal('13.75'), cache_read_mtok=Decimal('0.275'), output_mtok=Decimal('55'), ), ), ModelInfo( id='regional.anthropic.claude-fable-5-v1:0', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic.claude-fable-5'), ClauseEquals(equals='claude-fable-5'), ClauseStartsWith(starts_with='anthropic.claude-fable-5-v1'), ClauseStartsWith(starts_with='claude-fable-5-v1'), ClauseContains(contains='us.anthropic.claude-fable-5-v1'), ClauseContains(contains='au.anthropic.claude-fable-5-v1'), ClauseContains(contains='eu.anthropic.claude-fable-5-v1'), ClauseContains(contains='jp.anthropic.claude-fable-5-v1'), ] ), context_window=1000000, prices=ModelPrice( input_mtok=Decimal('11'), cache_write_mtok=Decimal('13.75'), cache_read_mtok=Decimal('1.1'), output_mtok=Decimal('55'), ), ), ModelInfo( id='regional.anthropic.claude-haiku-4-5-20251001-v1:0', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='anthropic.claude-haiku-4-5-20251001'), ClauseStartsWith(starts_with='claude-haiku-4-5-20251001'), ClauseContains(contains='us.anthropic.claude-haiku-4-5-20251001'), ClauseContains(contains='au.anthropic.claude-haiku-4-5-20251001'), ClauseContains(contains='apac.anthropic.claude-haiku-4-5-20251001'), ClauseContains(contains='eu.anthropic.claude-haiku-4-5-20251001'), ClauseContains(contains='us-gov.anthropic.claude-haiku-4-5-20251001'), ClauseContains(contains='jp.anthropic.claude-haiku-4-5-20251001'), ] ), context_window=200000, prices=ModelPrice( input_mtok=Decimal('1.1'), cache_write_mtok=Decimal('1.375'), cache_read_mtok=Decimal('0.11'), output_mtok=Decimal('5.5'), ), ), ModelInfo( id='regional.anthropic.claude-opus-4-1-20250805-v1:0', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='anthropic.claude-opus-4-1-20250805'), ClauseStartsWith(starts_with='claude-opus-4-1-20250805'), ClauseContains(contains='us.anthropic.claude-opus-4-1-20250805'), ClauseContains(contains='au.anthropic.claude-opus-4-1-20250805'), ClauseContains(contains='apac.anthropic.claude-opus-4-1-20250805'), ClauseContains(contains='eu.anthropic.claude-opus-4-1-20250805'), ClauseContains(contains='us-gov.anthropic.claude-opus-4-1-20250805'), ClauseContains(contains='jp.anthropic.claude-opus-4-1-20250805'), ] ), context_window=200000, prices=ModelPrice( input_mtok=Decimal('15'), cache_write_mtok=Decimal('18.75'), cache_read_mtok=Decimal('1.5'), output_mtok=Decimal('75'), ), ), ModelInfo( id='regional.anthropic.claude-opus-4-20250514-v1:0', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='anthropic.claude-opus-4-20250514'), ClauseStartsWith(starts_with='claude-opus-4-20250514'), ClauseContains(contains='us.anthropic.claude-opus-4-20250514'), ClauseContains(contains='au.anthropic.claude-opus-4-20250514'), ClauseContains(contains='apac.anthropic.claude-opus-4-20250514'), ClauseContains(contains='eu.anthropic.claude-opus-4-20250514'), ClauseContains(contains='us-gov.anthropic.claude-opus-4-20250514'), ClauseContains(contains='jp.anthropic.claude-opus-4-20250514'), ] ), context_window=200000, prices=ModelPrice( input_mtok=Decimal('15'), cache_write_mtok=Decimal('18.75'), cache_read_mtok=Decimal('1.5'), output_mtok=Decimal('75'), ), ), ModelInfo( id='regional.anthropic.claude-opus-4-5-v1:0', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='anthropic.claude-opus-4-5'), ClauseStartsWith(starts_with='claude-opus-4-5'), ClauseContains(contains='us.anthropic.claude-opus-4-5'), ClauseContains(contains='au.anthropic.claude-opus-4-5'), ClauseContains(contains='apac.anthropic.claude-opus-4-5'), ClauseContains(contains='eu.anthropic.claude-opus-4-5'), ClauseContains(contains='us-gov.anthropic.claude-opus-4-5'), ClauseContains(contains='jp.anthropic.claude-opus-4-5'), ] ), context_window=200000, prices=ModelPrice( input_mtok=Decimal('5.5'), cache_write_mtok=Decimal('6.875'), cache_read_mtok=Decimal('0.55'), output_mtok=Decimal('27.5'), ), ), ModelInfo( id='regional.anthropic.claude-opus-4-6-v1:0', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='anthropic.claude-opus-4-6'), ClauseStartsWith(starts_with='claude-opus-4-6'), ClauseContains(contains='us.anthropic.claude-opus-4-6'), ClauseContains(contains='au.anthropic.claude-opus-4-6'), ClauseContains(contains='apac.anthropic.claude-opus-4-6'), ClauseContains(contains='eu.anthropic.claude-opus-4-6'), ClauseContains(contains='us-gov.anthropic.claude-opus-4-6'), ClauseContains(contains='jp.anthropic.claude-opus-4-6'), ] ), context_window=1000000, prices=ModelPrice( input_mtok=Decimal('5.5'), cache_write_mtok=Decimal('6.875'), cache_read_mtok=Decimal('0.55'), output_mtok=Decimal('27.5'), ), ), ModelInfo( id='regional.anthropic.claude-opus-4-7-v1:0', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='anthropic.claude-opus-4-7'), ClauseStartsWith(starts_with='claude-opus-4-7'), ClauseContains(contains='us.anthropic.claude-opus-4-7'), ClauseContains(contains='au.anthropic.claude-opus-4-7'), ClauseContains(contains='apac.anthropic.claude-opus-4-7'), ClauseContains(contains='eu.anthropic.claude-opus-4-7'), ClauseContains(contains='us-gov.anthropic.claude-opus-4-7'), ClauseContains(contains='jp.anthropic.claude-opus-4-7'), ] ), context_window=1000000, prices=ModelPrice( input_mtok=Decimal('5.5'), cache_write_mtok=Decimal('6.875'), cache_read_mtok=Decimal('0.55'), output_mtok=Decimal('27.5'), ), ), ModelInfo( id='regional.anthropic.claude-opus-4-8-v1:0', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='anthropic.claude-opus-4-8'), ClauseStartsWith(starts_with='claude-opus-4-8'), ClauseContains(contains='us.anthropic.claude-opus-4-8'), ClauseContains(contains='au.anthropic.claude-opus-4-8'), ClauseContains(contains='eu.anthropic.claude-opus-4-8'), ClauseContains(contains='jp.anthropic.claude-opus-4-8'), ] ), context_window=1000000, prices=ModelPrice( input_mtok=Decimal('5.5'), cache_write_mtok=Decimal('6.875'), cache_read_mtok=Decimal('0.55'), output_mtok=Decimal('27.5'), ), ), ModelInfo( id='regional.anthropic.claude-opus-5', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic.claude-opus-5'), ClauseEquals(equals='claude-opus-5'), ClauseStartsWith(starts_with='anthropic.claude-opus-5-v1'), ClauseStartsWith(starts_with='claude-opus-5-v1'), ClauseEquals(equals='us.anthropic.claude-opus-5'), ClauseEquals(equals='au.anthropic.claude-opus-5'), ClauseEquals(equals='eu.anthropic.claude-opus-5'), ClauseEquals(equals='jp.anthropic.claude-opus-5'), ClauseContains(contains='us.anthropic.claude-opus-5-v1'), ClauseContains(contains='au.anthropic.claude-opus-5-v1'), ClauseContains(contains='eu.anthropic.claude-opus-5-v1'), ClauseContains(contains='jp.anthropic.claude-opus-5-v1'), ] ), context_window=1000000, price_comments='Regional endpoints and US/EU/JP/AU inference profiles carry a 10% premium over the global endpoint. Ref: https://platform.claude.com/docs/en/build-with-claude/claude-in-amazon-bedrock#regions', prices=ModelPrice( input_mtok=Decimal('5.5'), cache_write_mtok=Decimal('6.875'), cache_read_mtok=Decimal('0.55'), output_mtok=Decimal('27.5'), ), ), ModelInfo( id='regional.anthropic.claude-opus-5-5', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic.claude-opus-5-5'), ClauseEquals(equals='claude-opus-5-5'), ClauseStartsWith(starts_with='anthropic.claude-opus-5-5-v1'), ClauseStartsWith(starts_with='claude-opus-5-5-v1'), ClauseEquals(equals='us.anthropic.claude-opus-5-5'), ClauseEquals(equals='au.anthropic.claude-opus-5-5'), ClauseEquals(equals='eu.anthropic.claude-opus-5-5'), ClauseEquals(equals='jp.anthropic.claude-opus-5-5'), ClauseContains(contains='us.anthropic.claude-opus-5-5-v1'), ClauseContains(contains='au.anthropic.claude-opus-5-5-v1'), ClauseContains(contains='eu.anthropic.claude-opus-5-5-v1'), ClauseContains(contains='jp.anthropic.claude-opus-5-5-v1'), ] ), context_window=1000000, price_comments='Regional endpoints and US/EU/JP/AU inference profiles carry a 10% premium over the global endpoint. Cache hits are 0.05x base input (not the usual 0.1x), unique to Opus 5.5. Ref: AWS price list API, AmazonBedrockFoundationModels "Claude Opus 5.5 (Amazon Bedrock Edition)" (https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/us-east-1/index.json)', prices=ModelPrice( input_mtok=Decimal('4.4'), cache_write_mtok=Decimal('5.5'), cache_read_mtok=Decimal('0.22'), output_mtok=Decimal('22'), cache_write_1h_mtok=Decimal('8.8'), ), ), ModelInfo( id='regional.anthropic.claude-sonnet-4-20250514-v1:0', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='anthropic.claude-sonnet-4-20250514'), ClauseStartsWith(starts_with='claude-sonnet-4-20250514'), ClauseContains(contains='us.anthropic.claude-sonnet-4-20250514'), ClauseContains(contains='au.anthropic.claude-sonnet-4-20250514'), ClauseContains(contains='apac.anthropic.claude-sonnet-4-20250514'), ClauseContains(contains='eu.anthropic.claude-sonnet-4-20250514'), ClauseContains(contains='us-gov.anthropic.claude-sonnet-4-20250514'), ClauseContains(contains='jp.anthropic.claude-sonnet-4-20250514'), ] ), context_window=200000, prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), ), ), ModelInfo( id='regional.anthropic.claude-sonnet-4-5-20250929-v1:0', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='anthropic.claude-sonnet-4-5-20250929'), ClauseStartsWith(starts_with='claude-sonnet-4-5-20250929'), ClauseContains(contains='us.anthropic.claude-sonnet-4-5-20250929'), ClauseContains(contains='au.anthropic.claude-sonnet-4-5-20250929'), ClauseContains(contains='apac.anthropic.claude-sonnet-4-5-20250929'), ClauseContains(contains='eu.anthropic.claude-sonnet-4-5-20250929'), ClauseContains(contains='us-gov.anthropic.claude-sonnet-4-5-20250929'), ClauseContains(contains='jp.anthropic.claude-sonnet-4-5-20250929'), ] ), context_window=200000, prices=ModelPrice( input_mtok=Decimal('3.3'), cache_write_mtok=Decimal('4.125'), cache_read_mtok=Decimal('0.33'), output_mtok=Decimal('16.5'), ), ), ModelInfo( id='regional.anthropic.claude-sonnet-4-6-v1:0', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='anthropic.claude-sonnet-4-6'), ClauseStartsWith(starts_with='claude-sonnet-4-6'), ClauseContains(contains='us.anthropic.claude-sonnet-4-6'), ClauseContains(contains='au.anthropic.claude-sonnet-4-6'), ClauseContains(contains='apac.anthropic.claude-sonnet-4-6'), ClauseContains(contains='eu.anthropic.claude-sonnet-4-6'), ClauseContains(contains='us-gov.anthropic.claude-sonnet-4-6'), ClauseContains(contains='jp.anthropic.claude-sonnet-4-6'), ] ), context_window=1000000, prices=ModelPrice( input_mtok=Decimal('3.3'), cache_write_mtok=Decimal('4.125'), cache_read_mtok=Decimal('0.33'), output_mtok=Decimal('16.5'), ), ), ModelInfo( id='regional.anthropic.claude-sonnet-5-v1:0', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='anthropic.claude-sonnet-5'), ClauseStartsWith(starts_with='claude-sonnet-5'), ClauseContains(contains='us.anthropic.claude-sonnet-5'), ClauseContains(contains='au.anthropic.claude-sonnet-5'), ClauseContains(contains='apac.anthropic.claude-sonnet-5'), ClauseContains(contains='eu.anthropic.claude-sonnet-5'), ClauseContains(contains='us-gov.anthropic.claude-sonnet-5'), ClauseContains(contains='jp.anthropic.claude-sonnet-5'), ] ), context_window=1000000, price_comments='Regional/cross-region endpoints carry a 10% premium over global. The launch rates are now permanent, with no 2026-09-01 increase. Refs: https://aws.amazon.com/bedrock/pricing/, https://platform.claude.com/docs/en/about-claude/pricing', prices=ModelPrice( input_mtok=Decimal('2.2'), cache_write_mtok=Decimal('2.75'), cache_read_mtok=Decimal('0.22'), output_mtok=Decimal('11'), ), ), ModelInfo( id='regional.openai.gpt-5.6-luna', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='openai.gpt-5.6-luna'), ClauseStartsWith(starts_with='gpt-5.6-luna'), ClauseContains(contains='us.openai.gpt-5.6-luna'), ClauseContains(contains='in.openai.gpt-5.6-luna'), ] ), name='GPT-5.6 Luna (regional)', context_window=1000000, price_comments='In-Region and Geo (`us.`/`in.`) inference, 10% above global. Cache writes (30m TTL) are billed at 1.25x input; at 272K input tokens and above, input and cache are 2x and output 1.5x. Tier starts are encoded as 271999 because the pricing engines select a tier when the token count is greater than start. AWS cut Luna prices by 80% on 2026-07-30; the unconstrained entry preserves the launch (2026-07-13) rates. The long-context tier arrived with the 1M context window on 2026-08-03, so the launch entry stays flat. Refs: https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-56-luna.html, https://aws.amazon.com/about-aws/whats-new/2026/07/openai-gpt-terra-luna-pricing-bedrock/, https://aws.amazon.com/about-aws/whats-new/2026/08/gpt-sol-terra-luna-long-context-bedrock/', prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=Decimal('1.1'), cache_write_mtok=Decimal('1.375'), cache_read_mtok=Decimal('0.11'), output_mtok=Decimal('6.6'), ) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2026, 7, 30)), prices=ModelPrice( input_mtok=TieredPrices( base=Decimal('0.22'), tiers=[Tier(start=271999, price=Decimal('0.44'))] ), cache_write_mtok=TieredPrices( base=Decimal('0.275'), tiers=[Tier(start=271999, price=Decimal('0.55'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.022'), tiers=[Tier(start=271999, price=Decimal('0.044'))] ), output_mtok=TieredPrices( base=Decimal('1.32'), tiers=[Tier(start=271999, price=Decimal('1.98'))] ), ), ), ], ), ModelInfo( id='regional.openai.gpt-5.6-sol', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='openai.gpt-5.6-sol'), ClauseStartsWith(starts_with='gpt-5.6-sol'), ClauseContains(contains='us.openai.gpt-5.6-sol'), ] ), name='GPT-5.6 Sol (regional)', context_window=1000000, price_comments='In-Region and Geo (`us.`) inference, 10% above global. Cache writes (30m TTL) are billed at 1.25x input; at 272K input tokens and above, input and cache are 2x and output 1.5x. Tier starts are encoded as 271999 because the pricing engines select a tier when the token count is greater than start. Sol input prices fell 20% and output prices 33.3% on 2026-08-21; the announcement quotes the global rates, and the in-Region and Geo rates moved with them. Refs: https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-56-sol.html, https://aws.amazon.com/about-aws/whats-new/2026/08/bedrock-openai-gpt-56-sol-reduced-pricing/, https://aws.amazon.com/about-aws/whats-new/2026/08/gpt-sol-terra-luna-long-context-bedrock/', prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=TieredPrices( base=Decimal('5.5'), tiers=[Tier(start=271999, price=Decimal('11'))] ), cache_write_mtok=TieredPrices( base=Decimal('6.875'), tiers=[Tier(start=271999, price=Decimal('13.75'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.55'), tiers=[Tier(start=271999, price=Decimal('1.1'))] ), output_mtok=TieredPrices( base=Decimal('33'), tiers=[Tier(start=271999, price=Decimal('49.5'))] ), ) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2026, 8, 21)), prices=ModelPrice( input_mtok=TieredPrices( base=Decimal('4.4'), tiers=[Tier(start=271999, price=Decimal('8.8'))] ), cache_write_mtok=TieredPrices( base=Decimal('5.5'), tiers=[Tier(start=271999, price=Decimal('11'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.44'), tiers=[Tier(start=271999, price=Decimal('0.88'))] ), output_mtok=TieredPrices( base=Decimal('22'), tiers=[Tier(start=271999, price=Decimal('33'))] ), ), ), ], ), ModelInfo( id='regional.openai.gpt-5.6-terra', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='openai.gpt-5.6-terra'), ClauseStartsWith(starts_with='gpt-5.6-terra'), ClauseContains(contains='us.openai.gpt-5.6-terra'), ClauseContains(contains='in.openai.gpt-5.6-terra'), ] ), name='GPT-5.6 Terra (regional)', context_window=1000000, price_comments='In-Region and Geo (`us.`/`in.`) inference, 10% above global. Cache writes (30m TTL) are billed at 1.25x input; at 272K input tokens and above, input and cache are 2x and output 1.5x. Tier starts are encoded as 271999 because the pricing engines select a tier when the token count is greater than start. AWS cut Terra prices by 20% on 2026-07-30; the unconstrained entry preserves the launch (2026-07-13) rates. The long-context tier arrived with the 1M context window on 2026-08-03, so the launch entry stays flat. Refs: https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-56-terra.html, https://aws.amazon.com/about-aws/whats-new/2026/07/openai-gpt-terra-luna-pricing-bedrock/, https://aws.amazon.com/about-aws/whats-new/2026/08/gpt-sol-terra-luna-long-context-bedrock/', prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=Decimal('2.75'), cache_write_mtok=Decimal('3.4375'), cache_read_mtok=Decimal('0.275'), output_mtok=Decimal('16.5'), ) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2026, 7, 30)), prices=ModelPrice( input_mtok=TieredPrices( base=Decimal('2.2'), tiers=[Tier(start=271999, price=Decimal('4.4'))] ), cache_write_mtok=TieredPrices( base=Decimal('2.75'), tiers=[Tier(start=271999, price=Decimal('5.5'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.22'), tiers=[Tier(start=271999, price=Decimal('0.44'))] ), output_mtok=TieredPrices( base=Decimal('13.2'), tiers=[Tier(start=271999, price=Decimal('19.8'))] ), ), ), ], ), ModelInfo( id='writer.palmyra-x4-v1:0', match=ClauseContains(contains='writer.palmyra-x4'), name='Palmyra X4', context_window=128000, price_comments="Bedrock serves Palmyra X4 through cross-region inference profiles, so the model reference arrives prefixed, e.g. 'us.writer.palmyra-x4-v1:0'. Pricing is flat across regions. Ref: https://aws.amazon.com/bedrock/pricing/", prices=ModelPrice(input_mtok=Decimal('2.5'), output_mtok=Decimal('10')), ), ModelInfo( id='writer.palmyra-x5-v1:0', match=ClauseContains(contains='writer.palmyra-x5'), name='Palmyra X5', context_window=128000, price_comments="Bedrock serves Palmyra X5 through cross-region inference profiles, so the model reference arrives prefixed, e.g. 'us.writer.palmyra-x5-v1:0'. Pricing is flat across regions. Ref: https://aws.amazon.com/bedrock/pricing/", prices=ModelPrice(input_mtok=Decimal('0.6'), output_mtok=Decimal('6')), ), ], ), Provider( id='azure', name='Microsoft Azure', api_pattern='(https?://)?([^.]*\\.)?(?:openai\\.azure\\.com|azure-api\\.net|cognitiveservices\\.azure\\.com|services\\.ai\\.azure\\.com)', pricing_urls=['https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/#pricing'], price_comments='These are prices for "*-Global" models, prices for "Regional" models are often slightly higher. Retired models are listed at https://learn.microsoft.com/th-th/azure/ai-foundry/openai/concepts/legacy-models', extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['input_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['input_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['output_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=True), ], api_flavor='responses', model_path='model', ), UsageExtractor( root=['response', 'usage'], mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=False), UsageExtractorMapping( path=['input_token_details', 'text_tokens'], dest='input_text_tokens', required=False ), UsageExtractorMapping( path=['input_token_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['input_token_details', 'image_tokens'], dest='input_image_tokens', required=False ), UsageExtractorMapping( path=['input_token_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['input_token_details', 'cached_tokens_details', 'text_tokens'], dest='cache_text_read_tokens', required=False, ), UsageExtractorMapping( path=['input_token_details', 'cached_tokens_details', 'audio_tokens'], dest='cache_audio_read_tokens', required=False, ), UsageExtractorMapping( path=['input_token_details', 'cached_tokens_details', 'image_tokens'], dest='cache_image_read_tokens', required=False, ), UsageExtractorMapping( path=['output_token_details', 'text_tokens'], dest='output_text_tokens', required=False ), UsageExtractorMapping( path=['output_token_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=False), ], api_flavor='realtime', model_path='model', ), UsageExtractor( root='usage', mappings=[UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True)], api_flavor='embeddings', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=True), UsageExtractorMapping(path='cache_creation_input_tokens', dest='input_tokens', required=False), UsageExtractorMapping(path='cache_read_input_tokens', dest='input_tokens', required=False), UsageExtractorMapping( path='cache_creation_input_tokens', dest='cache_write_tokens', required=False ), UsageExtractorMapping(path='cache_read_input_tokens', dest='cache_read_tokens', required=False), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=True), ], api_flavor='anthropic', model_path='model', ), ], fallback_model_providers=['openai', 'anthropic', 'deepseek', 'x-ai', 'moonshotai'], models=[ ModelInfo( id='ada', match=ClauseOr( or_=[ ClauseEquals(equals='ada'), ClauseEquals(equals='text-embedding-ada'), ClauseEquals(equals='text-embedding-ada-002'), ClauseEquals(equals='text-embedding-ada-002-v2'), ] ), prices=ModelPrice(input_mtok=Decimal('0.1')), ), ModelInfo( id='babbage', match=ClauseOr(or_=[ClauseEquals(equals='babbage'), ClauseEquals(equals='babbage-002')]), prices=ModelPrice(input_mtok=Decimal('0.4')), ), ModelInfo( id='curie', match=ClauseOr( or_=[ ClauseEquals(equals='curie'), ClauseEquals(equals='text-curie'), ClauseEquals(equals='text-curie-001'), ] ), prices=ModelPrice(input_mtok=Decimal('2')), ), ModelInfo( id='davinci', match=ClauseOr( or_=[ ClauseEquals(equals='davinci'), ClauseEquals(equals='davinci-002'), ClauseEquals(equals='text-davinci'), ClauseEquals(equals='text-davinci-002'), ] ), prices=ModelPrice(input_mtok=Decimal('2')), ), ModelInfo( id='mai-ds-r1:free', match=ClauseEquals(equals='mai-ds-r1:free'), name='MAI DS R1 (free)', description="MAI-DS-R1 is a post-trained variant of DeepSeek-R1 developed by the Microsoft AI team to improve the model's responsiveness on previously blocked topics while enhancing its safety profile. Built on top of DeepSeek-R1's reasoning foundation, it integrates 110k examples from the Tulu-3 SFT dataset and 350k internally curated multilingual safety-alignment samples. The model retains strong reasoning, coding, and problem-solving capabilities, while unblocking a wide range of prompts previously restricted in R1.", context_window=163840, prices=ModelPrice(), ), ModelInfo( id='o1', match=ClauseOr(or_=[ClauseEquals(equals='o1'), ClauseEquals(equals='o1-2024-12-17')]), context_window=200000, prices=ModelPrice(input_mtok=Decimal('15'), cache_read_mtok=Decimal('7.5'), output_mtok=Decimal('60')), ), ModelInfo( id='o1-mini', match=ClauseOr(or_=[ClauseEquals(equals='o1-mini'), ClauseEquals(equals='o1-mini-2024-09-12')]), context_window=128000, prices=ModelPrice( input_mtok=Decimal('1.1'), cache_read_mtok=Decimal('0.55'), output_mtok=Decimal('4.4') ), ), ModelInfo( id='o1-preview', match=ClauseOr(or_=[ClauseEquals(equals='o1-preview'), ClauseEquals(equals='o1-preview-2024-09-12')]), context_window=128000, prices=ModelPrice(input_mtok=Decimal('15'), cache_read_mtok=Decimal('7.5'), output_mtok=Decimal('60')), ), ModelInfo( id='o3-2025-04-16', match=ClauseOr(or_=[ClauseEquals(equals='o3'), ClauseEquals(equals='o3-2025-04-16')]), context_window=200000, prices=ModelPrice(input_mtok=Decimal('2'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('8')), ), ModelInfo( id='o3-mini', match=ClauseOr(or_=[ClauseEquals(equals='o3-mini'), ClauseEquals(equals='o3-mini-2025-01-31')]), context_window=200000, prices=ModelPrice( input_mtok=Decimal('1.1'), cache_read_mtok=Decimal('0.55'), output_mtok=Decimal('4.4') ), ), ModelInfo( id='o4-mini', match=ClauseOr(or_=[ClauseContains(contains='o4-mini'), ClauseContains(contains='o4-mini-2025-04-16')]), context_window=200000, prices=ModelPrice( input_mtok=Decimal('1.1'), cache_read_mtok=Decimal('0.28'), output_mtok=Decimal('4.4') ), ), ModelInfo( id='phi-3-medium-128k-instruct', match=ClauseEquals(equals='phi-3-medium-128k-instruct'), name='Phi-3 Medium 128K Instruct', description='Phi-3 128K Medium is a powerful 14-billion parameter model designed for advanced language understanding, reasoning, and instruction following. Optimized through supervised fine-tuning and preference adjustments, it excels in tasks involving common sense, mathematics, logical reasoning, and code processing.', context_window=131072, prices=ModelPrice(input_mtok=Decimal('1'), output_mtok=Decimal('1')), ), ModelInfo( id='phi-3-mini-128k-instruct', match=ClauseEquals(equals='phi-3-mini-128k-instruct'), name='Phi-3 Mini 128K Instruct', description='Phi-3 Mini is a powerful 3.8B parameter model designed for advanced language understanding, reasoning, and instruction following. Optimized through supervised fine-tuning and preference adjustments, it excels in tasks involving common sense, mathematics, logical reasoning, and code processing.', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ), ModelInfo( id='phi-3.5-mini-128k-instruct', match=ClauseEquals(equals='phi-3.5-mini-128k-instruct'), name='Phi-3.5 Mini 128K Instruct', description='Phi-3.5 models are lightweight, state-of-the-art open models. These models were trained with Phi-3 datasets that include both synthetic data and the filtered, publicly available websites data, with a focus on high quality and reasoning-dense properties. Phi-3.5 Mini uses 3.8B parameters, and is a dense decoder-only transformer model using the same tokenizer as Phi-3 Mini.', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ), ModelInfo( id='phi-4', match=ClauseEquals(equals='phi-4'), name='Phi 4', description='Microsoft Research Phi-4 is designed to perform well in complex reasoning tasks and can operate efficiently in situations with limited memory or where quick responses are needed.', context_window=16384, prices=ModelPrice(input_mtok=Decimal('0.07'), output_mtok=Decimal('0.14')), ), ModelInfo( id='phi-4-mini-instruct', match=ClauseEquals(equals='phi-4-mini-instruct'), name='Phi 4 Mini Instruct', description='Phi-4-mini-instruct is a lightweight open model built upon synthetic data and filtered publicly available websites, with a focus on high-quality, reasoning-dense data.', context_window=131072, price_comments='Imported from OpenRouter pricing; verify against Azure AI Foundry when native pricing is published.', prices=ModelPrice(input_mtok=Decimal('0.08'), output_mtok=Decimal('0.35')), ), ModelInfo( id='phi-4-multimodal-instruct', match=ClauseEquals(equals='phi-4-multimodal-instruct'), name='Phi 4 Multimodal Instruct', description='Phi-4 Multimodal Instruct is a versatile 5.6B parameter foundation model that combines advanced reasoning and instruction-following capabilities across both text and visual inputs, providing accurate text outputs. The unified architecture enables efficient, low-latency inference, suitable for edge and mobile deployments. Phi-4 Multimodal Instruct supports text inputs in multiple languages including Arabic, Chinese, English, French, German, Japanese, Spanish, and more, with visual input optimized primarily for English. It delivers impressive performance on multimodal tasks involving mathematical, scientific, and document reasoning, providing developers and enterprises a powerful yet compact model for sophisticated interactive applications. For more information, see the Phi-4 Multimodal blog post.', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.05'), output_mtok=Decimal('0.1')), ), ModelInfo( id='phi-4-reasoning-plus', match=ClauseEquals(equals='phi-4-reasoning-plus'), name='Phi 4 Reasoning Plus', description='Phi-4-reasoning-plus is an enhanced 14B parameter model from Microsoft, fine-tuned from Phi-4 with additional reinforcement learning to boost accuracy on math, science, and code reasoning tasks. It uses the same dense decoder-only transformer architecture as Phi-4, but generates longer, more comprehensive outputs structured into a step-by-step reasoning trace and final answer.', context_window=32768, prices=ModelPrice(input_mtok=Decimal('0.07'), output_mtok=Decimal('0.35')), ), ModelInfo( id='phi-4-reasoning-plus:free', match=ClauseEquals(equals='phi-4-reasoning-plus:free'), name='Phi 4 Reasoning Plus (free)', description='Phi-4-reasoning-plus is an enhanced 14B parameter model from Microsoft, fine-tuned from Phi-4 with additional reinforcement learning to boost accuracy on math, science, and code reasoning tasks. It uses the same dense decoder-only transformer architecture as Phi-4, but generates longer, more comprehensive outputs structured into a step-by-step reasoning trace and final answer.', context_window=32768, prices=ModelPrice(), ), ModelInfo( id='phi-4-reasoning:free', match=ClauseEquals(equals='phi-4-reasoning:free'), name='Phi 4 Reasoning (free)', description='Phi-4-reasoning is a 14B parameter dense decoder-only transformer developed by Microsoft, fine-tuned from Phi-4 to enhance complex reasoning capabilities. It uses a combination of supervised fine-tuning on chain-of-thought traces and reinforcement learning, targeting math, science, and code reasoning tasks. With a 32k context window and high inference efficiency, it is optimized for structured responses in a two-part format: reasoning trace followed by a final solution.', context_window=32768, prices=ModelPrice(), ), ModelInfo( id='text-embedding-3-large', match=ClauseEquals(equals='text-embedding-3-large'), context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.13')), ), ModelInfo( id='text-embedding-3-small', match=ClauseEquals(equals='text-embedding-3-small'), context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.02')), ), ModelInfo( id='wizardlm-2-8x22b', match=ClauseEquals(equals='wizardlm-2-8x22b'), name='WizardLM-2 8x22B', description="WizardLM-2 8x22B is Microsoft AI's most advanced Wizard model. It demonstrates highly competitive performance compared to leading proprietary models, and it consistently outperforms all existing state-of-the-art opensource models.", prices=ModelPrice(input_mtok=Decimal('0.48'), output_mtok=Decimal('0.48')), ), ], ), Provider( id='baseten', name='Baseten', api_pattern='https://inference\\.baseten\\.co(?:/|$)', pricing_urls=['https://www.baseten.co/pricing/', 'https://docs.baseten.co/inference/model-apis/overview'], description='OpenAI- and Anthropic-compatible hosted inference through Baseten Model APIs.', price_comments="Covers the complete public Model APIs pricing table. Baseten automatically caches prompt prefixes and bills cached input at the published cache-input rate. Context windows use the catalog's displayed values in thousands of tokens. Model IDs use third-party namespaces, so they do not infer the Baseten provider without a Baseten provider ID or API URL.", provider_match=ClauseContains(contains='baseten'), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=True), UsageExtractorMapping(path='cache_read_input_tokens', dest='input_tokens', required=False), UsageExtractorMapping(path='cache_read_input_tokens', dest='cache_read_tokens', required=False), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=True), ], api_flavor='anthropic', model_path='model', ), ], models=[ ModelInfo( id='deepseek-ai/DeepSeek-V4-Flash-0731', match=ClauseEquals(equals='deepseek-ai/DeepSeek-V4-Flash-0731'), name='DeepSeek V4 Flash 0731', context_window=1048000, prices=ModelPrice( input_mtok=Decimal('0.13'), cache_read_mtok=Decimal('0.028'), output_mtok=Decimal('0.26') ), ), ModelInfo( id='deepseek-ai/DeepSeek-V4-Pro', match=ClauseEquals(equals='deepseek-ai/DeepSeek-V4-Pro'), name='DeepSeek V4 Pro', context_window=1048000, prices=ModelPrice( input_mtok=Decimal('1.74'), cache_read_mtok=Decimal('0.145'), output_mtok=Decimal('3.48') ), ), ModelInfo( id='deepseek-ai/DeepSeek-V4-Pro-0813', match=ClauseEquals(equals='deepseek-ai/DeepSeek-V4-Pro-0813'), name='DeepSeek V4 Pro 0813', context_window=1048000, prices=ModelPrice( input_mtok=Decimal('1.32'), cache_read_mtok=Decimal('0.132'), output_mtok=Decimal('3.96') ), ), ModelInfo( id='moonshotai/Kimi-K2.6', match=ClauseEquals(equals='moonshotai/Kimi-K2.6'), name='Kimi K2.6', context_window=262000, prices=ModelPrice( input_mtok=Decimal('0.95'), cache_read_mtok=Decimal('0.16'), output_mtok=Decimal('4') ), ), ModelInfo( id='moonshotai/Kimi-K2.7-Code', match=ClauseEquals(equals='moonshotai/Kimi-K2.7-Code'), name='Kimi K2.7 Code', context_window=262000, prices=ModelPrice( input_mtok=Decimal('0.95'), cache_read_mtok=Decimal('0.16'), output_mtok=Decimal('4') ), ), ModelInfo( id='moonshotai/Kimi-K3', match=ClauseEquals(equals='moonshotai/Kimi-K3'), name='Kimi K3', context_window=1048000, prices=ModelPrice(input_mtok=Decimal('3'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15')), ), ModelInfo( id='nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B', match=ClauseEquals(equals='nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B'), name='NVIDIA Nemotron 3 Ultra', context_window=202000, prices=ModelPrice( input_mtok=Decimal('0.6'), cache_read_mtok=Decimal('0.12'), output_mtok=Decimal('2.4') ), ), ModelInfo( id='openai/gpt-oss-120b', match=ClauseEquals(equals='openai/gpt-oss-120b'), name='GPT OSS 120B', context_window=128000, price_comments='The pricing table does not publish a separate cached-input rate for this model.', prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.5')), ), ModelInfo( id='thinkingmachines/inkling', match=ClauseEquals(equals='thinkingmachines/inkling'), name='Inkling', context_window=1048000, prices=ModelPrice( input_mtok=Decimal('1'), cache_read_mtok=Decimal('0.17'), output_mtok=Decimal('4.05') ), ), ModelInfo( id='thinkingmachines/inkling-small', match=ClauseEquals(equals='thinkingmachines/inkling-small'), name='Inkling Small', context_window=1048000, prices=ModelPrice( input_mtok=Decimal('0.5'), cache_read_mtok=Decimal('0.1'), output_mtok=Decimal('1.2') ), ), ModelInfo( id='zai-org/GLM-4.7', match=ClauseEquals(equals='zai-org/GLM-4.7'), name='GLM 4.7', context_window=200000, prices=ModelPrice( input_mtok=Decimal('0.6'), cache_read_mtok=Decimal('0.12'), output_mtok=Decimal('2.2') ), ), ModelInfo( id='zai-org/GLM-5.2', match=ClauseEquals(equals='zai-org/GLM-5.2'), name='GLM 5.2', context_window=1048000, prices=ModelPrice( input_mtok=Decimal('1.4'), cache_read_mtok=Decimal('0.14'), output_mtok=Decimal('4.4') ), ), ModelInfo( id='zai-org/GLM-5.2-Fast', match=ClauseEquals(equals='zai-org/GLM-5.2-Fast'), name='GLM 5.2 Fast', context_window=1048000, prices=ModelPrice( input_mtok=Decimal('2.1'), cache_read_mtok=Decimal('0.21'), output_mtok=Decimal('6.6') ), ), ModelInfo( id='zai-org/GLM-5.3', match=ClauseEquals(equals='zai-org/GLM-5.3'), name='GLM 5.3', context_window=1048000, prices=ModelPrice( input_mtok=Decimal('1.4'), cache_read_mtok=Decimal('0.14'), output_mtok=Decimal('4.4') ), ), ModelInfo( id='zai-org/GLM-5.3-Flash', match=ClauseEquals(equals='zai-org/GLM-5.3-Flash'), name='GLM 5.3 Flash', context_window=1048000, prices=ModelPrice( input_mtok=Decimal('0.15'), cache_read_mtok=Decimal('0.03'), output_mtok=Decimal('0.5') ), ), ], ), Provider( id='cerebras', name='Cerebras', api_pattern='https://api\\.cerebras\\.ai', pricing_urls=[ 'https://www.cerebras.ai/pricing#pricing', 'https://inference-docs.cerebras.ai/models/openai-oss', 'https://inference-docs.cerebras.ai/models/gemma-4-31b', 'https://inference-docs.cerebras.ai/support/change-log', ], model_match=ClauseContains(contains='cerebras'), provider_match=ClauseContains(contains='cerebras'), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ) ], models=[ ModelInfo( id='gemma-4-31b', match=ClauseOr( or_=[ ClauseEquals(equals='gemma-4-31b'), ClauseStartsWith(starts_with='cerebras/gemma-4-31b'), ClauseStartsWith(starts_with='cerebras:gemma-4-31b'), ] ), name='Gemma 4 31B', description="Google's Gemma 4 31B open model for chat, instruction following, and multilingual tasks, served with fast inference on Cerebras hardware (~1,850 tokens/second).", context_window=131072, price_comments='Developer tier pricing. Free tier: 65k context, Paid tier: 131k context.', prices=ModelPrice(input_mtok=Decimal('0.99'), output_mtok=Decimal('1.49')), ), ModelInfo( id='gpt-oss-120b', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-oss-120b'), ClauseStartsWith(starts_with='cerebras/gpt-oss-120b'), ClauseStartsWith(starts_with='cerebras:gpt-oss-120b'), ] ), name='GPT-OSS 120B', description="OpenAI's flagship open source model, built on a Mixture-of-Experts (MoE) architecture with 120 billion parameters and 128 experts. Delivers frontier reasoning capabilities with record-breaking inference speeds on Cerebras hardware (~3,000 tokens/second).", context_window=131072, price_comments='Developer tier pricing. Free tier: 65k context, Paid tier: 131k context.', prices=ModelPrice(input_mtok=Decimal('0.35'), output_mtok=Decimal('0.75')), ), ModelInfo( id='llama-3.3-70b', match=ClauseOr( or_=[ ClauseEquals(equals='llama-3.3-70b'), ClauseStartsWith(starts_with='cerebras/llama-3.3-70b'), ClauseStartsWith(starts_with='cerebras:llama-3.3-70b'), ] ), name='Llama 3.3 70B', description="Meta's enhanced 70B model delivering 405B-level accuracy. Optimized for chat, coding, instruction following, mathematics, and reasoning with high-speed inference on Cerebras hardware (~2,100 tokens/second).", context_window=128000, price_comments='No longer listed in the Cerebras Inference docs (checked 2026-07-17 and 2026-08-27); last known rates kept for historical usage.', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.85'), output_mtok=Decimal('1.2')), ), ModelInfo( id='llama3.1-8b', match=ClauseOr( or_=[ ClauseEquals(equals='llama3.1-8b'), ClauseStartsWith(starts_with='cerebras/llama3.1-8b'), ClauseStartsWith(starts_with='cerebras:llama3.1-8b'), ] ), name='Llama 3.1 8B', description="Meta's Llama 3.1 8B model for general-purpose tasks including chat, coding, and instruction following. Optimized for fast inference on Cerebras hardware (~2,200 tokens/second).", context_window=32768, price_comments='No longer listed in the Cerebras Inference docs (checked 2026-07-17 and 2026-08-27); last known rates kept for historical usage.', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ), ModelInfo( id='qwen-3-32b', match=ClauseOr( or_=[ ClauseEquals(equals='qwen-3-32b'), ClauseStartsWith(starts_with='cerebras/qwen-3-32b'), ClauseStartsWith(starts_with='cerebras:qwen-3-32b'), ] ), name='Qwen 3 32B', description="Qwen's 32B parameter model with enhanced reasoning and coding capabilities. Supports both standard and reasoning modes for complex tasks, with fast inference speeds on Cerebras hardware (~2,600 tokens/second).", context_window=131072, price_comments='No longer listed in the Cerebras Inference docs (checked 2026-07-17 and 2026-08-27); last known rates kept for historical usage.', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.4'), output_mtok=Decimal('0.8')), ), ModelInfo( id='qwen-3-coder-480b', match=ClauseEquals(equals='qwen-3-coder-480b'), name='qwen-3-coder-480b', price_comments='Seems to be no longer available on cerebras, here to help with tests', prices=ModelPrice(), ), ModelInfo( id='zai-glm-4.7', match=ClauseOr( or_=[ ClauseEquals(equals='zai-glm-4.7'), ClauseStartsWith(starts_with='cerebras/zai-glm-4.7'), ClauseStartsWith(starts_with='cerebras:zai-glm-4.7'), ] ), name='Z.ai GLM 4.7', description="Z.ai's GLM 4.7, a 355 billion parameter model with reasoning enabled by default, served with fast inference on Cerebras hardware (~1,000 tokens/second).", context_window=131072, price_comments='Deprecated by Cerebras on 2026-08-17 (see https://inference-docs.cerebras.ai/support/change-log) and no longer in the model catalog; last published developer tier rates kept for historical usage.', deprecated=True, prices=ModelPrice(input_mtok=Decimal('2.25'), output_mtok=Decimal('2.75')), ), ], ), Provider( id='cloudflare', name='Cloudflare Workers AI', api_pattern='https://api\\.cloudflare\\.com/client/v4/accounts/[^/]+/ai(?:/|$)', pricing_urls=[ 'https://developers.cloudflare.com/workers-ai/platform/pricing/', 'https://developers.cloudflare.com/workers-ai/models/', ], price_comments="Paid Workers plan rates, expressed using Cloudflare's token-equivalent prices. Calculations do not apply the account-wide free allocation of 10,000 Neurons per day. This provider entry deliberately covers only models with token-equivalent prices. Image and audio models are omitted.", model_match=ClauseStartsWith(starts_with='@cf/'), provider_match=ClauseContains(contains='cloudflare'), extractors=[ UsageExtractor( root=['result', 'usage'], mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path=['result', 'model'], ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['input_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['output_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=True), ], api_flavor='responses', model_path='model', ), UsageExtractor( root='usage', mappings=[UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True)], api_flavor='embeddings', model_path='model', ), ], models=[ ModelInfo( id='@cf/ai4bharat/indictrans2-en-indic-1B', match=ClauseEquals(equals='@cf/ai4bharat/indictrans2-en-indic-1B'), name='IndicTrans2 EN-Indic 1B', prices=ModelPrice(input_mtok=Decimal('0.342'), output_mtok=Decimal('0.342')), ), ModelInfo( id='@cf/aisingapore/gemma-sea-lion-v4-27b-it', match=ClauseEquals(equals='@cf/aisingapore/gemma-sea-lion-v4-27b-it'), name='Gemma SEA-LION v4 27B IT', prices=ModelPrice(input_mtok=Decimal('0.351'), output_mtok=Decimal('0.555')), ), ModelInfo( id='@cf/baai/bge-base-en-v1.5', match=ClauseEquals(equals='@cf/baai/bge-base-en-v1.5'), name='BGE Base English v1.5', prices=ModelPrice(input_mtok=Decimal('0.067')), ), ModelInfo( id='@cf/baai/bge-large-en-v1.5', match=ClauseEquals(equals='@cf/baai/bge-large-en-v1.5'), name='BGE Large English v1.5', prices=ModelPrice(input_mtok=Decimal('0.204')), ), ModelInfo( id='@cf/baai/bge-m3', match=ClauseEquals(equals='@cf/baai/bge-m3'), name='BGE-M3', prices=ModelPrice(input_mtok=Decimal('0.012')), ), ModelInfo( id='@cf/baai/bge-reranker-base', match=ClauseEquals(equals='@cf/baai/bge-reranker-base'), name='BGE Reranker Base', prices=ModelPrice(input_mtok=Decimal('0.003')), ), ModelInfo( id='@cf/baai/bge-small-en-v1.5', match=ClauseEquals(equals='@cf/baai/bge-small-en-v1.5'), name='BGE Small English v1.5', prices=ModelPrice(input_mtok=Decimal('0.02')), ), ModelInfo( id='@cf/deepseek-ai/deepseek-r1-distill-qwen-32b', match=ClauseEquals(equals='@cf/deepseek-ai/deepseek-r1-distill-qwen-32b'), name='DeepSeek R1 Distill Qwen 32B', prices=ModelPrice(input_mtok=Decimal('0.497'), output_mtok=Decimal('4.881')), ), ModelInfo( id='@cf/deepseek-ai/deepseek-v4-flash-0731', match=ClauseEquals(equals='@cf/deepseek-ai/deepseek-v4-flash-0731'), name='DeepSeek V4 Flash 0731', prices=ModelPrice( input_mtok=Decimal('0.44'), cache_read_mtok=Decimal('0.014'), output_mtok=Decimal('1.32') ), ), ModelInfo( id='@cf/deepseek-ai/deepseek-v4-pro-0813', match=ClauseEquals(equals='@cf/deepseek-ai/deepseek-v4-pro-0813'), name='DeepSeek V4 Pro 0813', prices=ModelPrice( input_mtok=Decimal('1.32'), cache_read_mtok=Decimal('0.044'), output_mtok=Decimal('3.96') ), ), ModelInfo( id='@cf/google/gemma-3-12b-it', match=ClauseEquals(equals='@cf/google/gemma-3-12b-it'), name='Gemma 3 12B IT', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.345'), output_mtok=Decimal('0.556')), ), ModelInfo( id='@cf/google/gemma-4-26b-a4b-it', match=ClauseEquals(equals='@cf/google/gemma-4-26b-a4b-it'), name='Gemma 4 26B A4B IT', context_window=256000, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.3')), ), ModelInfo( id='@cf/huggingface/distilbert-sst-2-int8', match=ClauseEquals(equals='@cf/huggingface/distilbert-sst-2-int8'), name='DistilBERT SST-2 INT8', prices=ModelPrice(input_mtok=Decimal('0.026')), ), ModelInfo( id='@cf/ibm-granite/granite-4.0-h-micro', match=ClauseEquals(equals='@cf/ibm-granite/granite-4.0-h-micro'), name='Granite 4.0 H Micro', prices=ModelPrice(input_mtok=Decimal('0.017'), output_mtok=Decimal('0.112')), ), ModelInfo( id='@cf/meta/llama-2-7b-chat-fp16', match=ClauseEquals(equals='@cf/meta/llama-2-7b-chat-fp16'), name='Llama 2 7B Chat FP16', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.556'), output_mtok=Decimal('6.667')), ), ModelInfo( id='@cf/meta/llama-3-8b-instruct', match=ClauseEquals(equals='@cf/meta/llama-3-8b-instruct'), name='Llama 3 8B Instruct', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.282'), output_mtok=Decimal('0.827')), ), ModelInfo( id='@cf/meta/llama-3-8b-instruct-awq', match=ClauseEquals(equals='@cf/meta/llama-3-8b-instruct-awq'), name='Llama 3 8B Instruct AWQ', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.123'), output_mtok=Decimal('0.266')), ), ModelInfo( id='@cf/meta/llama-3.1-70b-instruct-fp8-fast', match=ClauseEquals(equals='@cf/meta/llama-3.1-70b-instruct-fp8-fast'), name='Llama 3.1 70B Instruct FP8 Fast', prices=ModelPrice(input_mtok=Decimal('0.293'), output_mtok=Decimal('2.253')), ), ModelInfo( id='@cf/meta/llama-3.1-8b-instruct', match=ClauseEquals(equals='@cf/meta/llama-3.1-8b-instruct'), name='Llama 3.1 8B Instruct', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.282'), output_mtok=Decimal('0.827')), ), ModelInfo( id='@cf/meta/llama-3.1-8b-instruct-awq', match=ClauseEquals(equals='@cf/meta/llama-3.1-8b-instruct-awq'), name='Llama 3.1 8B Instruct AWQ', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.123'), output_mtok=Decimal('0.266')), ), ModelInfo( id='@cf/meta/llama-3.1-8b-instruct-fp8', match=ClauseEquals(equals='@cf/meta/llama-3.1-8b-instruct-fp8'), name='Llama 3.1 8B Instruct FP8', prices=ModelPrice(input_mtok=Decimal('0.152'), output_mtok=Decimal('0.287')), ), ModelInfo( id='@cf/meta/llama-3.1-8b-instruct-fp8-fast', match=ClauseOr( or_=[ ClauseEquals(equals='@cf/meta/llama-3.1-8b-instruct-fp8-fast'), ClauseEquals(equals='@cf/meta/llama-3.1-8b-instruct-fast'), ] ), name='Llama 3.1 8B Instruct FP8 Fast', price_comments="Cloudflare's pricing table uses the fp8-fast identifier while the model catalog documents the fast identifier.", prices=ModelPrice(input_mtok=Decimal('0.045'), output_mtok=Decimal('0.384')), ), ModelInfo( id='@cf/meta/llama-3.2-11b-vision-instruct', match=ClauseEquals(equals='@cf/meta/llama-3.2-11b-vision-instruct'), name='Llama 3.2 11B Vision Instruct', prices=ModelPrice(input_mtok=Decimal('0.049'), output_mtok=Decimal('0.676')), ), ModelInfo( id='@cf/meta/llama-3.2-1b-instruct', match=ClauseEquals(equals='@cf/meta/llama-3.2-1b-instruct'), name='Llama 3.2 1B Instruct', prices=ModelPrice(input_mtok=Decimal('0.027'), output_mtok=Decimal('0.201')), ), ModelInfo( id='@cf/meta/llama-3.2-3b-instruct', match=ClauseEquals(equals='@cf/meta/llama-3.2-3b-instruct'), name='Llama 3.2 3B Instruct', prices=ModelPrice(input_mtok=Decimal('0.051'), output_mtok=Decimal('0.335')), ), ModelInfo( id='@cf/meta/llama-3.3-70b-instruct-fp8-fast', match=ClauseEquals(equals='@cf/meta/llama-3.3-70b-instruct-fp8-fast'), name='Llama 3.3 70B Instruct FP8 Fast', context_window=24000, prices=ModelPrice(input_mtok=Decimal('0.293'), output_mtok=Decimal('2.253')), ), ModelInfo( id='@cf/meta/llama-4-scout-17b-16e-instruct', match=ClauseEquals(equals='@cf/meta/llama-4-scout-17b-16e-instruct'), name='Llama 4 Scout 17B 16E Instruct', prices=ModelPrice(input_mtok=Decimal('0.27'), output_mtok=Decimal('0.85')), ), ModelInfo( id='@cf/meta/llama-guard-3-8b', match=ClauseEquals(equals='@cf/meta/llama-guard-3-8b'), name='Llama Guard 3 8B', prices=ModelPrice(input_mtok=Decimal('0.484'), output_mtok=Decimal('0.03')), ), ModelInfo( id='@cf/meta/m2m100-1.2b', match=ClauseEquals(equals='@cf/meta/m2m100-1.2b'), name='M2M100 1.2B', prices=ModelPrice(input_mtok=Decimal('0.342'), output_mtok=Decimal('0.342')), ), ModelInfo( id='@cf/mistral/mistral-7b-instruct-v0.1', match=ClauseEquals(equals='@cf/mistral/mistral-7b-instruct-v0.1'), name='Mistral 7B Instruct v0.1', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.11'), output_mtok=Decimal('0.19')), ), ModelInfo( id='@cf/mistralai/mistral-small-3.1-24b-instruct', match=ClauseEquals(equals='@cf/mistralai/mistral-small-3.1-24b-instruct'), name='Mistral Small 3.1 24B Instruct', prices=ModelPrice(input_mtok=Decimal('0.351'), output_mtok=Decimal('0.555')), ), ModelInfo( id='@cf/moondream/moondream3.1-9B-A2B', match=ClauseEquals(equals='@cf/moondream/moondream3.1-9B-A2B'), name='Moondream 3.1 9B A2B', prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('1')), ), ModelInfo( id='@cf/moonshotai/kimi-k2.5', match=ClauseEquals(equals='@cf/moonshotai/kimi-k2.5'), name='Kimi K2.5', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.6'), cache_read_mtok=Decimal('0.1'), output_mtok=Decimal('3')), ), ModelInfo( id='@cf/moonshotai/kimi-k2.6', match=ClauseEquals(equals='@cf/moonshotai/kimi-k2.6'), name='Kimi K2.6', prices=ModelPrice( input_mtok=Decimal('0.95'), cache_read_mtok=Decimal('0.16'), output_mtok=Decimal('4') ), ), ModelInfo( id='@cf/moonshotai/kimi-k2.7-code', match=ClauseEquals(equals='@cf/moonshotai/kimi-k2.7-code'), name='Kimi K2.7 Code', prices=ModelPrice( input_mtok=Decimal('0.95'), cache_read_mtok=Decimal('0.19'), output_mtok=Decimal('4') ), ), ModelInfo( id='@cf/nvidia/nemotron-3-120b-a12b', match=ClauseEquals(equals='@cf/nvidia/nemotron-3-120b-a12b'), name='Nemotron 3 120B A12B', prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('1.5')), ), ModelInfo( id='@cf/openai/gpt-oss-120b', match=ClauseEquals(equals='@cf/openai/gpt-oss-120b'), name='GPT-OSS 120B', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.35'), output_mtok=Decimal('0.75')), ), ModelInfo( id='@cf/openai/gpt-oss-20b', match=ClauseEquals(equals='@cf/openai/gpt-oss-20b'), name='GPT-OSS 20B', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.3')), ), ModelInfo( id='@cf/pfnet/plamo-embedding-1b', match=ClauseEquals(equals='@cf/pfnet/plamo-embedding-1b'), name='PLaMo Embedding 1B', prices=ModelPrice(input_mtok=Decimal('0.019')), ), ModelInfo( id='@cf/qwen/qwen2.5-coder-32b-instruct', match=ClauseEquals(equals='@cf/qwen/qwen2.5-coder-32b-instruct'), name='Qwen2.5 Coder 32B Instruct', prices=ModelPrice(input_mtok=Decimal('0.66'), output_mtok=Decimal('1')), ), ModelInfo( id='@cf/qwen/qwen3-30b-a3b-fp8', match=ClauseEquals(equals='@cf/qwen/qwen3-30b-a3b-fp8'), name='Qwen3 30B A3B FP8', prices=ModelPrice(input_mtok=Decimal('0.051'), output_mtok=Decimal('0.335')), ), ModelInfo( id='@cf/qwen/qwen3-embedding-0.6b', match=ClauseEquals(equals='@cf/qwen/qwen3-embedding-0.6b'), name='Qwen3 Embedding 0.6B', prices=ModelPrice(input_mtok=Decimal('0.012')), ), ModelInfo( id='@cf/qwen/qwen3.8-27b', match=ClauseEquals(equals='@cf/qwen/qwen3.8-27b'), name='Qwen3.8 27B', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.45'), output_mtok=Decimal('3.2')), ), ModelInfo( id='@cf/qwen/qwq-32b', match=ClauseEquals(equals='@cf/qwen/qwq-32b'), name='QwQ 32B', prices=ModelPrice(input_mtok=Decimal('0.66'), output_mtok=Decimal('1')), ), ModelInfo( id='@cf/zai-org/glm-4.7-flash', match=ClauseEquals(equals='@cf/zai-org/glm-4.7-flash'), name='GLM 4.7 Flash', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.06'), output_mtok=Decimal('0.4')), ), ModelInfo( id='@cf/zai-org/glm-5.2', match=ClauseEquals(equals='@cf/zai-org/glm-5.2'), name='GLM 5.2', prices=ModelPrice( input_mtok=Decimal('1.4'), cache_read_mtok=Decimal('0.26'), output_mtok=Decimal('4.4') ), ), ModelInfo( id='@cf/zai-org/glm-5.3-flash', match=ClauseEquals(equals='@cf/zai-org/glm-5.3-flash'), name='GLM 5.3 Flash', prices=ModelPrice( input_mtok=Decimal('0.15'), cache_read_mtok=Decimal('0.03'), output_mtok=Decimal('0.5') ), ), ], ), Provider( id='cohere', name='Cohere', api_pattern='https://api\\.cohere\\.(?:ai|com)', pricing_urls=['https://cohere.com/pricing'], model_match=ClauseStartsWith(starts_with='command-'), provider_match=ClauseContains(contains='cohere'), extractors=[ UsageExtractor( root=['usage', 'billed_units'], mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=True), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path=['tokens', 'input_tokens'], dest='input_tokens', required=False), UsageExtractorMapping(path=['tokens', 'output_tokens'], dest='output_tokens', required=False), UsageExtractorMapping(path='cached_tokens', dest='cache_read_tokens', required=False), ], api_flavor='tokens', model_path='model', ), UsageExtractor( root=['meta', 'billed_units'], mappings=[UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=True)], api_flavor='embeddings', model_path='model', ), ], models=[ ModelInfo( id='c4ai-aya-expanse-32b', match=ClauseContains(contains='aya-expanse-32b'), name='Aya Expanse 32B', description='Aya Expanse is a highly performant 32B multilingual model, designed to rival monolingual performance through innovations in instruction tuning with data arbitrage, preference training, and model merging. Serves 23 languages.', context_window=128000, price_comments='https://cohere.com/pricing FAQ: Aya Expanse models (8B and 32B) on the API are charged at $0.50/1M tokens for input and $1.50/1M tokens for output.', prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('1.5')), ), ModelInfo( id='c4ai-aya-expanse-8b', match=ClauseContains(contains='aya-expanse-8b'), name='Aya Expanse 8B', description='Aya Expanse is a highly performant 8B multilingual model, designed to rival monolingual performance through innovations in instruction tuning with data arbitrage, preference training, and model merging. Serves 23 languages.', context_window=8192, price_comments='Retired 2026-04-04 per https://docs.cohere.com/docs/models; last published price kept for historical usage. https://cohere.com/pricing FAQ: Aya Expanse models (8B and 32B) on the API are charged at $0.50/1M tokens for input and $1.50/1M tokens for output.', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('1.5')), ), ModelInfo( id='command', match=ClauseEquals(equals='command'), name='Command', description='Command is an instruction-following conversational model that performs language tasks with high quality, more reliably and with a longer context than our base generative models.', prices=ModelPrice(input_mtok=Decimal('1'), output_mtok=Decimal('2')), ), ModelInfo( id='command-a', match=ClauseOr(or_=[ClauseEquals(equals='command-a'), ClauseEquals(equals='command-a-03-2025')]), name='Command A', description='Command A is an open-weights 111B parameter model with a 256k context window focused on delivering great performance across agentic, multilingual, and coding use cases.\nCompared to other leading proprietary and open-weights models Command A delivers maximum performance with minimum hardware costs, excelling on business-critical agentic and multilingual tasks.', prices=ModelPrice(input_mtok=Decimal('2.5'), output_mtok=Decimal('10')), ), ModelInfo( id='command-light', match=ClauseStartsWith(starts_with='command-light'), name='Command Light', description='A smaller, faster version of Command. Almost as capable, but a lot faster.', context_window=4096, price_comments='Deprecated 2025-09-15 per https://docs.cohere.com/docs/models; still billable for existing customers. https://cohere.com/pricing FAQ: Command-light pricing is $0.30/1M tokens for input and $0.60/1M tokens for output.', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('0.6')), ), ModelInfo( id='command-r', match=ClauseOr(or_=[ClauseEquals(equals='command-r'), ClauseEquals(equals='command-r-08-2024')]), name='Command R', description='Command-R is a 35B parameter model that performs conversational language tasks at a higher quality, more reliably, and with a longer context than previous models. It can be used for complex workflows like code generation, retrieval augmented generation (RAG), tool use, and agents.', prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.6')), ), ModelInfo( id='command-r-plus', match=ClauseOr( or_=[ClauseEquals(equals='command-r-plus'), ClauseEquals(equals='command-r-plus-08-2024')] ), name='Command R+', description="Command R+ is a new, 104B-parameter LLM from Cohere. It's useful for roleplay, general consumer usecases, and Retrieval Augmented Generation (RAG).", prices=ModelPrice(input_mtok=Decimal('2.5'), output_mtok=Decimal('10')), ), ModelInfo( id='command-r7b', match=ClauseOr(or_=[ClauseEquals(equals='command-r7b'), ClauseEquals(equals='command-r7b-12-2024')]), name='Command R7B', description='Command R7B (12-2024) is a small, fast update of the Command R+ model, delivered in December 2024. It excels at RAG, tool use, agents, and similar tasks requiring complex reasoning and multiple steps.', prices=ModelPrice(input_mtok=Decimal('0.0375'), output_mtok=Decimal('0.15')), ), ModelInfo( id='embed-v4.0', match=ClauseEquals(equals='embed-v4.0'), name='Embed v4.0', description='Embed v4.0 is a state-of-the-art embedding model designed for precise retrieval across noisy, multilingual, and multimodal data.', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.12')), ), ], ), Provider( id='cursor', name='Cursor', api_pattern='https://api\\.cursor\\.com', pricing_urls=[ 'https://cursor.com/docs/models-and-pricing', 'https://cursor.com/docs/models/grok-4-6', 'https://cursor.com/docs/models/grok-4-5', 'https://cursor.com/docs/models/cursor-composer-2-5', ], description="Agentic coding models available through Cursor's editor, SDK, and Cloud Agents API.", model_match=ClauseStartsWith(starts_with='composer-'), extractors=[ UsageExtractor( root='tokenUsage', mappings=[ UsageExtractorMapping(path='inputTokens', dest='input_tokens', required=True), UsageExtractorMapping(path='cacheWriteTokens', dest='input_tokens', required=True), UsageExtractorMapping(path='cacheReadTokens', dest='input_tokens', required=True), UsageExtractorMapping(path='cacheWriteTokens', dest='cache_write_tokens', required=True), UsageExtractorMapping(path='cacheReadTokens', dest='cache_read_tokens', required=True), UsageExtractorMapping(path='outputTokens', dest='output_tokens', required=True), ], api_flavor='usage-event', model_path='model', ) ], models=[ ModelInfo( id='composer-2.5', match=ClauseOr( or_=[ClauseEquals(equals='composer-2.5'), ClauseRegex(regex='^composer-2\\.5\\[fast=false\\]$')] ), name='Composer 2.5', description="Cursor's agentic coding model, optimized for long-running tasks and tool use.", context_window=200000, price_comments='Standard on-demand usage. Fast is a separately priced variant.', prices=ModelPrice( input_mtok=Decimal('0.5'), cache_read_mtok=Decimal('0.2'), output_mtok=Decimal('2.5') ), ), ModelInfo( id='composer-2.5-fast', match=ClauseOr( or_=[ClauseEquals(equals='composer-2.5-fast'), ClauseRegex(regex='^composer-2\\.5\\[fast=true\\]$')] ), name='Composer 2.5 Fast', description='Faster Composer 2.5 speed tier with the same model intelligence.', context_window=200000, price_comments='Fast on-demand usage.', prices=ModelPrice(input_mtok=Decimal('3'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('15')), ), ModelInfo( id='grok-4.5', match=ClauseOr( or_=[ClauseEquals(equals='grok-4.5'), ClauseRegex(regex='^grok-4\\.5\\[fast=false\\]$')] ), name='Grok 4.5', description="Cursor and SpaceXAI's agentic model for long-running coding and knowledge work.", context_window=256000, price_comments='Standard on-demand usage. Fast is a separately priced variant.', prices=ModelPrice(input_mtok=Decimal('2'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('6')), ), ModelInfo( id='grok-4.5-fast', match=ClauseOr( or_=[ClauseEquals(equals='grok-4.5-fast'), ClauseRegex(regex='^grok-4\\.5\\[fast=true\\]$')] ), name='Grok 4.5 Fast', description='Faster Grok 4.5 speed tier for agentic coding and knowledge work.', context_window=256000, price_comments='Fast on-demand usage.', prices=ModelPrice(input_mtok=Decimal('4'), cache_read_mtok=Decimal('1'), output_mtok=Decimal('18')), ), ModelInfo( id='grok-4.6', match=ClauseOr( or_=[ClauseEquals(equals='grok-4.6'), ClauseRegex(regex='^grok-4\\.6\\[fast=false\\]$')] ), name='Grok 4.6', description="Cursor and SpaceXAI's frontier model for complex coding and knowledge work.", context_window=256000, price_comments='Standard on-demand usage. Fast is a separately priced variant.', prices=ModelPrice(input_mtok=Decimal('2'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('6')), ), ModelInfo( id='grok-4.6-fast', match=ClauseOr( or_=[ClauseEquals(equals='grok-4.6-fast'), ClauseRegex(regex='^grok-4\\.6\\[fast=true\\]$')] ), name='Grok 4.6 Fast', description='Faster Grok 4.6 speed tier for complex coding and knowledge work.', context_window=256000, price_comments='Fast on-demand usage.', prices=ModelPrice(input_mtok=Decimal('4'), cache_read_mtok=Decimal('1'), output_mtok=Decimal('12')), ), ], ), Provider( id='deepseek', name='Deepseek', api_pattern='https://api\\.deepseek\\.com', pricing_urls=['https://api-docs.deepseek.com/quick_start/pricing'], price_comments='Deepseek off-peak pricing applies "UTC 16:30-00:30" so we switch it around and use the off-peak pricing as the default (first) price then the second price with a constraint is the "standard" pricing that applies "UTC 00:30-16:30".', model_match=ClauseStartsWith(starts_with='deepseek'), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ) ], models=[ ModelInfo( id='deepseek-chat', match=ClauseOr( or_=[ClauseStartsWith(starts_with='deepseek-chat'), ClauseEquals(equals='deepseek-chat-v3-0324')] ), name='DeepSeek Chat', description='DeepSeek-V3 is the latest model from the DeepSeek team, building upon the instruction following and coding abilities of the previous versions. Pre-trained on nearly 15 trillion tokens, the reported evaluations reveal that the model outperforms other open-source models and rivals leading closed-source models.', context_window=64000, prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=Decimal('0.135'), cache_read_mtok=Decimal('0.035'), output_mtok=Decimal('0.55') ) ), ConditionalPrice( constraint=TimeOfDateConstraint( start_time=datetime.time(0, 30, tzinfo=datetime.timezone.utc), end_time=datetime.time(16, 30, tzinfo=datetime.timezone.utc), ), prices=ModelPrice( input_mtok=Decimal('0.27'), cache_read_mtok=Decimal('0.07'), output_mtok=Decimal('1.1') ), ), ], ), ModelInfo( id='deepseek-reasoner', match=ClauseOr( or_=[ ClauseEquals(equals='deepseek-reasoner'), ClauseStartsWith(starts_with='deepseek-r1'), ClauseEquals(equals='deepseek-r1-0528'), ] ), name='Deepseek R1', description="DeepSeek R1 is here: Performance on par with OpenAI o1, but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass.", context_window=64000, prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=Decimal('0.135'), cache_read_mtok=Decimal('0.035'), output_mtok=Decimal('0.55') ) ), ConditionalPrice( constraint=TimeOfDateConstraint( start_time=datetime.time(0, 30, tzinfo=datetime.timezone.utc), end_time=datetime.time(16, 30, tzinfo=datetime.timezone.utc), ), prices=ModelPrice( input_mtok=Decimal('0.55'), cache_read_mtok=Decimal('0.14'), output_mtok=Decimal('2.19') ), ), ], ), ModelInfo( id='deepseek-v3.1-terminus', match=ClauseEquals(equals='deepseek-v3.1-terminus'), name='DeepSeek V3.1 Terminus', description="DeepSeek-V3.1 Terminus is an update to DeepSeek V3.1 that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities.", context_window=128000, prices=ModelPrice( input_mtok=Decimal('0.27'), cache_read_mtok=Decimal('0.13'), output_mtok=Decimal('0.95') ), ), ModelInfo( id='deepseek-v3.2', match=ClauseEquals(equals='deepseek-v3.2'), name='DeepSeek V3.2', description='DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance.', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.2288'), output_mtok=Decimal('0.3432')), ), ModelInfo( id='deepseek-v3.2-exp', match=ClauseEquals(equals='deepseek-v3.2-exp'), name='DeepSeek V3.2 Exp', description='DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures.', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.27'), output_mtok=Decimal('0.41')), ), ModelInfo( id='deepseek-v4-flash', match=ClauseOr(or_=[ClauseStartsWith(starts_with='deepseek-v4-flash')]), name='DeepSeek V4 Flash', description='DeepSeek-V4-Flash. Supports both non-thinking and thinking (default) modes, JSON output, tool calls, chat prefix completion, and FIM completion (non-thinking only).', context_window=1000000, price_comments='From 2026-08-17 the V4 models use different peak windows from the V3 models: peak is "UTC 01:00-04:00" and "UTC 06:00-10:00", everything else is off-peak. The flat rate that applied before 2026-08-17 is kept as the unconstrained first price, the new off-peak rate is gated on start_date, and the two peak windows come last so they win during peak hours. Two things this layout still cannot say, because `constraint` is a union and one entry cannot carry both a date and a daily window. Requests from before 2026-08-17 that fall inside 01:00-04:00 or 06:00-10:00 UTC - 7 hours of every day - resolve to the new peak rate rather than the old flat rate. And from 2026-08-23 Deepseek bills off-peak all day at weekends (Beijing time), which needs a day-of-week condition, so on Saturdays and Sundays the peak prices here are an upper bound. See https://github.com/pydantic/genai-prices/issues/582.', prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=Decimal('0.14'), cache_read_mtok=Decimal('0.0028'), output_mtok=Decimal('0.28') ) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2026, 8, 17)), prices=ModelPrice( input_mtok=Decimal('0.22'), cache_read_mtok=Decimal('0.007'), output_mtok=Decimal('0.66') ), ), ConditionalPrice( constraint=TimeOfDateConstraint( start_time=datetime.time(1, 0, tzinfo=datetime.timezone.utc), end_time=datetime.time(4, 0, tzinfo=datetime.timezone.utc), ), prices=ModelPrice( input_mtok=Decimal('0.44'), cache_read_mtok=Decimal('0.014'), output_mtok=Decimal('1.32') ), ), ConditionalPrice( constraint=TimeOfDateConstraint( start_time=datetime.time(6, 0, tzinfo=datetime.timezone.utc), end_time=datetime.time(10, 0, tzinfo=datetime.timezone.utc), ), prices=ModelPrice( input_mtok=Decimal('0.44'), cache_read_mtok=Decimal('0.014'), output_mtok=Decimal('1.32') ), ), ], ), ModelInfo( id='deepseek-v4-pro', match=ClauseOr(or_=[ClauseStartsWith(starts_with='deepseek-v4-pro')]), name='DeepSeek V4 Pro', description='DeepSeek-V4-Pro. Supports both non-thinking and thinking (default) modes, JSON output, tool calls, chat prefix completion, and FIM completion (non-thinking only).', context_window=1000000, price_comments='From 2026-08-17 the V4 models use different peak windows from the V3 models: peak is "UTC 01:00-04:00" and "UTC 06:00-10:00", everything else is off-peak. The flat rate that applied before 2026-08-17 is kept as the unconstrained first price, the new off-peak rate is gated on start_date, and the two peak windows come last so they win during peak hours. Two things this layout still cannot say, because `constraint` is a union and one entry cannot carry both a date and a daily window. Requests from before 2026-08-17 that fall inside 01:00-04:00 or 06:00-10:00 UTC - 7 hours of every day - resolve to the new peak rate rather than the old flat rate. And from 2026-08-23 Deepseek bills off-peak all day at weekends (Beijing time), which needs a day-of-week condition, so on Saturdays and Sundays the peak prices here are an upper bound. See https://github.com/pydantic/genai-prices/issues/582.', prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=Decimal('0.435'), cache_read_mtok=Decimal('0.003625'), output_mtok=Decimal('0.87'), ) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2026, 8, 17)), prices=ModelPrice( input_mtok=Decimal('0.66'), cache_read_mtok=Decimal('0.022'), output_mtok=Decimal('1.98') ), ), ConditionalPrice( constraint=TimeOfDateConstraint( start_time=datetime.time(1, 0, tzinfo=datetime.timezone.utc), end_time=datetime.time(4, 0, tzinfo=datetime.timezone.utc), ), prices=ModelPrice( input_mtok=Decimal('1.32'), cache_read_mtok=Decimal('0.044'), output_mtok=Decimal('3.96') ), ), ConditionalPrice( constraint=TimeOfDateConstraint( start_time=datetime.time(6, 0, tzinfo=datetime.timezone.utc), end_time=datetime.time(10, 0, tzinfo=datetime.timezone.utc), ), prices=ModelPrice( input_mtok=Decimal('1.32'), cache_read_mtok=Decimal('0.044'), output_mtok=Decimal('3.96') ), ), ], ), ], ), Provider( id='doubleword', name='Doubleword', api_pattern='https://api\\.doubleword\\.ai', pricing_urls=['https://docs.doubleword.ai/inference-api/models'], price_comments='Doubleword publishes Realtime, Async, and Batch prices. This provider currently encodes only Realtime pricing.', extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['input_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=True), ], api_flavor='responses', model_path='model', ), UsageExtractor( root='usage', mappings=[UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True)], api_flavor='embeddings', model_path='model', ), ], models=[ ModelInfo( id='Qwen/Qwen3-14B-FP8', match=ClauseEquals(equals='Qwen/Qwen3-14B-FP8'), name='Qwen3 14B', prices=ModelPrice(input_mtok=Decimal('0.05'), output_mtok=Decimal('0.6')), ), ModelInfo( id='Qwen/Qwen3-Embedding-8B', match=ClauseEquals(equals='Qwen/Qwen3-Embedding-8B'), name='Qwen3 Embedding 8B', prices=ModelPrice(input_mtok=Decimal('0.04')), ), ModelInfo( id='Qwen/Qwen3-VL-235B-A22B-Instruct-FP8', match=ClauseEquals(equals='Qwen/Qwen3-VL-235B-A22B-Instruct-FP8'), name='Qwen3 VL 235B A22B Instruct', prices=ModelPrice(input_mtok=Decimal('0.6'), output_mtok=Decimal('1.2')), ), ModelInfo( id='Qwen/Qwen3-VL-30B-A3B-Instruct-FP8', match=ClauseEquals(equals='Qwen/Qwen3-VL-30B-A3B-Instruct-FP8'), name='Qwen3 VL 30B A3B Instruct', prices=ModelPrice(input_mtok=Decimal('0.16'), output_mtok=Decimal('0.8')), ), ModelInfo( id='Qwen/Qwen3.5-35B-A3B-FP8', match=ClauseEquals(equals='Qwen/Qwen3.5-35B-A3B-FP8'), name='Qwen3.5 35B A3B', prices=ModelPrice(input_mtok=Decimal('0.25'), output_mtok=Decimal('2')), ), ModelInfo( id='Qwen/Qwen3.5-397B-A17B', match=ClauseEquals(equals='Qwen/Qwen3.5-397B-A17B'), name='Qwen3.5 397B A17B', prices=ModelPrice(input_mtok=Decimal('0.6'), output_mtok=Decimal('3.6')), ), ModelInfo( id='Qwen/Qwen3.5-9B', match=ClauseEquals(equals='Qwen/Qwen3.5-9B'), name='Qwen3.5 9B', prices=ModelPrice(input_mtok=Decimal('0.08'), output_mtok=Decimal('0.7')), ), ModelInfo( id='Qwen/Qwen3.6-35B-A3B-FP8', match=ClauseEquals(equals='Qwen/Qwen3.6-35B-A3B-FP8'), name='Qwen3.6 35B A3B', prices=ModelPrice(input_mtok=Decimal('0.25'), output_mtok=Decimal('2')), ), ModelInfo( id='Qwen/Qwen3.8-27B-FP8', match=ClauseEquals(equals='Qwen/Qwen3.8-27B-FP8'), name='Qwen3.8 27B', context_window=262144, price_comments='Realtime pricing. See https://docs.doubleword.ai/inference-api/models/qwen-qwen3-8-27b-fp8.', prices=ModelPrice( input_mtok=Decimal('0.45'), cache_read_mtok=Decimal('0.04'), output_mtok=Decimal('3') ), ), ModelInfo( id='deepseek-ai/DeepSeek-V4-Flash', match=ClauseEquals(equals='deepseek-ai/DeepSeek-V4-Flash'), name='DeepSeek V4 Flash', prices=ModelPrice(input_mtok=Decimal('0.14'), output_mtok=Decimal('0.28')), ), ModelInfo( id='deepseek-ai/DeepSeek-V4-Flash-0731', match=ClauseEquals(equals='deepseek-ai/DeepSeek-V4-Flash-0731'), name='DeepSeek V4 Flash 0731', price_comments='Realtime pricing. See https://docs.doubleword.ai/inference-api/models/deepseek-ai-deepseek-v4-flash-0731.', prices=ModelPrice( input_mtok=Decimal('0.09'), cache_read_mtok=Decimal('0.02'), output_mtok=Decimal('0.18') ), ), ModelInfo( id='deepseek-ai/DeepSeek-V4-Pro', match=ClauseEquals(equals='deepseek-ai/DeepSeek-V4-Pro'), name='DeepSeek V4 Pro', prices=ModelPrice(input_mtok=Decimal('1.74'), output_mtok=Decimal('3.48')), ), ModelInfo( id='deepseek-ai/DeepSeek-V4.1-Flash', match=ClauseEquals(equals='deepseek-ai/DeepSeek-V4.1-Flash'), name='DeepSeek V4.1 Flash', price_comments='Realtime pricing. See https://docs.doubleword.ai/inference-api/models/deepseek-ai-deepseek-v4-1-flash.', prices=ModelPrice( input_mtok=Decimal('0.15'), cache_read_mtok=Decimal('0.01'), output_mtok=Decimal('0.6') ), ), ModelInfo( id='google/gemma-4-31B-it', match=ClauseEquals(equals='google/gemma-4-31B-it'), name='Gemma 4 31B IT', prices=ModelPrice(input_mtok=Decimal('0.14'), output_mtok=Decimal('0.4')), ), ModelInfo( id='mistralai/Devstral-2-123B-Instruct-2512', match=ClauseEquals(equals='mistralai/Devstral-2-123B-Instruct-2512'), name='Devstral 2 123B Instruct 2512', prices=ModelPrice(input_mtok=Decimal('0.4'), output_mtok=Decimal('2')), ), ModelInfo( id='moonshotai/Kimi-K2.6', match=ClauseEquals(equals='moonshotai/Kimi-K2.6'), name='Kimi K2.6', prices=ModelPrice(input_mtok=Decimal('0.95'), output_mtok=Decimal('4')), ), ModelInfo( id='moonshotai/kimi-k3', match=ClauseEquals(equals='moonshotai/kimi-k3'), name='Kimi K3', context_window=1048576, price_comments='Realtime pricing. See https://docs.doubleword.ai/inference-api/models/moonshotai-kimi-k3.', prices=ModelPrice(input_mtok=Decimal('3'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15')), ), ModelInfo( id='nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4', match=ClauseEquals(equals='nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4'), name='Nemotron 3 Super 120B A12B', prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('0.75')), ), ModelInfo( id='openai/gpt-oss-20b', match=ClauseEquals(equals='openai/gpt-oss-20b'), name='GPT OSS 20B', prices=ModelPrice(input_mtok=Decimal('0.04'), output_mtok=Decimal('0.3')), ), ModelInfo( id='zai-org/GLM-5.1-FP8', match=ClauseEquals(equals='zai-org/GLM-5.1-FP8'), name='GLM 5.1', prices=ModelPrice(input_mtok=Decimal('1.4'), output_mtok=Decimal('4.4')), ), ModelInfo( id='zai-org/GLM-5.2-FP8', match=ClauseEquals(equals='zai-org/GLM-5.2-FP8'), name='GLM 5.2', context_window=1048576, price_comments='Realtime pricing. See https://docs.doubleword.ai/inference-api/models/zai-org-glm-5-2-fp8.', prices=ModelPrice( input_mtok=Decimal('0.93'), cache_read_mtok=Decimal('0.19'), output_mtok=Decimal('3') ), ), ], ), Provider( id='fireworks', name='Fireworks', api_pattern='https://api\\.fireworks\\.ai', pricing_urls=['https://fireworks.ai/pricing'], model_match=ClauseStartsWith(starts_with='accounts/fireworks/'), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ) ], models=[ ModelInfo( id='deepseek-r1-0528', match=ClauseEquals(equals='accounts/fireworks/models/deepseek-r1-0528'), name='DeepSeek R1 0528', description='The updated DeepSeek-R1-0528 model delivers major improvements in reasoning, inference, and accuracy through enhanced post-training optimization and greater computational resources. It now performs at a level approaching top-tier models like O3 and Gemini 2.5 Pro, with notable gains in complex tasks such as math and programming.', context_window=160000, prices=ModelPrice(input_mtok=Decimal('3'), output_mtok=Decimal('8')), ), ModelInfo( id='deepseek-v3-0324', match=ClauseEquals(equals='accounts/fireworks/models/deepseek-v3-0324'), name='Deepseek V3 03-24', description='A strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token from Deepseek. Updated checkpoint.', context_window=160000, price_comments='docs give just one price - "Pricing Per 1M Tokens", we assume that\'s input and output', prices=ModelPrice(input_mtok=Decimal('0.9'), output_mtok=Decimal('0.9')), ), ModelInfo( id='deepseek-v3p2', match=ClauseEquals(equals='accounts/fireworks/models/deepseek-v3p2'), name='Deepseek V3.2', description='Model from Deepseek that harmonizes high computational efficiency with superior reasoning and agent performance. 675B parameter MoE model.', context_window=163840, prices=ModelPrice( input_mtok=Decimal('0.56'), cache_read_mtok=Decimal('0.28'), output_mtok=Decimal('1.68') ), ), ModelInfo( id='deepseek-v4-flash', match=ClauseOr( or_=[ ClauseEquals(equals='accounts/fireworks/models/deepseek-v4-flash'), ClauseEquals(equals='accounts/fireworks/models/deepseek-v4-flash-0731'), ] ), name='DeepSeek-V4-Flash', description='Official release of DeepSeek-V4-Flash with enhanced agentic capabilities and speculative decoding.', context_window=1040000, price_comments='Standard serverless pricing. See https://docs.fireworks.ai/serverless/pricing.', prices=ModelPrice( input_mtok=Decimal('0.14'), cache_read_mtok=Decimal('0.028'), output_mtok=Decimal('0.28') ), ), ModelInfo( id='deepseek-v4-pro', match=ClauseEquals(equals='accounts/fireworks/models/deepseek-v4-pro'), name='DeepSeek-V4-Pro', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('1.74'), cache_read_mtok=Decimal('0.145'), output_mtok=Decimal('3.48') ), ), ModelInfo( id='gemma-3-27b-it', match=ClauseEquals(equals='accounts/fireworks/models/gemma-3-27b-it'), name='Gemma 3 27B Instruct', context_window=131000, price_comments='docs give just one price - "Pricing Per 1M Tokens", we assume that\'s input and output', prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ), ModelInfo( id='glm-4p7', match=ClauseEquals(equals='accounts/fireworks/models/glm-4p7'), name='GLM-4.7', description='Next-generation general-purpose model from Z.ai optimized for coding, reasoning, and agentic workflows. 352B parameter MoE model with advanced thinking controls.', context_window=202752, prices=ModelPrice(input_mtok=Decimal('0.6'), output_mtok=Decimal('2.2')), ), ModelInfo( id='glm-5p1', match=ClauseEquals(equals='accounts/fireworks/models/glm-5p1'), name='GLM-5.1', prices=ModelPrice( input_mtok=Decimal('1.4'), cache_read_mtok=Decimal('0.26'), output_mtok=Decimal('4.4') ), ), ModelInfo( id='glm-5p1-fast', match=ClauseEquals(equals='accounts/fireworks/routers/glm-5p1-fast'), name='GLM 5.1 Fast', price_comments='Fast serverless pricing. See https://docs.fireworks.ai/serverless/pricing.', prices=ModelPrice( input_mtok=Decimal('2.8'), cache_read_mtok=Decimal('0.52'), output_mtok=Decimal('8.8') ), ), ModelInfo( id='glm-5p2', match=ClauseEquals(equals='accounts/fireworks/models/glm-5p2'), name='GLM-5.2', description='GLM-5.2 introduces a robust 1M-token context and advanced, multi-effort coding capabilities to significantly enhance performance on long-horizon tasks. Features a new IndexShare architecture and improved MTP layer for greater efficiency. 743B parameter MoE model from Z.ai.', context_window=1040000, prices=ModelPrice( input_mtok=Decimal('1.4'), cache_read_mtok=Decimal('0.14'), output_mtok=Decimal('4.4') ), ), ModelInfo( id='glm-5p2-fast', match=ClauseOr( or_=[ ClauseEquals(equals='accounts/fireworks/routers/glm-5p2-fast'), ClauseEquals(equals='accounts/fireworks/routers/glm-5p2-fast-us'), ] ), name='GLM 5.2 Fast', context_window=1040000, price_comments='Fast and US-only serverless pricing. See https://docs.fireworks.ai/serverless/pricing.', prices=ModelPrice( input_mtok=Decimal('2.1'), cache_read_mtok=Decimal('0.21'), output_mtok=Decimal('6.6') ), ), ModelInfo( id='gpt-oss-120b', match=ClauseEquals(equals='accounts/fireworks/models/gpt-oss-120b'), name='OpenAI gpt-oss-120b', description="OpenAI's open-weight 117B parameter MoE model designed for production, general purpose, high reasoning use-cases. Features powerful reasoning, agentic tasks, and versatile developer use cases.", context_window=131072, price_comments='Standard serverless pricing. See https://fireworks.ai/models/fireworks/gpt-oss-120b.', prices=ModelPrice( input_mtok=Decimal('0.15'), cache_read_mtok=Decimal('0.014'), output_mtok=Decimal('0.6') ), ), ModelInfo( id='gpt-oss-20b', match=ClauseEquals(equals='accounts/fireworks/models/gpt-oss-20b'), name='OpenAI gpt-oss-20b', description="OpenAI's open-weight 21.5B parameter model designed for powerful reasoning, agentic tasks, and versatile developer use cases. Optimized for lower latency and local or specialized tasks.", context_window=131072, price_comments='Standard serverless pricing. See https://fireworks.ai/models/fireworks/gpt-oss-20b.', prices=ModelPrice( input_mtok=Decimal('0.07'), cache_read_mtok=Decimal('0.035'), output_mtok=Decimal('0.3') ), ), ModelInfo( id='inkling', match=ClauseEquals(equals='accounts/fireworks/models/inkling'), name='Inkling', description="Thinking Machines Lab's open-weights multimodal Mixture-of-Experts model, trained across text, image, and audio.", context_window=1040000, price_comments='Standard serverless pricing. See https://fireworks.ai/models/fireworks/inkling.', prices=ModelPrice( input_mtok=Decimal('1'), cache_read_mtok=Decimal('0.17'), output_mtok=Decimal('4.05') ), ), ModelInfo( id='kimi-k2p5', match=ClauseEquals(equals='accounts/fireworks/models/kimi-k2p5'), name='Kimi K2.5', description="Moonshot AI's flagship agentic model. Unifies vision and text, thinking and non-thinking modes, and single-agent and multi-agent execution into one model. 1T parameter MoE model.", context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.6'), cache_read_mtok=Decimal('0.1'), output_mtok=Decimal('3')), ), ModelInfo( id='kimi-k2p6', match=ClauseEquals(equals='accounts/fireworks/models/kimi-k2p6'), name='Kimi K2.6', prices=ModelPrice( input_mtok=Decimal('0.95'), cache_read_mtok=Decimal('0.16'), output_mtok=Decimal('4') ), ), ModelInfo( id='kimi-k2p6-fast', match=ClauseEquals(equals='accounts/fireworks/routers/kimi-k2p6-fast'), name='Kimi K2.6 Fast', context_window=262144, price_comments='Fast serverless pricing. See https://docs.fireworks.ai/serverless/pricing.', prices=ModelPrice(input_mtok=Decimal('2'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('8')), ), ModelInfo( id='kimi-k2p7-code', match=ClauseEquals(equals='accounts/fireworks/models/kimi-k2p7-code'), name='Kimi K2.7 Code', description='Kimi K2.7 Code is a coding-focused agentic model built upon Kimi K2.6, delivering substantial improvements on real-world long-horizon coding tasks while reducing thinking tokens by roughly 30% compared to its predecessor.', context_window=262144, prices=ModelPrice( input_mtok=Decimal('0.95'), cache_read_mtok=Decimal('0.19'), output_mtok=Decimal('4') ), ), ModelInfo( id='kimi-k2p7-code-fast', match=ClauseEquals(equals='accounts/fireworks/routers/kimi-k2p7-code-fast'), name='Kimi K2.7 Code Fast', context_window=262144, price_comments='Fast serverless pricing. See https://docs.fireworks.ai/serverless/pricing.', prices=ModelPrice(input_mtok=Decimal('1.9'), cache_read_mtok=Decimal('0.38'), output_mtok=Decimal('8')), ), ModelInfo( id='kimi-k3', match=ClauseEquals(equals='accounts/fireworks/models/kimi-k3'), name='Kimi K3', description="Moonshot AI's 2.81T-parameter flagship model with native visual understanding and a 1M-token context window.", context_window=1040000, price_comments='Standard serverless pricing. See https://fireworks.ai/models/fireworks/kimi-k3. Fast and US router variants have separate model IDs.', prices=ModelPrice(input_mtok=Decimal('3'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15')), ), ModelInfo( id='kimi-k3-fast', match=ClauseEquals(equals='accounts/fireworks/routers/kimi-k3-fast'), name='Kimi K3 Fast', context_window=1040000, price_comments='Fast serverless pricing. See https://docs.fireworks.ai/serverless/pricing.', prices=ModelPrice( input_mtok=Decimal('4.5'), cache_read_mtok=Decimal('0.45'), output_mtok=Decimal('22.5') ), ), ModelInfo( id='kimi-k3-us', match=ClauseEquals(equals='accounts/fireworks/routers/kimi-k3-us'), name='Kimi K3 US', context_window=1040000, price_comments='US-only serverless pricing. See https://docs.fireworks.ai/serverless/us-only-serverless.', prices=ModelPrice( input_mtok=Decimal('3.3'), cache_read_mtok=Decimal('0.33'), output_mtok=Decimal('16.5') ), ), ModelInfo( id='llama-v3p1-8b-instruct', match=ClauseEquals(equals='accounts/fireworks/models/llama-v3p1-8b-instruct'), name='Llama 3.1 8B Instruct', description='The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes. The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.', context_window=131000, price_comments='docs give just one price - "Pricing Per 1M Tokens", we assume that\'s input and output', prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='llama4-maverick-instruct-basic', match=ClauseEquals(equals='accounts/fireworks/models/llama4-maverick-instruct-basic'), name='Llama 4 Maverick Instruct (Basic)', description='The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes. The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.', context_window=1000000, prices=ModelPrice(input_mtok=Decimal('0.22'), output_mtok=Decimal('0.88')), ), ModelInfo( id='minimax-m2p1', match=ClauseEquals(equals='accounts/fireworks/models/minimax-m2p1'), name='MiniMax-M2.1', description='Built for strong real-world performance across complex, multi-language, and agent-driven workflows. 228B parameter model with robust support for systems, backend, web, mobile, and office-style tasks.', context_window=204800, prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('1.2')), ), ModelInfo( id='minimax-m2p7', match=ClauseEquals(equals='accounts/fireworks/models/minimax-m2p7'), name='MiniMax M2.7', prices=ModelPrice( input_mtok=Decimal('0.3'), cache_read_mtok=Decimal('0.06'), output_mtok=Decimal('1.2') ), ), ModelInfo( id='minimax-m3', match=ClauseEquals(equals='accounts/fireworks/models/minimax-m3'), name='MiniMax M3', description='Multimodal foundation model from MiniMax with text, image, and video inputs, a long context window, and long-horizon agentic work.', context_window=524288, prices=ModelPrice( input_mtok=Decimal('0.3'), cache_read_mtok=Decimal('0.06'), output_mtok=Decimal('1.2') ), ), ModelInfo( id='nemotron-3-ultra-nvfp4', match=ClauseEquals(equals='accounts/fireworks/models/nemotron-3-ultra-nvfp4'), name='NVIDIA Nemotron 3 Ultra NVFP4', description='Frontier-scale LLM from NVIDIA using a hybrid Latent Mixture-of-Experts (LatentMoE) architecture with interleaved Mamba-2 and MoE layers plus select Attention layers. Features 55B active parameters out of 550B total and Multi-Token Prediction layers for faster generation, optimized for complex multi-step agents, long-context analysis, and high-accuracy reasoning over code, math, and science.', context_window=262000, prices=ModelPrice( input_mtok=Decimal('0.6'), cache_read_mtok=Decimal('0.12'), output_mtok=Decimal('2.4') ), ), ModelInfo( id='qwen2p5-vl-72b-instruct', match=ClauseEquals(equals='accounts/fireworks/models/qwen2p5-vl-72b-instruct'), name='Qwen2.5-VL 72B Instruct', description="Latest Qwen's VLM model", context_window=128000, price_comments='docs give just one price - "Pricing Per 1M Tokens", we assume that\'s input and output', prices=ModelPrice(input_mtok=Decimal('0.9'), output_mtok=Decimal('0.9')), ), ModelInfo( id='qwen3-235b-a22b', match=ClauseEquals(equals='accounts/fireworks/models/qwen3-235b-a22b'), name='Qwen3 235B-A22B', description='Qwen3 is the latest evolution in the Qwen LLM series, featuring both dense and MoE models with major advancements in reasoning, agent capabilities, multilingual support, and instruction following. It uniquely allows seamless switching between "thinking" (for complex logic, math, coding) and "non-thinking" modes (for fast, general dialogue), delivering strong performance across tasks.', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.22'), output_mtok=Decimal('0.88')), ), ModelInfo( id='qwen3p6-plus', match=ClauseEquals(equals='accounts/fireworks/models/qwen3p6-plus'), name='Qwen3.6 Plus', prices=ModelPrice(input_mtok=Decimal('0.5'), cache_read_mtok=Decimal('0.1'), output_mtok=Decimal('3')), ), ModelInfo( id='qwen3p7-plus', match=ClauseEquals(equals='accounts/fireworks/models/qwen3p7-plus'), name='Qwen3.7 Plus', description="Qwen3.7 Plus is Alibaba's latest flagship closed model, available exclusively through Fireworks AI outside of Alibaba's own infrastructure.", context_window=262144, prices=ModelPrice( input_mtok=Decimal('0.4'), cache_read_mtok=Decimal('0.08'), output_mtok=Decimal('1.6') ), ), ], ), Provider( id='github-copilot', name='GitHub Copilot', api_pattern='https://api\\.githubcopilot\\.com', pricing_urls=[ 'https://docs.github.com/en/copilot/reference/copilot-billing/models-and-pricing', 'https://docs.github.com/en/copilot/concepts/billing/usage-based-billing-for-individuals', ], description="OpenAI, Anthropic, Google, xAI, Moonshot AI and Microsoft models served through GitHub Copilot's OpenAI-compatible API at api.githubcopilot.com.", price_comments="Copilot meters token usage in AI credits, where 1 credit = $0.01; the rates recorded here are the published per-model USD rates from the models-and-pricing page. Every response also carries a `copilot_usage.token_details[].cost_per_batch` figure in nano-credits, which agreed with the published rate for each of the nine priced models reachable on the plan used to check them on 2026-09-03. Long-context tiers reprice the whole request once the prompt crosses the threshold. Not represented: embedding models and Copilot-internal agent models, which have no published rate, and `gpt-4`, which Copilot routes to GPT-4.1 and bills at a rate the pricing page does not list. Catalog membership varies by subscription and by API integrator, so this is not the union of every plan's models.", provider_match=ClauseContains(contains='copilot'), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['input_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['input_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['output_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=True), ], api_flavor='responses', model_path='model', ), ], models=[ ModelInfo( id='claude-fable-5', match=ClauseEquals(equals='claude-fable-5'), name='Claude Fable 5', context_window=264000, price_comments='The pricing page gives cached input as a $0.25-$1.00 range; the highest rate is recorded.', prices=ModelPrice( input_mtok=Decimal('10'), cache_write_mtok=Decimal('12.5'), cache_read_mtok=Decimal('1'), output_mtok=Decimal('50'), ), ), ModelInfo( id='claude-fable-5.1', match=ClauseEquals(equals='claude-fable-5.1'), name='Claude Fable 5.1', context_window=264000, price_comments='The pricing page gives cached input as a $0.25-$1.00 range; the highest rate is recorded.', prices=ModelPrice( input_mtok=Decimal('10'), cache_write_mtok=Decimal('12.5'), cache_read_mtok=Decimal('1'), output_mtok=Decimal('50'), ), ), ModelInfo( id='claude-haiku-4.5', match=ClauseEquals(equals='claude-haiku-4.5'), name='Claude Haiku 4.5', context_window=144000, prices=ModelPrice( input_mtok=Decimal('1'), cache_write_mtok=Decimal('1.25'), cache_read_mtok=Decimal('0.1'), output_mtok=Decimal('5'), ), ), ModelInfo( id='claude-opus-4.7', match=ClauseEquals(equals='claude-opus-4.7'), name='Claude Opus 4.7', context_window=264000, prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), ), ), ModelInfo( id='claude-opus-4.8', match=ClauseEquals(equals='claude-opus-4.8'), name='Claude Opus 4.8', context_window=264000, prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), ), ), ModelInfo( id='claude-opus-4.8-fast', match=ClauseEquals(equals='claude-opus-4.8-fast'), name='Claude Opus 4.8 (fast mode)', context_window=264000, price_comments='Fast mode is priced at 2x the standard Opus 4.8 rates.', prices=ModelPrice( input_mtok=Decimal('10'), cache_write_mtok=Decimal('12.5'), cache_read_mtok=Decimal('1'), output_mtok=Decimal('50'), ), ), ModelInfo( id='claude-opus-5', match=ClauseEquals(equals='claude-opus-5'), name='Claude Opus 5', context_window=264000, prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), ), ), ModelInfo( id='claude-sonnet-5', match=ClauseEquals(equals='claude-sonnet-5'), name='Claude Sonnet 5', context_window=264000, prices=ModelPrice( input_mtok=Decimal('2'), cache_write_mtok=Decimal('2.5'), cache_read_mtok=Decimal('0.2'), output_mtok=Decimal('10'), ), ), ModelInfo( id='gemini-3.5-flash', match=ClauseEquals(equals='gemini-3.5-flash'), name='Gemini 3.5 Flash', context_window=264000, price_comments='Copilot does not charge for cache writes on the Gemini models.', prices=ModelPrice(input_mtok=Decimal('1.5'), cache_read_mtok=Decimal('0.15'), output_mtok=Decimal('9')), ), ModelInfo( id='gemini-3.6-flash', match=ClauseEquals(equals='gemini-3.6-flash'), name='Gemini 3.6 Flash', context_window=264000, price_comments='Copilot does not charge for cache writes on the Gemini models.', prices=ModelPrice( input_mtok=Decimal('0.75'), cache_read_mtok=Decimal('0.075'), output_mtok=Decimal('3.75') ), ), ModelInfo( id='gemini-3.7-flash', match=ClauseEquals(equals='gemini-3.7-flash'), name='Gemini 3.7 Flash', context_window=264000, price_comments='Copilot does not charge for cache writes on the Gemini models.', prices=ModelPrice( input_mtok=Decimal('0.75'), cache_read_mtok=Decimal('0.075'), output_mtok=Decimal('3.75') ), ), ModelInfo( id='gemini-3.8-flash', match=ClauseEquals(equals='gemini-3.8-flash'), name='Gemini 3.8 Flash', context_window=265536, price_comments='Copilot does not charge for cache writes on the Gemini models.', prices=ModelPrice( input_mtok=Decimal('0.75'), cache_read_mtok=Decimal('0.075'), output_mtok=Decimal('3.75') ), ), ModelInfo( id='gpt-3.5-turbo', match=ClauseOr(or_=[ClauseEquals(equals='gpt-3.5-turbo'), ClauseEquals(equals='gpt-3.5-turbo-0613')]), name='GPT 3.5 Turbo', context_window=16384, price_comments='Included in the subscription: the pricing page lists no rate, and a live request reported a zero token cost on 2026-09-03.', prices=ModelPrice(), ), ModelInfo( id='gpt-4.1', match=ClauseOr(or_=[ClauseEquals(equals='gpt-4.1'), ClauseEquals(equals='gpt-4.1-2025-04-14')]), name='GPT-4.1', context_window=128000, price_comments='Included in the subscription: the pricing page lists no rate, and a live request reported a zero token cost on 2026-09-03.', prices=ModelPrice(), ), ModelInfo( id='gpt-4o', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-4o'), ClauseEquals(equals='gpt-4o-2024-05-13'), ClauseEquals(equals='gpt-4o-2024-08-06'), ClauseEquals(equals='gpt-4o-2024-11-20'), ClauseEquals(equals='gpt-4-o-preview'), ] ), name='GPT-4o', context_window=128000, price_comments='Included in the subscription: the pricing page lists no rate, and a live request reported a zero token cost on 2026-09-03.', prices=ModelPrice(), ), ModelInfo( id='gpt-4o-mini', match=ClauseOr(or_=[ClauseEquals(equals='gpt-4o-mini'), ClauseEquals(equals='gpt-4o-mini-2024-07-18')]), name='GPT-4o mini', context_window=128000, price_comments='Included in the subscription: the pricing page lists no rate, and a live request reported a zero token cost on 2026-09-03.', prices=ModelPrice(), ), ModelInfo( id='gpt-5-mini', match=ClauseOr( or_=[ClauseEquals(equals='gpt-5-mini'), ClauseRegex(regex='^gpt-5-mini-\\d{4}-\\d{2}-\\d{2}$')] ), name='GPT-5 mini', context_window=264000, prices=ModelPrice( input_mtok=Decimal('0.25'), cache_read_mtok=Decimal('0.025'), output_mtok=Decimal('2') ), ), ModelInfo( id='gpt-5.3-codex', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-5.3-codex'), ClauseRegex(regex='^gpt-5\\.3-codex-\\d{4}-\\d{2}-\\d{2}$'), ] ), name='GPT-5.3-Codex', context_window=400000, prices=ModelPrice( input_mtok=Decimal('1.75'), cache_read_mtok=Decimal('0.175'), output_mtok=Decimal('14') ), ), ModelInfo( id='gpt-5.4', match=ClauseOr( or_=[ClauseEquals(equals='gpt-5.4'), ClauseRegex(regex='^gpt-5\\.4-\\d{4}-\\d{2}-\\d{2}$')] ), name='GPT-5.4', context_window=400000, price_comments='Prompts over 272,000 tokens are billed at the long-context rates.', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('2.5'), tiers=[Tier(start=272000, price=Decimal('5'))]), cache_read_mtok=TieredPrices( base=Decimal('0.25'), tiers=[Tier(start=272000, price=Decimal('0.5'))] ), output_mtok=TieredPrices(base=Decimal('15'), tiers=[Tier(start=272000, price=Decimal('22.5'))]), ), ), ModelInfo( id='gpt-5.4-mini', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-5.4-mini'), ClauseRegex(regex='^gpt-5\\.4-mini-\\d{4}-\\d{2}-\\d{2}$'), ] ), name='GPT-5.4 mini', context_window=400000, prices=ModelPrice( input_mtok=Decimal('0.75'), cache_read_mtok=Decimal('0.075'), output_mtok=Decimal('4.5') ), ), ModelInfo( id='gpt-5.5', match=ClauseOr( or_=[ClauseEquals(equals='gpt-5.5'), ClauseRegex(regex='^gpt-5\\.5-\\d{4}-\\d{2}-\\d{2}$')] ), name='GPT-5.5', context_window=400000, price_comments='Prompts over 272,000 tokens are billed at the long-context rates.', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('5'), tiers=[Tier(start=272000, price=Decimal('10'))]), cache_read_mtok=TieredPrices(base=Decimal('0.5'), tiers=[Tier(start=272000, price=Decimal('1'))]), output_mtok=TieredPrices(base=Decimal('30'), tiers=[Tier(start=272000, price=Decimal('45'))]), ), ), ModelInfo( id='gpt-5.6-luna', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-5.6-luna'), ClauseRegex(regex='^gpt-5\\.6-luna-\\d{4}-\\d{2}-\\d{2}$'), ] ), name='GPT-5.6 Luna', context_window=328000, price_comments='Prompts over 200,000 tokens are billed at the long-context rates.', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('0.2'), tiers=[Tier(start=200000, price=Decimal('0.4'))]), cache_write_mtok=TieredPrices( base=Decimal('0.25'), tiers=[Tier(start=200000, price=Decimal('0.5'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.02'), tiers=[Tier(start=200000, price=Decimal('0.04'))] ), output_mtok=TieredPrices(base=Decimal('1.2'), tiers=[Tier(start=200000, price=Decimal('1.8'))]), ), ), ModelInfo( id='gpt-5.6-sol', match=ClauseOr( or_=[ClauseEquals(equals='gpt-5.6-sol'), ClauseRegex(regex='^gpt-5\\.6-sol-\\d{4}-\\d{2}-\\d{2}$')] ), name='GPT-5.6 Sol', context_window=400000, price_comments='Prompts over 272,000 tokens are billed at the long-context rates.', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('4'), tiers=[Tier(start=272000, price=Decimal('8'))]), cache_write_mtok=TieredPrices(base=Decimal('5'), tiers=[Tier(start=272000, price=Decimal('10'))]), cache_read_mtok=TieredPrices(base=Decimal('0.4'), tiers=[Tier(start=272000, price=Decimal('0.8'))]), output_mtok=TieredPrices(base=Decimal('20'), tiers=[Tier(start=272000, price=Decimal('30'))]), ), ), ModelInfo( id='gpt-5.6-terra', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-5.6-terra'), ClauseRegex(regex='^gpt-5\\.6-terra-\\d{4}-\\d{2}-\\d{2}$'), ] ), name='GPT-5.6 Terra', context_window=400000, price_comments='Prompts over 272,000 tokens are billed at the long-context rates.', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('2'), tiers=[Tier(start=272000, price=Decimal('4'))]), cache_write_mtok=TieredPrices(base=Decimal('2.5'), tiers=[Tier(start=272000, price=Decimal('5'))]), cache_read_mtok=TieredPrices(base=Decimal('0.2'), tiers=[Tier(start=272000, price=Decimal('0.4'))]), output_mtok=TieredPrices(base=Decimal('12'), tiers=[Tier(start=272000, price=Decimal('18'))]), ), ), ModelInfo( id='grok-4.5', match=ClauseEquals(equals='grok-4.5'), name='Grok 4.5', context_window=328000, price_comments='Prompts over 200,000 tokens are billed at the long-context rates.', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('2'), tiers=[Tier(start=200000, price=Decimal('4'))]), cache_read_mtok=TieredPrices(base=Decimal('0.5'), tiers=[Tier(start=200000, price=Decimal('1'))]), output_mtok=TieredPrices(base=Decimal('6'), tiers=[Tier(start=200000, price=Decimal('12'))]), ), ), ModelInfo( id='grok-4.6', match=ClauseEquals(equals='grok-4.6'), name='Grok 4.6', context_window=328000, price_comments='Prompts over 200,000 tokens are billed at the long-context rates.', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('2'), tiers=[Tier(start=200000, price=Decimal('4'))]), cache_read_mtok=TieredPrices(base=Decimal('0.5'), tiers=[Tier(start=200000, price=Decimal('1'))]), output_mtok=TieredPrices(base=Decimal('6'), tiers=[Tier(start=200000, price=Decimal('12'))]), ), ), ModelInfo( id='kimi-k2.7-code', match=ClauseEquals(equals='kimi-k2.7-code'), name='Kimi K2.7 Code', context_window=256000, prices=ModelPrice( input_mtok=Decimal('0.95'), cache_read_mtok=Decimal('0.19'), output_mtok=Decimal('4') ), ), ModelInfo( id='kimi-k3', match=ClauseEquals(equals='kimi-k3'), name='Kimi K3', context_window=1048576, prices=ModelPrice(input_mtok=Decimal('3'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15')), ), ModelInfo( id='mai-code-1-flash-picker', match=ClauseEquals(equals='mai-code-1-flash-picker'), name='MAI-Code-1-Flash', context_window=256000, price_comments='The catalog id carries a `-picker` suffix; the pricing page names it MAI-Code-1-Flash.', prices=ModelPrice( input_mtok=Decimal('0.75'), cache_read_mtok=Decimal('0.075'), output_mtok=Decimal('4.5') ), ), ModelInfo( id='mai-code-1.1-flash', match=ClauseEquals(equals='mai-code-1.1-flash'), name='MAI-Code-1.1-Flash', context_window=256000, prices=ModelPrice( input_mtok=Decimal('0.2'), cache_read_mtok=Decimal('0.02'), output_mtok=Decimal('1.2') ), ), ], ), Provider( id='google', name='Google', api_pattern='https://(.*\\.)?googleapis\\.com', pricing_urls=[ 'https://ai.google.dev/gemini-api/docs/pricing', 'https://cloud.google.com/vertex-ai/generative-ai/pricing', ], model_match=ClauseContains(contains='gemini'), provider_match=ClauseOr( or_=[ ClauseContains(contains='google'), ClauseContains(contains='vertex'), ClauseContains(contains='gemini'), ] ), extractors=[ UsageExtractor( root='usageMetadata', mappings=[ UsageExtractorMapping(path='promptTokenCount', dest='input_tokens', required=False), UsageExtractorMapping(path='cachedContentTokenCount', dest='cache_read_tokens', required=False), UsageExtractorMapping( path=[ 'cacheTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='TEXT')), 'tokenCount', ], dest='cache_text_read_tokens', required=False, ), UsageExtractorMapping( path=[ 'cacheTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='AUDIO')), 'tokenCount', ], dest='cache_audio_read_tokens', required=False, ), UsageExtractorMapping( path=[ 'cacheTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='IMAGE')), 'tokenCount', ], dest='cache_image_read_tokens', required=False, ), UsageExtractorMapping( path=[ 'cacheTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='DOCUMENT')), 'tokenCount', ], dest='cache_image_read_tokens', required=False, ), UsageExtractorMapping( path=[ 'cacheTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='VIDEO')), 'tokenCount', ], dest='cache_video_read_tokens', required=False, ), UsageExtractorMapping( path=[ 'promptTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='TEXT')), 'tokenCount', ], dest='input_text_tokens', required=False, ), UsageExtractorMapping( path=[ 'promptTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='AUDIO')), 'tokenCount', ], dest='input_audio_tokens', required=False, ), UsageExtractorMapping( path=[ 'promptTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='IMAGE')), 'tokenCount', ], dest='input_image_tokens', required=False, ), UsageExtractorMapping( path=[ 'promptTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='DOCUMENT')), 'tokenCount', ], dest='input_image_tokens', required=False, ), UsageExtractorMapping( path=[ 'promptTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='VIDEO')), 'tokenCount', ], dest='input_video_tokens', required=False, ), UsageExtractorMapping( path=[ 'candidatesTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='TEXT')), 'tokenCount', ], dest='output_text_tokens', required=False, ), UsageExtractorMapping( path=[ 'candidatesTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='AUDIO')), 'tokenCount', ], dest='output_audio_tokens', required=False, ), UsageExtractorMapping( path=[ 'candidatesTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='IMAGE')), 'tokenCount', ], dest='output_image_tokens', required=False, ), UsageExtractorMapping( path=[ 'candidatesTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='DOCUMENT')), 'tokenCount', ], dest='output_image_tokens', required=False, ), UsageExtractorMapping( path=[ 'candidatesTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='VIDEO')), 'tokenCount', ], dest='output_video_tokens', required=False, ), UsageExtractorMapping(path='candidatesTokenCount', dest='output_tokens', required=False), UsageExtractorMapping(path='thoughtsTokenCount', dest='output_tokens', required=False), UsageExtractorMapping(path='thoughtsTokenCount', dest='output_reasoning_tokens', required=False), UsageExtractorMapping(path='toolUsePromptTokenCount', dest='input_tokens', required=False), UsageExtractorMapping(path='toolUsePromptTokenCount', dest='input_tool_tokens', required=False), UsageExtractorMapping( path=[ 'toolUsePromptTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='TEXT')), 'tokenCount', ], dest='input_text_tokens', required=False, ), UsageExtractorMapping( path=[ 'toolUsePromptTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='TEXT')), 'tokenCount', ], dest='input_text_tool_tokens', required=False, ), UsageExtractorMapping( path=[ 'toolUsePromptTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='AUDIO')), 'tokenCount', ], dest='input_audio_tokens', required=False, ), UsageExtractorMapping( path=[ 'toolUsePromptTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='AUDIO')), 'tokenCount', ], dest='input_audio_tool_tokens', required=False, ), UsageExtractorMapping( path=[ 'toolUsePromptTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='IMAGE')), 'tokenCount', ], dest='input_image_tokens', required=False, ), UsageExtractorMapping( path=[ 'toolUsePromptTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='IMAGE')), 'tokenCount', ], dest='input_image_tool_tokens', required=False, ), UsageExtractorMapping( path=[ 'toolUsePromptTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='DOCUMENT')), 'tokenCount', ], dest='input_image_tokens', required=False, ), UsageExtractorMapping( path=[ 'toolUsePromptTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='DOCUMENT')), 'tokenCount', ], dest='input_image_tool_tokens', required=False, ), UsageExtractorMapping( path=[ 'toolUsePromptTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='VIDEO')), 'tokenCount', ], dest='input_video_tokens', required=False, ), UsageExtractorMapping( path=[ 'toolUsePromptTokensDetails', ArrayMatch(type='array-match', field='modality', match=ClauseEquals(equals='VIDEO')), 'tokenCount', ], dest='input_video_tool_tokens', required=False, ), ], api_flavor='default', model_path='modelVersion', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=True), UsageExtractorMapping(path='cache_creation_input_tokens', dest='input_tokens', required=False), UsageExtractorMapping(path='cache_read_input_tokens', dest='input_tokens', required=False), UsageExtractorMapping( path='cache_creation_input_tokens', dest='cache_write_tokens', required=False ), UsageExtractorMapping(path='cache_read_input_tokens', dest='cache_read_tokens', required=False), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=True), ], api_flavor='anthropic', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), ], fallback_model_providers=['anthropic'], models=[ ModelInfo( id='claude-3-5-haiku', match=ClauseContains(contains='claude-3-5-haiku'), context_window=200000, prices=ModelPrice( input_mtok=Decimal('0.8'), cache_write_mtok=Decimal('1'), cache_read_mtok=Decimal('0.08'), output_mtok=Decimal('4'), ), ), ModelInfo( id='claude-3-5-sonnet', match=ClauseContains(contains='claude-3-5-sonnet'), context_window=200000, prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), ), ), ModelInfo( id='claude-3-7-sonnet', match=ClauseContains(contains='claude-3-7-sonnet'), context_window=200000, prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), ), ), ModelInfo( id='claude-3-haiku', match=ClauseContains(contains='claude-3-haiku'), context_window=200000, prices=ModelPrice( input_mtok=Decimal('0.25'), cache_write_mtok=Decimal('0.3'), cache_read_mtok=Decimal('0.03'), output_mtok=Decimal('1.25'), ), ), ModelInfo( id='claude-3-opus', match=ClauseContains(contains='claude-3-opus'), context_window=200000, prices=ModelPrice( input_mtok=Decimal('15'), cache_write_mtok=Decimal('18.75'), cache_read_mtok=Decimal('1.5'), output_mtok=Decimal('75'), ), ), ModelInfo( id='claude-4-opus', match=ClauseOr( or_=[ ClauseContains(contains='claude-4-opus'), ClauseContains(contains='claude-opus-4@'), ClauseContains(contains='claude-opus-4-0'), ClauseContains(contains='claude-opus-4-1'), ClauseEquals(equals='claude-opus-4'), ] ), context_window=200000, prices=ModelPrice( input_mtok=Decimal('15'), cache_write_mtok=Decimal('18.75'), cache_read_mtok=Decimal('1.5'), output_mtok=Decimal('75'), ), ), ModelInfo( id='claude-4-sonnet', match=ClauseOr( or_=[ ClauseContains(contains='claude-4-sonnet'), ClauseContains(contains='claude-sonnet-4-0'), ClauseContains(contains='claude-sonnet-4@'), ClauseContains(contains='claude-sonnet-4-2025'), ClauseEquals(equals='claude-sonnet-4'), ClauseEquals(equals='anthropic/claude-sonnet-4'), ] ), context_window=200000, prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), ), ), ModelInfo( id='claude-fable-5', match=ClauseOr( or_=[ClauseEndsWith(ends_with='claude-fable-5'), ClauseContains(contains='claude-fable-5@')] ), context_window=1000000, price_comments='Flat pricing across full 1M context window. Ref: https://cloud.google.com/vertex-ai/generative-ai/pricing#claude-models', prices=ModelPrice( input_mtok=Decimal('10'), cache_write_mtok=Decimal('12.5'), cache_read_mtok=Decimal('1'), output_mtok=Decimal('50'), ), ), ModelInfo( id='claude-fable-5-1', match=ClauseOr( or_=[ClauseEndsWith(ends_with='claude-fable-5-1'), ClauseContains(contains='claude-fable-5-1@')] ), context_window=1000000, price_comments='Flat pricing across full 1M context window. Global endpoint; regional and multi-region endpoints add a 10% premium. Cache hits are 0.025x base input (not the usual 0.1x), unique to Fable 5.1 and Mythos 5.1. Ref: https://cloud.google.com/vertex-ai/generative-ai/pricing#claude-models Model ID ref: https://platform.claude.com/docs/en/build-with-claude/claude-on-vertex-ai', prices=ModelPrice( input_mtok=Decimal('10'), cache_write_mtok=Decimal('12.5'), cache_read_mtok=Decimal('0.25'), output_mtok=Decimal('50'), ), ), ModelInfo( id='claude-opus-4-6', match=ClauseOr( or_=[ ClauseContains(contains='claude-4-6-opus'), ClauseContains(contains='claude-opus-4-6'), ClauseContains(contains='claude-4.6-opus'), ClauseContains(contains='claude-opus-4.6'), ] ), context_window=1000000, prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('5'), tiers=[Tier(start=200000, price=Decimal('10'))]), cache_write_mtok=TieredPrices( base=Decimal('6.25'), tiers=[Tier(start=200000, price=Decimal('12.5'))] ), cache_read_mtok=TieredPrices(base=Decimal('0.5'), tiers=[Tier(start=200000, price=Decimal('1'))]), output_mtok=TieredPrices(base=Decimal('25'), tiers=[Tier(start=200000, price=Decimal('37.5'))]), ), ), ModelInfo( id='claude-opus-4-7', match=ClauseOr( or_=[ ClauseContains(contains='claude-4-7-opus'), ClauseContains(contains='claude-opus-4-7'), ClauseContains(contains='claude-4.7-opus'), ClauseContains(contains='claude-opus-4.7'), ] ), context_window=1000000, price_comments='Flat pricing across full 1M context window. Ref: https://cloud.google.com/vertex-ai/generative-ai/pricing#claude-models', prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), ), ), ModelInfo( id='claude-opus-4-8', match=ClauseOr( or_=[ ClauseContains(contains='claude-4-8-opus'), ClauseContains(contains='claude-opus-4-8'), ClauseContains(contains='claude-4.8-opus'), ClauseContains(contains='claude-opus-4.8'), ] ), context_window=1000000, price_comments='Flat pricing across full 1M context window. Ref: https://cloud.google.com/vertex-ai/generative-ai/pricing#claude-models', prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), ), ), ModelInfo( id='claude-opus-5', match=ClauseOr( or_=[ ClauseContains(contains='claude-5-opus'), ClauseEndsWith(ends_with='claude-opus-5'), ClauseContains(contains='claude-opus-5@'), ClauseContains(contains='claude-5.0-opus'), ClauseContains(contains='claude-opus-5.0'), ] ), context_window=1000000, price_comments='Global endpoint pricing, flat across the full 1M context window. Multi-region and regional endpoints carry a 10% premium. Ref: https://cloud.google.com/vertex-ai/generative-ai/pricing#claude-models', prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), ), ), ModelInfo( id='claude-opus-5-5', match=ClauseOr( or_=[ ClauseContains(contains='claude-5-5-opus'), ClauseEndsWith(ends_with='claude-opus-5-5'), ClauseContains(contains='claude-opus-5-5@'), ClauseContains(contains='claude-5.5-opus'), ClauseContains(contains='claude-opus-5.5'), ] ), context_window=1000000, price_comments="Global endpoint pricing, flat across the full 1M context window. Multi-region and regional endpoints carry a 10% premium. Google's pricing page renders client-side and could not be read when this entry was added; the rates follow Anthropic's list price, as every other Claude entry in this file does. Cache hits are 0.05x base input (not the usual 0.1x), unique to Opus 5.5. Ref: https://cloud.google.com/vertex-ai/generative-ai/pricing#claude-models Anthropic ref: https://platform.claude.com/docs/en/about-claude/pricing#model-pricing Model ID ref: https://platform.claude.com/docs/en/models/opus-5-5/overview", prices=ModelPrice( input_mtok=Decimal('4'), cache_write_mtok=Decimal('5'), cache_read_mtok=Decimal('0.2'), output_mtok=Decimal('20'), ), ), ModelInfo( id='claude-sonnet-4-5', match=ClauseOr( or_=[ClauseContains(contains='claude-sonnet-4-5'), ClauseContains(contains='claude-sonnet-4.5')] ), context_window=200000, price_comments='Long-context rates apply above 200K input tokens. Ref: https://cloud.google.com/vertex-ai/generative-ai/pricing#partner-models', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('3'), tiers=[Tier(start=200000, price=Decimal('6'))]), cache_write_mtok=TieredPrices( base=Decimal('3.75'), tiers=[Tier(start=200000, price=Decimal('7.5'))] ), cache_read_mtok=TieredPrices(base=Decimal('0.3'), tiers=[Tier(start=200000, price=Decimal('0.6'))]), output_mtok=TieredPrices(base=Decimal('15'), tiers=[Tier(start=200000, price=Decimal('22.5'))]), ), ), ModelInfo( id='claude-sonnet-4-6', match=ClauseOr( or_=[ClauseContains(contains='claude-sonnet-4-6'), ClauseContains(contains='claude-sonnet-4.6')] ), context_window=1000000, price_comments='Flat pricing across the full 1M context window. Ref: https://cloud.google.com/vertex-ai/generative-ai/pricing#partner-models', prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), ), ), ModelInfo( id='gemini-1.0-pro-vision-001', match=ClauseEquals(equals='gemini-1.0-pro-vision-001'), name='gemini 1.0 pro vision', description="Google's first-generation advanced multimodal model that can understand text, code, and images. It provides strong reasoning capabilities and follows instructions effectively.", context_window=32768, price_comments="I can't find anything about this model or it's pricing, so trusting the original source", prices=ModelPrice(input_mtok=Decimal('0.125'), output_mtok=Decimal('0.375')), ), ModelInfo( id='gemini-1.5-flash', match=ClauseContains(contains='gemini-1.5-flash'), name='gemini 1.5 flash', description='A faster, more cost-effective variant of Gemini 1.5 that maintains strong capabilities while optimizing for performance and cost efficiency. Suitable for production deployments requiring high throughput.', context_window=1000000, prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('0.075'), tiers=[Tier(start=128000, price=Decimal('0.15'))]), cache_read_mtok=TieredPrices( base=Decimal('0.01875'), tiers=[Tier(start=128000, price=Decimal('0.0375'))] ), output_mtok=TieredPrices(base=Decimal('0.3'), tiers=[Tier(start=128000, price=Decimal('0.6'))]), ), ), ModelInfo( id='gemini-1.5-pro', match=ClauseContains(contains='gemini-1.5-pro'), name='gemini 1.5 Pro', description="Google's most capable multimodal model with an extremely long context window of up to 1 million tokens. It excels at complex reasoning, long-form content processing, and multimodal understanding.", context_window=1000000, prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('1.25'), tiers=[Tier(start=128000, price=Decimal('2.5'))]), output_mtok=TieredPrices(base=Decimal('5'), tiers=[Tier(start=128000, price=Decimal('10'))]), ), ), ModelInfo( id='gemini-2.0-flash', match=ClauseOr( or_=[ ClauseEndsWith(ends_with='gemini-2.0-flash'), ClauseContains(contains='gemini-2.0-flash-0'), ClauseContains(contains='gemini-2.0-flash-exp'), ClauseContains(contains='gemini-2.0-flash-thinking'), ClauseContains(contains='gemini-2.0-flash-latest'), ] ), name='gemini 2.0 flash', description="The newest generation of Google's Gemini models, featuring improved reasoning, instruction following, and factual accuracy, with the Flash variant optimized for cost-efficiency and performance.", context_window=1000000, prices=ModelPrice( input_mtok=Decimal('0.1'), cache_read_mtok=Decimal('0.025'), output_mtok=Decimal('0.4'), input_audio_mtok=Decimal('0.7'), cache_audio_read_mtok=Decimal('0.175'), ), ), ModelInfo( id='gemini-2.0-flash-lite', match=ClauseContains(contains='gemini-2.0-flash-lite'), name='gemini 2.0 flash lite', description='A lighter, more cost-effective version of Gemini 2.0 Flash, designed for applications requiring high efficiency while maintaining good performance. Ideal for high-volume, cost-sensitive deployments.', context_window=1000000, prices=ModelPrice(input_mtok=Decimal('0.075'), output_mtok=Decimal('0.3')), ), ModelInfo( id='gemini-2.5-flash', match=ClauseOr( or_=[ ClauseEquals(equals='gemini-2.5-flash'), ClauseEquals(equals='gemini-2.5-flash-latest'), ClauseEquals(equals='gemini-2.5-flash-preview-09-2025'), ] ), name='Gemini 2.5 Flash', description='Gemini 2.5 Flash is Google\'s state-of-the-art workhorse model, specifically designed for advanced reasoning, coding, mathematics, and scientific tasks. It includes built-in "thinking" capabilities, enabling it to provide responses with greater accuracy and nuanced context handling.', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('0.3'), cache_read_mtok=Decimal('0.03'), output_mtok=Decimal('2.5'), input_audio_mtok=Decimal('1'), cache_audio_read_mtok=Decimal('0.1'), ), ), ModelInfo( id='gemini-2.5-flash-image', match=ClauseOr( or_=[ ClauseEquals(equals='gemini-2.5-flash-image'), ClauseEquals(equals='gemini-2.5-flash-image-preview'), ] ), name='Gemini 2.5 Flash Image', description="Google's specialized image generation model optimized for fast, high-quality image generation. Outputs images at 1024x1024 resolution, with each image consuming 1290 output tokens.", context_window=1000000, price_comments='See https://ai.google.dev/gemini-api/docs/pricing#gemini-2.5-flash-image. Image output is priced at $30 per 1M tokens, with each 1024x1024 image = 1290 tokens = $0.039/image. Cache pricing is not available for this model. Text output uses the Gemini 2.5 Flash $2.50 rate and is the aggregate remainder when modality details omit text.', prices=ModelPrice( input_mtok=Decimal('0.3'), output_mtok=Decimal('2.5'), output_image_mtok=Decimal('30') ), ), ModelInfo( id='gemini-2.5-flash-lite', match=ClauseOr( or_=[ ClauseEquals(equals='gemini-2.5-flash-lite'), ClauseAnd( and_=[ ClauseStartsWith(starts_with='gemini-2.5-flash-lite-preview'), ClauseRegex(regex='^(?!.*-[tT][tT][sS](?:$|-))'), ] ), ] ), name='Gemini 2.5 Flash Lite', description='Gemini 2.5 Flash-Lite is a lightweight reasoning model in the Gemini 2.5 family, optimized for ultra-low latency and cost efficiency. It offers improved throughput, faster token generation, and better performance across common benchmarks compared to earlier Flash models. By default, "thinking" (i.e. multi-pass reasoning) is disabled to prioritize speed, but developers can enable it via the Reasoning API parameter to selectively trade off cost for intelligence.', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('0.1'), cache_read_mtok=Decimal('0.01'), output_mtok=Decimal('0.4'), input_audio_mtok=Decimal('0.3'), cache_audio_read_mtok=Decimal('0.03'), ), ), ModelInfo( id='gemini-2.5-flash-lite-preview-tts', match=ClauseEquals(equals='gemini-2.5-flash-lite-preview-tts'), name='Gemini 2.5 Flash-Lite Preview TTS', context_window=8192, price_comments='See https://cloud.google.com/text-to-speech/pricing#gemini-tts.', prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('10')), ), ModelInfo( id='gemini-2.5-flash-preview', match=ClauseOr( or_=[ ClauseContains(contains='gemini-2.5-flash-preview-05-20'), ClauseContains(contains='gemini-2.5-flash-preview-04-17'), ClauseEquals(equals='gemini-2.5-flash-preview-05-20:thinking'), ClauseEquals(equals='gemini-2.5-flash-preview'), ClauseEquals(equals='gemini-2.5-flash-preview:thinking'), ] ), name='Gemini 2.5 Flash Preview 05-20', description='Gemini 2.5 Flash May 20th Checkpoint is Google\'s state-of-the-art workhorse model, specifically designed for advanced reasoning, coding, mathematics, and scientific tasks. It includes built-in "thinking" capabilities, enabling it to provide responses with greater accuracy and nuanced context handling.', price_comments='from https://cloud.google.com/vertex-ai/generative-ai/pricing should be retired 2025-07-15', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.6')), ), ModelInfo( id='gemini-2.5-flash-tts', match=ClauseOr( or_=[ ClauseEquals(equals='gemini-2.5-flash-tts'), ClauseEquals(equals='gemini-2.5-flash-preview-tts'), ] ), name='Gemini 2.5 Flash TTS', context_window=8192, price_comments='See https://cloud.google.com/text-to-speech/pricing#gemini-tts.', prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('10')), ), ModelInfo( id='gemini-2.5-pro', match=ClauseAnd( and_=[ ClauseStartsWith(starts_with='gemini-2.5-pro'), ClauseRegex(regex='^(?!.*-[tT][tT][sS](?:$|-))'), ] ), name='Gemini 2.5 Pro', description='Gemini 2.5 Pro is Google\'s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs "thinking" capabilities, enabling it to reason through responses with enhanced accuracy and nuanced context handling. Gemini 2.5 Pro achieves top-tier performance on multiple benchmarks, including first-place positioning on the LMArena leaderboard, reflecting superior human-preference alignment and complex problem-solving abilities.', price_comments='See https://ai.google.dev/gemini-api/docs/pricing#gemini-2.5-pro', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('1.25'), tiers=[Tier(start=200000, price=Decimal('2.5'))]), cache_read_mtok=TieredPrices( base=Decimal('0.125'), tiers=[Tier(start=200000, price=Decimal('0.25'))] ), output_mtok=TieredPrices(base=Decimal('10'), tiers=[Tier(start=200000, price=Decimal('15'))]), ), ), ModelInfo( id='gemini-2.5-pro-tts', match=ClauseOr( or_=[ClauseEquals(equals='gemini-2.5-pro-tts'), ClauseEquals(equals='gemini-2.5-pro-preview-tts')] ), name='Gemini 2.5 Pro TTS', context_window=8192, price_comments='See https://cloud.google.com/text-to-speech/pricing#gemini-tts.', prices=ModelPrice(input_mtok=Decimal('1'), output_mtok=Decimal('20')), ), ModelInfo( id='gemini-3-flash-preview', match=ClauseOr( or_=[ ClauseEquals(equals='gemini-3-flash-preview'), ClauseStartsWith(starts_with='gemini-3-flash-preview-'), ] ), name='Gemini 3 Flash Preview', description="Google's ultra-fast frontier model optimized for speed and efficiency. Delivers state-of-the-art performance while maintaining low latency and cost, with improved reasoning and coding capabilities.", context_window=1000000, price_comments='See https://ai.google.dev/gemini-api/docs/pricing. Standard pricing shown; Batch API offers 50% discount on input/output.', prices=ModelPrice( input_mtok=Decimal('0.5'), cache_read_mtok=Decimal('0.05'), output_mtok=Decimal('3'), input_audio_mtok=Decimal('1'), cache_audio_read_mtok=Decimal('0.1'), ), ), ModelInfo( id='gemini-3-pro-image-preview', match=ClauseOr( or_=[ ClauseEquals(equals='gemini-3-pro-image'), ClauseStartsWith(starts_with='gemini-3-pro-image-preview'), ] ), name='Gemini 3 Pro Image Preview', description="Google's image generation model optimized for high-quality image generation. Supports 1K/2K and 4K resolution outputs with flexible pricing based on image dimensions.", context_window=1000000, price_comments='See https://ai.google.dev/gemini-api/docs/pricing#gemini-3-pro-image. Image output is priced at $120 per 1M tokens, with each 1K/2K image = 1120 tokens = $0.134/image and each 4K image = 2000 tokens = $0.24/image. Text and thinking output is $12 per 1M tokens and is the aggregate remainder when modality details omit text.', prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('12'), output_image_mtok=Decimal('120')), ), ModelInfo( id='gemini-3-pro-preview', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='gemini-3-pro-preview'), ClauseEquals(equals='gemini-3-pro-text-preview'), ] ), name='Gemini 3 Pro Preview', description='The best model in the world for multimodal understanding, and our most powerful agentic and vibe-coding model yet.', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('2'), tiers=[Tier(start=200000, price=Decimal('4'))]), cache_read_mtok=TieredPrices(base=Decimal('0.2'), tiers=[Tier(start=200000, price=Decimal('0.4'))]), output_mtok=TieredPrices(base=Decimal('12'), tiers=[Tier(start=200000, price=Decimal('18'))]), ), ), ModelInfo( id='gemini-3.1-flash-image-preview', match=ClauseOr( or_=[ ClauseEquals(equals='gemini-3.1-flash-image'), ClauseStartsWith(starts_with='gemini-3.1-flash-image-preview'), ] ), name='Gemini 3.1 Flash Image Preview', description="Google's latest image generation model (Nano Banana 2) optimized for fast, high-quality image generation. Supports multiple output resolutions from 512px to 4K, with text and thinking output priced separately from image output tokens.", context_window=1000000, price_comments='See https://ai.google.dev/gemini-api/docs/pricing. Text and thinking output is priced at $3 per 1M tokens and is the aggregate remainder when modality details omit text. Image output is priced at $60 per 1M tokens. Preview model - pricing may change.', prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('3'), output_image_mtok=Decimal('60')), ), ModelInfo( id='gemini-3.1-flash-lite', match=ClauseOr( or_=[ ClauseEquals(equals='gemini-3.1-flash-lite'), ClauseStartsWith(starts_with='gemini-3.1-flash-lite-preview'), ] ), name='Gemini 3.1 Flash Lite', description="Google's fastest and most cost-efficient Gemini 3 series model, built for intelligence at scale. Optimized for high-volume, low-latency applications while maintaining strong multimodal capabilities.", context_window=1000000, price_comments='See https://ai.google.dev/gemini-api/docs/pricing.', prices=ModelPrice( input_mtok=Decimal('0.25'), cache_read_mtok=Decimal('0.025'), output_mtok=Decimal('1.5'), input_audio_mtok=Decimal('0.5'), cache_audio_read_mtok=Decimal('0.05'), ), ), ModelInfo( id='gemini-3.1-flash-lite-image', match=ClauseStartsWith(starts_with='gemini-3.1-flash-lite-image'), name='Gemini 3.1 Flash Lite Image', description="Google's low-latency, cost-efficient image generation and editing model.", price_comments='See https://ai.google.dev/gemini-api/docs/pricing#gemini-3.1-flash-lite-image.', prices=ModelPrice( input_mtok=Decimal('0.25'), output_mtok=Decimal('1.5'), output_image_mtok=Decimal('30') ), ), ModelInfo( id='gemini-3.1-flash-live-preview', match=ClauseStartsWith(starts_with='gemini-3.1-flash-live-preview'), name='Gemini 3.1 Flash Live Preview', description="Google's low-latency audio-to-audio model with multimodal input support.", price_comments='See https://ai.google.dev/gemini-api/docs/pricing#gemini-3.1-flash-live-preview.', prices=ModelPrice( input_mtok=Decimal('0.75'), output_mtok=Decimal('4.5'), input_audio_mtok=Decimal('3'), output_audio_mtok=Decimal('12'), input_image_mtok=Decimal('1'), input_video_mtok=Decimal('1'), ), ), ModelInfo( id='gemini-3.1-pro-preview', match=ClauseStartsWith(starts_with='gemini-3.1-pro-preview'), name='Gemini 3.1 Pro Preview', description='The latest performance, intelligence, and usability improvements to the best model family in the world for multimodal understanding, agentic capabilities, and vibe-coding.', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('2'), tiers=[Tier(start=200000, price=Decimal('4'))]), cache_read_mtok=TieredPrices(base=Decimal('0.2'), tiers=[Tier(start=200000, price=Decimal('0.4'))]), output_mtok=TieredPrices(base=Decimal('12'), tiers=[Tier(start=200000, price=Decimal('18'))]), ), ), ModelInfo( id='gemini-3.5-flash', match=ClauseOr( or_=[ ClauseEquals(equals='gemini-3.5-flash'), ClauseStartsWith(starts_with='gemini-3.5-flash-preview'), ClauseRegex(regex='^gemini-3\\.5-flash-\\d'), ] ), name='Gemini 3.5 Flash', description="Google's most intelligent model built for speed, combining frontier intelligence with improved reasoning, coding, and multimodal understanding.", context_window=1000000, price_comments='See https://ai.google.dev/gemini-api/docs/pricing. Standard tier pricing shown; Batch and Flex tiers offer 50% discount on input/output.', prices=ModelPrice(input_mtok=Decimal('1.5'), cache_read_mtok=Decimal('0.15'), output_mtok=Decimal('9')), ), ModelInfo( id='gemini-3.5-flash-lite', match=ClauseStartsWith(starts_with='gemini-3.5-flash-lite'), name='Gemini 3.5 Flash Lite', description="Google's fastest and most cost-efficient Gemini 3.5 series model, optimized for high-volume, low-latency applications while maintaining strong multimodal capabilities.", context_window=1000000, price_comments='See https://ai.google.dev/gemini-api/docs/pricing. Standard tier pricing shown; Batch and Flex tiers offer 50% discount. Input rate is unified across text/image/video/audio (no separate audio rate).', prices=ModelPrice( input_mtok=Decimal('0.3'), cache_read_mtok=Decimal('0.03'), output_mtok=Decimal('2.5') ), ), ModelInfo( id='gemini-3.6-flash', match=ClauseStartsWith(starts_with='gemini-3.6-flash'), name='Gemini 3.6 Flash', description="Google's most intelligent model built for speed, combining frontier intelligence with improved reasoning, coding, and multimodal understanding.", context_window=1000000, price_comments='See https://ai.google.dev/gemini-api/docs/pricing. Standard tier pricing shown; Batch and Flex tiers offer 50% discount. No separate audio input rate documented. Introductory rates apply through 2026-12-31. The cache storage price ($0.50 per 1M tokens per hour) has no unit in prices/units.yml and is not represented here.', prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=Decimal('0.75'), cache_read_mtok=Decimal('0.075'), output_mtok=Decimal('3.75') ) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2027, 1, 1)), prices=ModelPrice( input_mtok=Decimal('1.5'), cache_read_mtok=Decimal('0.15'), output_mtok=Decimal('7.5') ), ), ], ), ModelInfo( id='gemini-3.7-flash', match=ClauseStartsWith(starts_with='gemini-3.7-flash'), name='Gemini 3.7 Flash', description="Google's most capable Flash model, built for complex coding, web development, agentic workflows and reliable multi-step execution.", context_window=1000000, price_comments='See https://ai.google.dev/gemini-api/docs/pricing. Standard tier pricing shown; Batch and Flex tiers offer 50% discount, Priority tier is 1.8x. Introductory rates apply through 2026-12-31. The cache storage price ($0.50 per 1M tokens per hour) has no unit in prices/units.yml and is not represented here.', prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=Decimal('0.75'), cache_read_mtok=Decimal('0.075'), output_mtok=Decimal('3.75') ) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2027, 1, 1)), prices=ModelPrice( input_mtok=Decimal('1.5'), cache_read_mtok=Decimal('0.15'), output_mtok=Decimal('7.5') ), ), ], ), ModelInfo( id='gemini-3.8-flash', match=ClauseStartsWith(starts_with='gemini-3.8-flash'), name='Gemini 3.8 Flash', description="Google's most intelligent Flash model, engineered for long-horizon software engineering, autonomous agents, and complex enterprise workflows.", context_window=1000000, price_comments='See https://ai.google.dev/gemini-api/docs/pricing. Standard tier pricing shown; Batch and Flex tiers offer 50% discount, Priority tier is 1.8x. Introductory rates apply through 2026-12-31. The cache storage price ($0.50 per 1M tokens per hour) has no unit in prices/units.yml and is not represented here.', prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=Decimal('0.75'), cache_read_mtok=Decimal('0.075'), output_mtok=Decimal('3.75') ) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2027, 1, 1)), prices=ModelPrice( input_mtok=Decimal('1.5'), cache_read_mtok=Decimal('0.15'), output_mtok=Decimal('7.5') ), ), ], ), ModelInfo( id='gemini-embedding-001', match=ClauseEquals(equals='gemini-embedding-001'), prices=ModelPrice(input_mtok=Decimal('0.15')), ), ModelInfo( id='gemini-embedding-2', match=ClauseEquals(equals='gemini-embedding-2'), name='Gemini Embedding 2', description="Google's multimodal embedding model for text, images, audio, video, and documents.", context_window=8192, price_comments='See https://ai.google.dev/gemini-api/docs/pricing#gemini-embedding-2.', prices=ModelPrice( input_mtok=Decimal('0.2'), input_audio_mtok=Decimal('6.5'), input_image_mtok=Decimal('0.45'), input_video_mtok=Decimal('12'), ), ), ModelInfo( id='gemini-flash-1.5', match=ClauseEquals(equals='gemini-flash-1.5'), name='Gemini 1.5 Flash', description="Gemini 1.5 Flash is a foundation model that performs well at a variety of multimodal tasks such as visual understanding, classification, summarization, and creating content from image, audio and video. It's adept at processing visual and text inputs such as photographs, documents, infographics, and screenshots.", price_comments='See https://ai.google.dev/gemini-api/docs/pricing#gemini-1.5-flash', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('0.075'), tiers=[Tier(start=128000, price=Decimal('0.15'))]), cache_read_mtok=TieredPrices( base=Decimal('0.01875'), tiers=[Tier(start=128000, price=Decimal('0.0375'))] ), output_mtok=TieredPrices(base=Decimal('0.3'), tiers=[Tier(start=128000, price=Decimal('0.6'))]), ), ), ModelInfo( id='gemini-flash-1.5-8b', match=ClauseEquals(equals='gemini-flash-1.5-8b'), name='gemini 1.5 flash', description='A faster, more cost-effective variant of Gemini 1.5 that maintains strong capabilities while optimizing for performance and cost efficiency. Suitable for production deployments requiring high throughput.', context_window=1000000, price_comments='See https://ai.google.dev/gemini-api/docs/pricing#gemini-1.5-flash-8b', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('0.0375'), tiers=[Tier(start=128000, price=Decimal('0.075'))]), cache_read_mtok=TieredPrices( base=Decimal('0.01'), tiers=[Tier(start=128000, price=Decimal('0.02'))] ), output_mtok=TieredPrices(base=Decimal('0.15'), tiers=[Tier(start=128000, price=Decimal('0.3'))]), ), ), ModelInfo( id='gemini-live-2.5-flash', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='gemini-live-2.5-flash'), ClauseStartsWith(starts_with='gemini-2.5-flash-native-audio-preview'), ClauseEquals(equals='gemini-2.5-flash-native-audio-latest'), ] ), name='Gemini Live 2.5 Flash', description="Google's Live API model for low-latency bidirectional voice and video interactions, GA on Vertex AI (model id `gemini-live-2.5-flash`, served from the `global` location). The prefix match also covers the AI Studio preview ids (`gemini-live-2.5-flash-preview*`).", price_comments='See https://cloud.google.com/vertex-ai/generative-ai/pricing (Live API) and https://ai.google.dev/gemini-api/docs/pricing - GA pricing matches the preview.', prices=ModelPrice( input_mtok=Decimal('0.5'), output_mtok=Decimal('2'), input_audio_mtok=Decimal('3'), output_audio_mtok=Decimal('12'), ), ), ModelInfo( id='gemini-omni-flash-preview', match=ClauseStartsWith(starts_with='gemini-omni-flash-preview'), name='Gemini Omni Flash Preview', description="Google's video generation and editing model with multimodal input.", price_comments='See https://ai.google.dev/gemini-api/docs/pricing#gemini-omni-flash-preview.', prices=ModelPrice( input_mtok=Decimal('1.5'), output_mtok=Decimal('9'), output_video_mtok=Decimal('17.5') ), ), ModelInfo( id='gemini-pro', match=ClauseOr(or_=[ClauseEquals(equals='gemini-pro'), ClauseEquals(equals='gemini-1.0-pro')]), name='gemini 1.0 pro', description="Google's first-generation advanced multimodal model that can understand text, code, and images. It provides strong reasoning capabilities and follows instructions effectively.", context_window=32768, price_comments="I can't find anything so trusting these prices, not sure the model still exists", prices=ModelPrice(input_mtok=Decimal('0.125'), output_mtok=Decimal('0.375')), ), ModelInfo( id='gemini-pro-1.5', match=ClauseEquals(equals='gemini-pro-1.5'), name='Gemini 1.5 Pro', description="Google's latest multimodal model, supports image and video[0] in text or chat prompts.", context_window=2000000, price_comments='See https://ai.google.dev/gemini-api/docs/pricing#gemini-1.5-pro', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('1.25'), tiers=[Tier(start=128000, price=Decimal('2.5'))]), cache_read_mtok=TieredPrices( base=Decimal('0.3125'), tiers=[Tier(start=128000, price=Decimal('0.625'))] ), output_mtok=TieredPrices(base=Decimal('5'), tiers=[Tier(start=128000, price=Decimal('10'))]), ), ), ModelInfo( id='gemma-2-27b-it', match=ClauseEquals(equals='gemma-2-27b-it'), name='Gemma 2 27B', description='Gemma 2 27B by Google is an open model built from the same research and technology used to create the Gemini models. Gemma models are well-suited for a variety of text generation and instruction-following tasks.', price_comments='Imported from OpenRouter pricing; verify against Google pricing when native API pricing is published.', prices=ModelPrice(input_mtok=Decimal('0.65'), output_mtok=Decimal('0.65')), ), ModelInfo( id='gemma-3', match=ClauseOr(or_=[ClauseStartsWith(starts_with='gemma-3-'), ClauseEquals(equals='gemma-3')]), name='Gemma 3 (free)', description='Lightweight, state-of the art, open model built from the same technology that powers our Gemini models.', prices=ModelPrice(), ), ModelInfo( id='gemma-3n', match=ClauseOr(or_=[ClauseStartsWith(starts_with='gemma-3n')]), name='Gemma 3n (free)', description='Our open model built for efficient performance on everyday devices like mobile phones, laptops, and tablets.', prices=ModelPrice(), ), ModelInfo( id='gemma-4-26b-a4b-it', match=ClauseEquals(equals='gemma-4-26b-a4b-it'), name='Gemma 4 26B A4B', description='Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference.', context_window=262144, price_comments='Gemini API model ID. Free of charge on the Gemini API with no paid tier, see https://ai.google.dev/gemini-api/docs/pricing. The Vertex AI Model-as-a-Service offering uses the gemma-4-26b-a4b-it-maas ID and is priced separately. The previous rates were imported from OpenRouter, not Google.', prices=ModelPrice(), ), ModelInfo( id='gemma-4-26b-a4b-it-maas', match=ClauseEquals(equals='gemma-4-26b-a4b-it-maas'), name='Gemma 4 26B A4B (Vertex AI MaaS)', description='Gemma 4 26B A4B IT served by Vertex AI as a managed Model-as-a-Service endpoint.', context_window=262144, price_comments='Vertex AI Model-as-a-Service rates, see https://cloud.google.com/vertex-ai/generative-ai/pricing. Model ID from https://cloud.google.com/vertex-ai/generative-ai/docs/maas/use-open-models.', prices=ModelPrice( input_mtok=Decimal('0.15'), cache_read_mtok=Decimal('0.015'), output_mtok=Decimal('0.6') ), ), ModelInfo( id='gemma-4-31b-it', match=ClauseEquals(equals='gemma-4-31b-it'), name='Gemma 4 31B', description="Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output. It features a 256K token context window, configurable thinking/reasoning mode, and native function calling.", context_window=262144, price_comments='Free of charge on the Gemini API with no paid tier, see https://ai.google.dev/gemini-api/docs/pricing. Not offered as Model-as-a-Service on Vertex AI (only Gemma 4 26B is); there it is self-deployed from Model Garden and billed as endpoint compute, so no per-token price applies. The previous rates were imported from OpenRouter, not Google.', prices=ModelPrice(), ), ], ), Provider( id='groq', name='Groq', api_pattern='https://api\\.groq\\.com', pricing_urls=['https://console.groq.com/docs/models'], extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), ], api_flavor='default', model_path='model', ) ], models=[ ModelInfo( id='deepseek-r1-distill-llama-70b', match=ClauseEquals(equals='deepseek-r1-distill-llama-70b'), name='DeepSeek R1 Distill Llama 70B', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.75'), output_mtok=Decimal('0.99')), ), ModelInfo( id='gemma-7b-it', match=ClauseEquals(equals='gemma-7b-it'), prices=ModelPrice(input_mtok=Decimal('0.07'), output_mtok=Decimal('0.07')), ), ModelInfo( id='gemma2-9b-it', match=ClauseOr(or_=[ClauseEquals(equals='gemma2-9b-it'), ClauseEquals(equals='gemma2-9b')]), name='Gemma 2 9B 8k', prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='llama-3.1-405b-reasoning', match=ClauseEquals(equals='llama-3.1-405b-reasoning'), prices=ModelPrice(input_mtok=Decimal('0.59'), output_mtok=Decimal('0.79')), ), ModelInfo( id='llama-3.1-70b-versatile', match=ClauseEquals(equals='llama-3.1-70b-versatile'), prices=ModelPrice(input_mtok=Decimal('0.59'), output_mtok=Decimal('0.79')), ), ModelInfo( id='llama-3.1-8b-instant', match=ClauseEquals(equals='llama-3.1-8b-instant'), name='Llama 3.1 8B Instant 128k', prices=ModelPrice(input_mtok=Decimal('0.05'), output_mtok=Decimal('0.08')), ), ModelInfo( id='llama-3.2-11b-text-preview', match=ClauseEquals(equals='llama-3.2-11b-text-preview'), prices=ModelPrice(input_mtok=Decimal('0.18'), output_mtok=Decimal('0.18')), ), ModelInfo( id='llama-3.2-11b-vision-preview', match=ClauseEquals(equals='llama-3.2-11b-vision-preview'), prices=ModelPrice(input_mtok=Decimal('0.18'), output_mtok=Decimal('0.18')), ), ModelInfo( id='llama-3.2-1b-preview', match=ClauseEquals(equals='llama-3.2-1b-preview'), prices=ModelPrice(input_mtok=Decimal('0.04'), output_mtok=Decimal('0.04')), ), ModelInfo( id='llama-3.2-3b-preview', match=ClauseEquals(equals='llama-3.2-3b-preview'), prices=ModelPrice(input_mtok=Decimal('0.06'), output_mtok=Decimal('0.06')), ), ModelInfo( id='llama-3.2-90b-text-preview', match=ClauseEquals(equals='llama-3.2-90b-text-preview'), prices=ModelPrice(input_mtok=Decimal('0.9'), output_mtok=Decimal('0.9')), ), ModelInfo( id='llama-3.2-90b-vision-preview', match=ClauseEquals(equals='llama-3.2-90b-vision-preview'), prices=ModelPrice(input_mtok=Decimal('0.9'), output_mtok=Decimal('0.9')), ), ModelInfo( id='llama-3.3-70b-specdec', match=ClauseEquals(equals='llama-3.3-70b-specdec'), prices=ModelPrice(input_mtok=Decimal('0.59'), output_mtok=Decimal('0.99')), ), ModelInfo( id='llama-3.3-70b-versatile', match=ClauseEquals(equals='llama-3.3-70b-versatile'), name='Llama 3.3 70B Versatile 128k', prices=ModelPrice(input_mtok=Decimal('0.59'), output_mtok=Decimal('0.79')), ), ModelInfo( id='llama-guard-3-8b', match=ClauseEquals(equals='llama-guard-3-8b'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='llama2-70b-4096', match=ClauseEquals(equals='llama2-70b-4096'), prices=ModelPrice(input_mtok=Decimal('0.7'), output_mtok=Decimal('0.8')), ), ModelInfo( id='llama3-70b-8192', match=ClauseEquals(equals='llama3-70b-8192'), prices=ModelPrice(input_mtok=Decimal('0.59'), output_mtok=Decimal('0.79')), ), ModelInfo( id='llama3-8b-8192', match=ClauseEquals(equals='llama3-8b-8192'), prices=ModelPrice(input_mtok=Decimal('0.05'), output_mtok=Decimal('0.08')), ), ModelInfo( id='llama3-groq-70b-8192-tool-use-preview', match=ClauseEquals(equals='llama3-groq-70b-8192-tool-use-preview'), prices=ModelPrice(input_mtok=Decimal('0.89'), output_mtok=Decimal('0.89')), ), ModelInfo( id='llama3-groq-8b-8192-tool-use-preview', match=ClauseEquals(equals='llama3-groq-8b-8192-tool-use-preview'), prices=ModelPrice(input_mtok=Decimal('0.19'), output_mtok=Decimal('0.19')), ), ModelInfo( id='meta-llama/llama-4-maverick-17b-128e-instruct', match=ClauseEquals(equals='meta-llama/llama-4-maverick-17b-128e-instruct'), name='Llama 4 Maverick 17B 128E', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.6')), ), ModelInfo( id='meta-llama/llama-4-scout-17b-16e-instruct', match=ClauseEquals(equals='meta-llama/llama-4-scout-17b-16e-instruct'), name='Llama 4 Scout (17Bx16E) 128k', prices=ModelPrice(input_mtok=Decimal('0.11'), output_mtok=Decimal('0.34')), ), ModelInfo( id='meta-llama/llama-guard-4-12b', match=ClauseEquals(equals='meta-llama/llama-guard-4-12b'), name='Llama Guard 4 12B', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='mistral-saba-24b', match=ClauseEquals(equals='mistral-saba-24b'), prices=ModelPrice(input_mtok=Decimal('0.79'), output_mtok=Decimal('0.79')), ), ModelInfo( id='mixtral-8x7b-32768', match=ClauseEquals(equals='mixtral-8x7b-32768'), prices=ModelPrice(input_mtok=Decimal('0.24'), output_mtok=Decimal('0.24')), ), ModelInfo( id='moonshotai/kimi-k2-instruct', match=ClauseOr( or_=[ ClauseEquals(equals='moonshotai/kimi-k2-instruct'), ClauseEquals(equals='moonshotai/kimi-k2-instruct-0905'), ] ), name='Kimi K2 1T 128k', context_window=131072, prices=ModelPrice(input_mtok=Decimal('1'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('3')), ), ModelInfo( id='openai/gpt-oss-120b', match=ClauseOr( or_=[ ClauseEquals(equals='openai/gpt-oss-120b'), ClauseEquals(equals='openai/gpt-oss-safeguard-20b'), ] ), description="GPT-OSS 120B is OpenAI's flagship open source model, built on a Mixture-of-Experts (MoE) architecture with\n120 billion parameters and 128 experts.\n", context_window=131072, prices=ModelPrice( input_mtok=Decimal('0.15'), cache_read_mtok=Decimal('0.075'), output_mtok=Decimal('0.6') ), ), ModelInfo( id='openai/gpt-oss-20b', match=ClauseEquals(equals='openai/gpt-oss-20b'), description="GPT-OSS 20B is OpenAI's flagship open source model, built on a Mixture-of-Experts (MoE) architecture with\n20 billion parameters and 32 experts.\n", context_window=131072, prices=ModelPrice( input_mtok=Decimal('0.075'), cache_read_mtok=Decimal('0.0375'), output_mtok=Decimal('0.3') ), ), ModelInfo( id='qwen/qwen3-32b', match=ClauseEquals(equals='qwen/qwen3-32b'), name='Qwen3 32B 131k', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.29'), output_mtok=Decimal('0.59')), ), ModelInfo( id='whisper-large-v3', match=ClauseEquals(equals='whisper-large-v3'), name='Whisper Large V3', price_comments='See https://console.groq.com/docs/model/whisper-large-v3.', prices=ModelPrice(audio_hours=Decimal('0.111'), input_audio_hours=Decimal('0.111')), ), ModelInfo( id='whisper-large-v3-turbo', match=ClauseEquals(equals='whisper-large-v3-turbo'), name='Whisper Large V3 Turbo', price_comments='See https://console.groq.com/docs/model/whisper-large-v3-turbo.', prices=ModelPrice(audio_hours=Decimal('0.04'), input_audio_hours=Decimal('0.04')), ), ], ), Provider( id='huggingface_cerebras', name='HuggingFace (cerebras)', api_pattern='https://router\\.huggingface\\.co/cerebras', pricing_urls=['https://router.huggingface.co/v1/models', 'https://huggingface.co/inference/models'], provider_match=ClauseAnd(and_=[ClauseContains(contains='huggingface'), ClauseContains(contains='cerebras')]), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), ], models=[ ModelInfo( id='meta-llama/Llama-3.1-8B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='meta-llama/llama-3.1-8b-instruct'), ClauseEquals(equals='meta-llama/llama-3.1-8b-instruct-fast'), ] ), name='Llama-3.1-8B-Instruct', prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ) ], ), Provider( id='huggingface_fireworks-ai', name='HuggingFace (fireworks-ai)', api_pattern='https://router\\.huggingface\\.co/fireworks-ai', pricing_urls=['https://router.huggingface.co/v1/models', 'https://huggingface.co/inference/models'], provider_match=ClauseAnd( and_=[ClauseContains(contains='huggingface'), ClauseContains(contains='fireworks-ai')] ), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), ], models=[ ModelInfo( id='meta-llama/Llama-3.3-70B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='meta-llama/llama-3.3-70b-instruct'), ClauseEquals(equals='meta-llama/llama-3.3-70b-instruct-fast'), ] ), name='Llama-3.3-70B-Instruct', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.9'), output_mtok=Decimal('0.9')), ), ModelInfo( id='openai/gpt-oss-120b', match=ClauseOr( or_=[ClauseEquals(equals='openai/gpt-oss-120b'), ClauseEquals(equals='openai/gpt-oss-120b-fast')] ), name='gpt-oss-120b', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.6')), ), ModelInfo( id='openai/gpt-oss-20b', match=ClauseOr( or_=[ClauseEquals(equals='openai/gpt-oss-20b'), ClauseEquals(equals='openai/gpt-oss-20b-fast')] ), name='gpt-oss-20b', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.05'), output_mtok=Decimal('0.2')), ), ], ), Provider( id='huggingface_groq', name='HuggingFace (groq)', api_pattern='https://router\\.huggingface\\.co/groq', pricing_urls=['https://router.huggingface.co/v1/models', 'https://huggingface.co/inference/models'], provider_match=ClauseAnd(and_=[ClauseContains(contains='huggingface'), ClauseContains(contains='groq')]), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), ], models=[ ModelInfo( id='Qwen/Qwen3-32B', match=ClauseOr(or_=[ClauseEquals(equals='qwen/qwen3-32b'), ClauseEquals(equals='qwen/qwen3-32b-fast')]), name='Qwen3-32B', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.29'), output_mtok=Decimal('0.59')), ), ModelInfo( id='meta-llama/Llama-3.3-70B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='meta-llama/llama-3.3-70b-instruct'), ClauseEquals(equals='meta-llama/llama-3.3-70b-instruct-fast'), ] ), name='Llama-3.3-70B-Instruct', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.59'), output_mtok=Decimal('0.79')), ), ModelInfo( id='meta-llama/Llama-4-Scout-17B-16E-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='meta-llama/llama-4-scout-17b-16e-instruct'), ClauseEquals(equals='meta-llama/llama-4-scout-17b-16e-instruct-fast'), ] ), name='Llama-4-Scout-17B-16E-Instruct', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.11'), output_mtok=Decimal('0.34')), ), ModelInfo( id='openai/gpt-oss-120b', match=ClauseOr( or_=[ClauseEquals(equals='openai/gpt-oss-120b'), ClauseEquals(equals='openai/gpt-oss-120b-fast')] ), name='gpt-oss-120b', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.75')), ), ModelInfo( id='openai/gpt-oss-20b', match=ClauseOr( or_=[ClauseEquals(equals='openai/gpt-oss-20b'), ClauseEquals(equals='openai/gpt-oss-20b-fast')] ), name='gpt-oss-20b', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.5')), ), ], ), Provider( id='huggingface_hyperbolic', name='HuggingFace (hyperbolic)', api_pattern='https://router\\.huggingface\\.co/hyperbolic', pricing_urls=['https://router.huggingface.co/v1/models', 'https://huggingface.co/inference/models'], provider_match=ClauseAnd(and_=[ClauseContains(contains='huggingface'), ClauseContains(contains='hyperbolic')]), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), ], models=[ ModelInfo( id='Qwen/Qwen2.5-VL-72B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen2.5-vl-72b-instruct'), ClauseEquals(equals='qwen/qwen2.5-vl-72b-instruct-fast'), ] ), name='Qwen2.5-VL-72B-Instruct', context_window=32768, prices=ModelPrice(input_mtok=Decimal('0.6'), output_mtok=Decimal('0.6')), ), ModelInfo( id='Qwen/Qwen2.5-VL-7B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen2.5-vl-7b-instruct'), ClauseEquals(equals='qwen/qwen2.5-vl-7b-instruct-fast'), ] ), name='Qwen2.5-VL-7B-Instruct', context_window=32768, prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='Qwen/Qwen3-235B-A22B-Instruct-2507', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen3-235b-a22b-instruct-2507'), ClauseEquals(equals='qwen/qwen3-235b-a22b-instruct-2507-fast'), ] ), name='Qwen3-235B-A22B-Instruct-2507', context_window=262144, prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('2')), ), ModelInfo( id='Qwen/Qwen3-Coder-480B-A35B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen3-coder-480b-a35b-instruct'), ClauseEquals(equals='qwen/qwen3-coder-480b-a35b-instruct-fast'), ] ), name='Qwen3-Coder-480B-A35B-Instruct', context_window=262144, prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('2')), ), ModelInfo( id='Qwen/Qwen3-Next-80B-A3B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen3-next-80b-a3b-instruct'), ClauseEquals(equals='qwen/qwen3-next-80b-a3b-instruct-fast'), ] ), name='Qwen3-Next-80B-A3B-Instruct', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('0.3')), ), ModelInfo( id='Qwen/Qwen3-Next-80B-A3B-Thinking', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen3-next-80b-a3b-thinking'), ClauseEquals(equals='qwen/qwen3-next-80b-a3b-thinking-fast'), ] ), name='Qwen3-Next-80B-A3B-Thinking', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('0.3')), ), ModelInfo( id='deepseek-ai/DeepSeek-R1', match=ClauseOr( or_=[ ClauseEquals(equals='deepseek-ai/deepseek-r1'), ClauseEquals(equals='deepseek-ai/deepseek-r1-fast'), ] ), name='DeepSeek-R1', context_window=163840, prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('2')), ), ModelInfo( id='deepseek-ai/DeepSeek-R1-0528', match=ClauseOr( or_=[ ClauseEquals(equals='deepseek-ai/deepseek-r1-0528'), ClauseEquals(equals='deepseek-ai/deepseek-r1-0528-fast'), ] ), name='DeepSeek-R1-0528', context_window=163840, prices=ModelPrice(input_mtok=Decimal('3'), output_mtok=Decimal('3')), ), ModelInfo( id='deepseek-ai/DeepSeek-V3-0324', match=ClauseOr( or_=[ ClauseEquals(equals='deepseek-ai/deepseek-v3-0324'), ClauseEquals(equals='deepseek-ai/deepseek-v3-0324-fast'), ] ), name='DeepSeek-V3-0324', context_window=163840, prices=ModelPrice(input_mtok=Decimal('1.25'), output_mtok=Decimal('1.25')), ), ModelInfo( id='meta-llama/Llama-3.3-70B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='meta-llama/llama-3.3-70b-instruct'), ClauseEquals(equals='meta-llama/llama-3.3-70b-instruct-fast'), ] ), name='Llama-3.3-70B-Instruct', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.4'), output_mtok=Decimal('0.4')), ), ModelInfo( id='openai/gpt-oss-120b', match=ClauseOr( or_=[ClauseEquals(equals='openai/gpt-oss-120b'), ClauseEquals(equals='openai/gpt-oss-120b-fast')] ), name='gpt-oss-120b', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('0.3')), ), ModelInfo( id='openai/gpt-oss-20b', match=ClauseOr( or_=[ClauseEquals(equals='openai/gpt-oss-20b'), ClauseEquals(equals='openai/gpt-oss-20b-fast')] ), name='gpt-oss-20b', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ), ], ), Provider( id='huggingface_nebius', name='HuggingFace (nebius)', api_pattern='https://router\\.huggingface\\.co/nebius', pricing_urls=['https://router.huggingface.co/v1/models', 'https://huggingface.co/inference/models'], provider_match=ClauseAnd(and_=[ClauseContains(contains='huggingface'), ClauseContains(contains='nebius')]), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), ], models=[ ModelInfo( id='NousResearch/Hermes-4-405B', match=ClauseOr( or_=[ ClauseEquals(equals='nousresearch/hermes-4-405b'), ClauseEquals(equals='nousresearch/hermes-4-405b-fast'), ] ), name='Hermes-4-405B', context_window=131072, prices=ModelPrice(input_mtok=Decimal('1'), output_mtok=Decimal('3')), ), ModelInfo( id='NousResearch/Hermes-4-70B', match=ClauseOr( or_=[ ClauseEquals(equals='nousresearch/hermes-4-70b'), ClauseEquals(equals='nousresearch/hermes-4-70b-fast'), ] ), name='Hermes-4-70B', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.13'), output_mtok=Decimal('0.4')), ), ModelInfo( id='PrimeIntellect/INTELLECT-3-FP8', match=ClauseOr( or_=[ ClauseEquals(equals='primeintellect/intellect-3-fp8'), ClauseEquals(equals='primeintellect/intellect-3-fp8-fast'), ] ), name='INTELLECT-3-FP8', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('1.1')), ), ModelInfo( id='Qwen/Qwen2.5-Coder-7B', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen2.5-coder-7b'), ClauseEquals(equals='qwen/qwen2.5-coder-7b-fast'), ] ), name='Qwen2.5-Coder-7B', context_window=32768, prices=ModelPrice(input_mtok=Decimal('0.03'), output_mtok=Decimal('0.09')), ), ModelInfo( id='Qwen/Qwen2.5-VL-72B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen2.5-vl-72b-instruct'), ClauseEquals(equals='qwen/qwen2.5-vl-72b-instruct-fast'), ] ), name='Qwen2.5-VL-72B-Instruct', context_window=32000, prices=ModelPrice(input_mtok=Decimal('0.25'), output_mtok=Decimal('0.75')), ), ModelInfo( id='Qwen/Qwen3-235B-A22B-Instruct-2507', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen3-235b-a22b-instruct-2507'), ClauseEquals(equals='qwen/qwen3-235b-a22b-instruct-2507-fast'), ] ), name='Qwen3-235B-A22B-Instruct-2507', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.6')), ), ModelInfo( id='Qwen/Qwen3-235B-A22B-Thinking-2507', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen3-235b-a22b-thinking-2507'), ClauseEquals(equals='qwen/qwen3-235b-a22b-thinking-2507-fast'), ] ), name='Qwen3-235B-A22B-Thinking-2507', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.8')), ), ModelInfo( id='Qwen/Qwen3-30B-A3B-Instruct-2507', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen3-30b-a3b-instruct-2507'), ClauseEquals(equals='qwen/qwen3-30b-a3b-instruct-2507-fast'), ] ), name='Qwen3-30B-A3B-Instruct-2507', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.3')), ), ModelInfo( id='Qwen/Qwen3-30B-A3B-Thinking-2507', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen3-30b-a3b-thinking-2507'), ClauseEquals(equals='qwen/qwen3-30b-a3b-thinking-2507-fast'), ] ), name='Qwen3-30B-A3B-Thinking-2507', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.3')), ), ModelInfo( id='Qwen/Qwen3-32B', match=ClauseOr(or_=[ClauseEquals(equals='qwen/qwen3-32b'), ClauseEquals(equals='qwen/qwen3-32b-fast')]), name='Qwen3-32B', context_window=40960, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.3')), ), ModelInfo( id='Qwen/Qwen3-Coder-30B-A3B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen3-coder-30b-a3b-instruct'), ClauseEquals(equals='qwen/qwen3-coder-30b-a3b-instruct-fast'), ] ), name='Qwen3-Coder-30B-A3B-Instruct', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.3')), ), ModelInfo( id='Qwen/Qwen3-Coder-480B-A35B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen3-coder-480b-a35b-instruct'), ClauseEquals(equals='qwen/qwen3-coder-480b-a35b-instruct-fast'), ] ), name='Qwen3-Coder-480B-A35B-Instruct', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.4'), output_mtok=Decimal('1.8')), ), ModelInfo( id='deepseek-ai/DeepSeek-R1-0528', match=ClauseOr( or_=[ ClauseEquals(equals='deepseek-ai/deepseek-r1-0528'), ClauseEquals(equals='deepseek-ai/deepseek-r1-0528-fast'), ] ), name='DeepSeek-R1-0528', context_window=163840, prices=ModelPrice(input_mtok=Decimal('0.8'), output_mtok=Decimal('2.4')), ), ModelInfo( id='deepseek-ai/DeepSeek-V3-0324', match=ClauseOr( or_=[ ClauseEquals(equals='deepseek-ai/deepseek-v3-0324'), ClauseEquals(equals='deepseek-ai/deepseek-v3-0324-fast'), ] ), name='DeepSeek-V3-0324', context_window=32768, prices=ModelPrice(input_mtok=Decimal('0.75'), output_mtok=Decimal('2.25')), ), ModelInfo( id='google/gemma-2-2b-it', match=ClauseOr( or_=[ClauseEquals(equals='google/gemma-2-2b-it'), ClauseEquals(equals='google/gemma-2-2b-it-fast')] ), name='gemma-2-2b-it', context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.02'), output_mtok=Decimal('0.06')), ), ModelInfo( id='google/gemma-2-9b-it', match=ClauseOr( or_=[ClauseEquals(equals='google/gemma-2-9b-it'), ClauseEquals(equals='google/gemma-2-9b-it-fast')] ), name='gemma-2-9b-it', context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.03'), output_mtok=Decimal('0.09')), ), ModelInfo( id='google/gemma-3-27b-it', match=ClauseOr( or_=[ ClauseEquals(equals='google/gemma-3-27b-it'), ClauseEquals(equals='google/gemma-3-27b-it-fast'), ] ), name='gemma-3-27b-it', context_window=110000, prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.6')), ), ModelInfo( id='meta-llama/Llama-3.1-8B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='meta-llama/llama-3.1-8b-instruct'), ClauseEquals(equals='meta-llama/llama-3.1-8b-instruct-fast'), ] ), name='Llama-3.1-8B-Instruct', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.03'), output_mtok=Decimal('0.09')), ), ModelInfo( id='meta-llama/Llama-3.3-70B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='meta-llama/llama-3.3-70b-instruct'), ClauseEquals(equals='meta-llama/llama-3.3-70b-instruct-fast'), ] ), name='Llama-3.3-70B-Instruct', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.25'), output_mtok=Decimal('0.75')), ), ModelInfo( id='moonshotai/Kimi-K2-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='moonshotai/kimi-k2-instruct'), ClauseEquals(equals='moonshotai/kimi-k2-instruct-fast'), ] ), name='Kimi-K2-Instruct', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('2.4')), ), ModelInfo( id='moonshotai/Kimi-K2-Thinking', match=ClauseOr( or_=[ ClauseEquals(equals='moonshotai/kimi-k2-thinking'), ClauseEquals(equals='moonshotai/kimi-k2-thinking-fast'), ] ), name='Kimi-K2-Thinking', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.6'), output_mtok=Decimal('2.5')), ), ModelInfo( id='nvidia/Llama-3_1-Nemotron-Ultra-253B-v1', match=ClauseOr( or_=[ ClauseEquals(equals='nvidia/llama-3_1-nemotron-ultra-253b-v1'), ClauseEquals(equals='nvidia/llama-3_1-nemotron-ultra-253b-v1-fast'), ] ), name='Llama-3_1-Nemotron-Ultra-253B-v1', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.6'), output_mtok=Decimal('1.8')), ), ModelInfo( id='nvidia/NVIDIA-Nemotron-Nano-12B-v2', match=ClauseOr( or_=[ ClauseEquals(equals='nvidia/nvidia-nemotron-nano-12b-v2'), ClauseEquals(equals='nvidia/nvidia-nemotron-nano-12b-v2-fast'), ] ), name='NVIDIA-Nemotron-Nano-12B-v2', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.07'), output_mtok=Decimal('0.2')), ), ModelInfo( id='openai/gpt-oss-120b', match=ClauseOr( or_=[ClauseEquals(equals='openai/gpt-oss-120b'), ClauseEquals(equals='openai/gpt-oss-120b-fast')] ), name='gpt-oss-120b', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.6')), ), ModelInfo( id='zai-org/GLM-4.5', match=ClauseOr( or_=[ClauseEquals(equals='zai-org/glm-4.5'), ClauseEquals(equals='zai-org/glm-4.5-fast')] ), name='GLM-4.5', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.6'), output_mtok=Decimal('2.2')), ), ModelInfo( id='zai-org/GLM-4.5-Air', match=ClauseOr( or_=[ClauseEquals(equals='zai-org/glm-4.5-air'), ClauseEquals(equals='zai-org/glm-4.5-air-fast')] ), name='GLM-4.5-Air', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('1.2')), ), ], ), Provider( id='huggingface_novita', name='HuggingFace (novita)', api_pattern='https://router\\.huggingface\\.co/novita', pricing_urls=['https://router.huggingface.co/v1/models', 'https://huggingface.co/inference/models'], provider_match=ClauseAnd(and_=[ClauseContains(contains='huggingface'), ClauseContains(contains='novita')]), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), ], models=[ ModelInfo( id='MiniMaxAI/MiniMax-M1-80k', match=ClauseOr( or_=[ ClauseEquals(equals='minimaxai/minimax-m1-80k'), ClauseEquals(equals='minimaxai/minimax-m1-80k-fast'), ] ), name='MiniMax-M1-80k', context_window=1000000, prices=ModelPrice(input_mtok=Decimal('0.55'), output_mtok=Decimal('2.2')), ), ModelInfo( id='MiniMaxAI/MiniMax-M2', match=ClauseOr( or_=[ ClauseEquals(equals='minimaxai/minimax-m2'), ClauseEquals(equals='minimaxai/minimax-m2-fast'), ClauseEquals(equals='minimaxai/minimax-m2.1'), ClauseEquals(equals='minimaxai/minimax-m2.1-fast'), ClauseEquals(equals='minimaxai/minimax-m2.5'), ClauseEquals(equals='minimaxai/minimax-m2.5-fast'), ] ), name='MiniMax-M2', context_window=204800, prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('1.2')), ), ModelInfo( 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required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), ], models=[ ModelInfo( id='Qwen/QwQ-32B', match=ClauseOr(or_=[ClauseEquals(equals='qwen/qwq-32b'), ClauseEquals(equals='qwen/qwq-32b-fast')]), name='QwQ-32B', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.18'), output_mtok=Decimal('0.2')), ), ModelInfo( id='Qwen/Qwen2.5-Coder-32B-Instruct', match=ClauseOr( or_=[ 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path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), ], models=[ ModelInfo( id='Qwen/Qwen2.5-VL-72B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen2.5-vl-72b-instruct'), ClauseEquals(equals='qwen/qwen2.5-vl-72b-instruct-fast'), ] ), name='Qwen2.5-VL-72B-Instruct', context_window=32768, prices=ModelPrice(input_mtok=Decimal('1.01'), output_mtok=Decimal('1.01')), ), ModelInfo( id='Qwen/Qwen3-32B', match=ClauseOr(or_=[ClauseEquals(equals='qwen/qwen3-32b'), ClauseEquals(equals='qwen/qwen3-32b-fast')]), name='Qwen3-32B', context_window=32768, prices=ModelPrice(input_mtok=Decimal('0.09'), output_mtok=Decimal('0.25')), ), ModelInfo( id='Qwen/Qwen3-Coder-30B-A3B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen3-coder-30b-a3b-instruct'), ClauseEquals(equals='qwen/qwen3-coder-30b-a3b-instruct-fast'), ] ), name='Qwen3-Coder-30B-A3B-Instruct', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.07'), output_mtok=Decimal('0.26')), ), ModelInfo( id='meta-llama/Llama-3.1-8B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='meta-llama/llama-3.1-8b-instruct'), ClauseEquals(equals='meta-llama/llama-3.1-8b-instruct-fast'), ] ), name='Llama-3.1-8B-Instruct', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.11'), output_mtok=Decimal('0.11')), ), ModelInfo( id='meta-llama/Llama-3.3-70B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='meta-llama/llama-3.3-70b-instruct'), ClauseEquals(equals='meta-llama/llama-3.3-70b-instruct-fast'), ] ), name='Llama-3.3-70B-Instruct', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.74'), output_mtok=Decimal('0.74')), ), ModelInfo( id='openai/gpt-oss-120b', match=ClauseOr( or_=[ClauseEquals(equals='openai/gpt-oss-120b'), ClauseEquals(equals='openai/gpt-oss-120b-fast')] ), name='gpt-oss-120b', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.09'), output_mtok=Decimal('0.47')), ), ModelInfo( id='openai/gpt-oss-20b', match=ClauseOr( or_=[ClauseEquals(equals='openai/gpt-oss-20b'), ClauseEquals(equals='openai/gpt-oss-20b-fast')] ), name='gpt-oss-20b', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.05'), output_mtok=Decimal('0.18')), ), ], ), Provider( id='huggingface_publicai', name='HuggingFace (publicai)', api_pattern='https://router\\.huggingface\\.co/publicai', pricing_urls=['https://router.huggingface.co/v1/models', 'https://huggingface.co/inference/models'], provider_match=ClauseAnd(and_=[ClauseContains(contains='huggingface'), ClauseContains(contains='publicai')]), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), ], models=[ ModelInfo( id='aisingapore/Gemma-SEA-LION-v4-27B-IT', match=ClauseOr( or_=[ ClauseEquals(equals='aisingapore/gemma-sea-lion-v4-27b-it'), ClauseEquals(equals='aisingapore/gemma-sea-lion-v4-27b-it-fast'), ] ), name='Gemma-SEA-LION-v4-27B-IT', prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.4')), ), ModelInfo( id='aisingapore/Qwen-SEA-LION-v4-32B-IT', match=ClauseOr( or_=[ ClauseEquals(equals='aisingapore/qwen-sea-lion-v4-32b-it'), ClauseEquals(equals='aisingapore/qwen-sea-lion-v4-32b-it-fast'), ] ), name='Qwen-SEA-LION-v4-32B-IT', prices=ModelPrice(input_mtok=Decimal('0.25'), output_mtok=Decimal('0.5')), ), ModelInfo( id='allenai/Olmo-3-7B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='allenai/olmo-3-7b-instruct'), ClauseEquals(equals='allenai/olmo-3-7b-instruct-fast'), ] ), name='Olmo-3-7B-Instruct', prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.2')), ), ModelInfo( id='allenai/Olmo-3.1-32B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='allenai/olmo-3.1-32b-instruct'), ClauseEquals(equals='allenai/olmo-3.1-32b-instruct-fast'), ] ), name='Olmo-3.1-32B-Instruct', prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.6')), ), ModelInfo( id='dicta-il/DictaLM-3.0-24B-Thinking', match=ClauseOr( or_=[ ClauseEquals(equals='dicta-il/dictalm-3.0-24b-thinking'), ClauseEquals(equals='dicta-il/dictalm-3.0-24b-thinking-fast'), ] ), name='DictaLM-3.0-24B-Thinking', prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.4')), ), ModelInfo( id='swiss-ai/Apertus-70B-Instruct-2509', match=ClauseOr( or_=[ ClauseEquals(equals='swiss-ai/apertus-70b-instruct-2509'), ClauseEquals(equals='swiss-ai/apertus-70b-instruct-2509-fast'), ] ), name='Apertus-70B-Instruct-2509', prices=ModelPrice(input_mtok=Decimal('0.82'), output_mtok=Decimal('2.92')), ), ModelInfo( id='swiss-ai/Apertus-8B-Instruct-2509', match=ClauseOr( or_=[ ClauseEquals(equals='swiss-ai/apertus-8b-instruct-2509'), ClauseEquals(equals='swiss-ai/apertus-8b-instruct-2509-fast'), ] ), name='Apertus-8B-Instruct-2509', prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.2')), ), ModelInfo( id='utter-project/EuroLLM-22B-Instruct-2512', match=ClauseOr( or_=[ ClauseEquals(equals='utter-project/eurollm-22b-instruct-2512'), ClauseEquals(equals='utter-project/eurollm-22b-instruct-2512-fast'), ] ), name='EuroLLM-22B-Instruct-2512', prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.2')), ), ], ), Provider( id='huggingface_sambanova', name='HuggingFace (sambanova)', api_pattern='https://router\\.huggingface\\.co/sambanova', pricing_urls=['https://router.huggingface.co/v1/models', 'https://huggingface.co/inference/models'], provider_match=ClauseAnd(and_=[ClauseContains(contains='huggingface'), ClauseContains(contains='sambanova')]), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), ], models=[ ModelInfo( id='Qwen/Qwen3-32B', match=ClauseOr(or_=[ClauseEquals(equals='qwen/qwen3-32b'), ClauseEquals(equals='qwen/qwen3-32b-fast')]), name='Qwen3-32B', context_window=32768, prices=ModelPrice(input_mtok=Decimal('0.4'), output_mtok=Decimal('0.8')), ), ModelInfo( id='deepseek-ai/DeepSeek-R1-0528', match=ClauseOr( or_=[ ClauseEquals(equals='deepseek-ai/deepseek-r1-0528'), ClauseEquals(equals='deepseek-ai/deepseek-r1-0528-fast'), ] ), name='DeepSeek-R1-0528', context_window=131072, prices=ModelPrice(input_mtok=Decimal('5'), output_mtok=Decimal('7')), ), ModelInfo( id='deepseek-ai/DeepSeek-R1-Distill-Llama-70B', match=ClauseOr( or_=[ ClauseEquals(equals='deepseek-ai/deepseek-r1-distill-llama-70b'), ClauseEquals(equals='deepseek-ai/deepseek-r1-distill-llama-70b-fast'), ] ), name='DeepSeek-R1-Distill-Llama-70B', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.7'), output_mtok=Decimal('1.4')), ), ModelInfo( id='deepseek-ai/DeepSeek-V3-0324', match=ClauseOr( or_=[ ClauseEquals(equals='deepseek-ai/deepseek-v3-0324'), ClauseEquals(equals='deepseek-ai/deepseek-v3-0324-fast'), ] ), name='DeepSeek-V3-0324', context_window=131072, prices=ModelPrice(input_mtok=Decimal('3'), output_mtok=Decimal('4.5')), ), ModelInfo( id='meta-llama/Llama-3.1-8B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='meta-llama/llama-3.1-8b-instruct'), ClauseEquals(equals='meta-llama/llama-3.1-8b-instruct-fast'), ] ), name='Llama-3.1-8B-Instruct', context_window=16384, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.2')), ), ModelInfo( id='meta-llama/Llama-3.3-70B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='meta-llama/llama-3.3-70b-instruct'), ClauseEquals(equals='meta-llama/llama-3.3-70b-instruct-fast'), ] ), name='Llama-3.3-70B-Instruct', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.6'), output_mtok=Decimal('1.2')), ), ModelInfo( id='openai/gpt-oss-120b', match=ClauseOr( or_=[ClauseEquals(equals='openai/gpt-oss-120b'), ClauseEquals(equals='openai/gpt-oss-120b-fast')] ), name='gpt-oss-120b', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.22'), output_mtok=Decimal('0.59')), ), ModelInfo( id='tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4', match=ClauseOr( or_=[ ClauseEquals(equals='tokyotech-llm/llama-3.3-swallow-70b-instruct-v0.4'), ClauseEquals(equals='tokyotech-llm/llama-3.3-swallow-70b-instruct-v0.4-fast'), ] ), name='Llama-3.3-Swallow-70B-Instruct-v0.4', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.6'), output_mtok=Decimal('1.2')), ), ], ), Provider( id='huggingface_together', name='HuggingFace (together)', api_pattern='https://router\\.huggingface\\.co/together', pricing_urls=['https://router.huggingface.co/v1/models', 'https://huggingface.co/inference/models'], provider_match=ClauseAnd(and_=[ClauseContains(contains='huggingface'), ClauseContains(contains='together')]), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), ], models=[ ModelInfo( id='EssentialAI/rnj-1-instruct', match=ClauseOr( or_=[ ClauseEquals(equals='essentialai/rnj-1-instruct'), ClauseEquals(equals='essentialai/rnj-1-instruct-fast'), ] ), name='rnj-1-instruct', context_window=32768, prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.15')), ), ModelInfo( id='Qwen/Qwen2.5-7B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen2.5-7b-instruct'), ClauseEquals(equals='qwen/qwen2.5-7b-instruct-fast'), ] ), name='Qwen2.5-7B-Instruct', context_window=32768, prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('0.3')), ), ModelInfo( id='Qwen/Qwen3-235B-A22B-Instruct-2507', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen3-235b-a22b-instruct-2507'), ClauseEquals(equals='qwen/qwen3-235b-a22b-instruct-2507-fast'), ] ), name='Qwen3-235B-A22B-Instruct-2507', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.6')), ), ModelInfo( id='Qwen/Qwen3-Coder-480B-A35B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen3-coder-480b-a35b-instruct'), ClauseEquals(equals='qwen/qwen3-coder-480b-a35b-instruct-fast'), ClauseEquals(equals='qwen/qwen3-coder-480b-a35b-instruct-fp8'), ClauseEquals(equals='qwen/qwen3-coder-480b-a35b-instruct-fp8-fast'), ] ), name='Qwen3-Coder-480B-A35B-Instruct', context_window=262144, prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('2')), ), ModelInfo( id='Qwen/Qwen3-Coder-Next-FP8', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen3-coder-next-fp8'), ClauseEquals(equals='qwen/qwen3-coder-next-fp8-fast'), ] ), name='Qwen3-Coder-Next-FP8', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('1.2')), ), ModelInfo( id='Qwen/Qwen3-Next-80B-A3B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen3-next-80b-a3b-instruct'), ClauseEquals(equals='qwen/qwen3-next-80b-a3b-instruct-fast'), ] ), name='Qwen3-Next-80B-A3B-Instruct', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('1.5')), ), ModelInfo( id='Qwen/Qwen3-VL-8B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen3-vl-8b-instruct'), ClauseEquals(equals='qwen/qwen3-vl-8b-instruct-fast'), ] ), name='Qwen3-VL-8B-Instruct', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.18000000000000002'), output_mtok=Decimal('0.68')), ), ModelInfo( id='Qwen/Qwen3.5-397B-A17B', match=ClauseOr( or_=[ ClauseEquals(equals='qwen/qwen3.5-397b-a17b'), ClauseEquals(equals='qwen/qwen3.5-397b-a17b-fast'), ] ), name='Qwen3.5-397B-A17B', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.6'), output_mtok=Decimal('3.6')), ), ModelInfo( id='Qwen/Qwen3.5-9B', match=ClauseOr( or_=[ClauseEquals(equals='qwen/qwen3.5-9b'), ClauseEquals(equals='qwen/qwen3.5-9b-fast')] ), name='Qwen3.5-9B', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.15')), ), ModelInfo( id='ServiceNow-AI/Apriel-1.6-15b-Thinker', match=ClauseOr( or_=[ ClauseEquals(equals='servicenow-ai/apriel-1.6-15b-thinker'), ClauseEquals(equals='servicenow-ai/apriel-1.6-15b-thinker-fast'), ] ), name='Apriel-1.6-15b-Thinker', context_window=131072, prices=ModelPrice(), ), ModelInfo( id='deepcogito/cogito-671b-v2.1', match=ClauseOr( or_=[ ClauseEquals(equals='deepcogito/cogito-671b-v2.1'), ClauseEquals(equals='deepcogito/cogito-671b-v2.1-fast'), ClauseEquals(equals='deepcogito/cogito-671b-v2.1-fp8'), ClauseEquals(equals='deepcogito/cogito-671b-v2.1-fp8-fast'), ] ), name='cogito-671b-v2.1', context_window=163840, prices=ModelPrice(input_mtok=Decimal('1.25'), output_mtok=Decimal('1.25')), ), ModelInfo( id='deepseek-ai/DeepSeek-R1', match=ClauseOr( or_=[ ClauseEquals(equals='deepseek-ai/deepseek-r1'), ClauseEquals(equals='deepseek-ai/deepseek-r1-fast'), ClauseEquals(equals='deepseek-ai/deepseek-r1-0528'), ClauseEquals(equals='deepseek-ai/deepseek-r1-0528-fast'), ] ), name='DeepSeek-R1', context_window=163840, prices=ModelPrice(input_mtok=Decimal('3'), output_mtok=Decimal('7')), ), ModelInfo( id='deepseek-ai/DeepSeek-V3', match=ClauseOr( or_=[ ClauseEquals(equals='deepseek-ai/deepseek-v3'), ClauseEquals(equals='deepseek-ai/deepseek-v3-fast'), ClauseEquals(equals='deepseek-ai/deepseek-v3-0324'), ClauseEquals(equals='deepseek-ai/deepseek-v3-0324-fast'), ] ), name='DeepSeek-V3', context_window=131072, prices=ModelPrice(input_mtok=Decimal('1.25'), output_mtok=Decimal('1.25')), ), ModelInfo( id='deepseek-ai/DeepSeek-V3.1', match=ClauseOr( or_=[ ClauseEquals(equals='deepseek-ai/deepseek-v3.1'), ClauseEquals(equals='deepseek-ai/deepseek-v3.1-fast'), ] ), name='DeepSeek-V3.1', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.6'), output_mtok=Decimal('1.7')), ), ModelInfo( id='google/gemma-3n-E4B-it', match=ClauseOr( or_=[ ClauseEquals(equals='google/gemma-3n-e4b-it'), ClauseEquals(equals='google/gemma-3n-e4b-it-fast'), ] ), name='gemma-3n-E4B-it', context_window=32768, prices=ModelPrice(input_mtok=Decimal('0.02'), output_mtok=Decimal('0.04')), ), ModelInfo( id='meta-llama/Llama-3.3-70B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='meta-llama/llama-3.3-70b-instruct'), ClauseEquals(equals='meta-llama/llama-3.3-70b-instruct-fast'), ] ), name='Llama-3.3-70B-Instruct', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.88'), output_mtok=Decimal('0.88')), ), ModelInfo( id='meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8', match=ClauseOr( or_=[ ClauseEquals(equals='meta-llama/llama-4-maverick-17b-128e-instruct-fp8'), ClauseEquals(equals='meta-llama/llama-4-maverick-17b-128e-instruct-fp8-fast'), ] ), name='Llama-4-Maverick-17B-128E-Instruct-FP8', context_window=1048576, prices=ModelPrice(input_mtok=Decimal('0.27'), output_mtok=Decimal('0.85')), ), ModelInfo( id='moonshotai/Kimi-K2.5', match=ClauseOr( or_=[ClauseEquals(equals='moonshotai/kimi-k2.5'), ClauseEquals(equals='moonshotai/kimi-k2.5-fast')] ), name='Kimi-K2.5', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('2.8')), ), ModelInfo( id='openai/gpt-oss-120b', match=ClauseOr( or_=[ClauseEquals(equals='openai/gpt-oss-120b'), ClauseEquals(equals='openai/gpt-oss-120b-fast')] ), name='gpt-oss-120b', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.6')), ), ModelInfo( id='openai/gpt-oss-20b', match=ClauseOr( or_=[ClauseEquals(equals='openai/gpt-oss-20b'), ClauseEquals(equals='openai/gpt-oss-20b-fast')] ), name='gpt-oss-20b', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.05'), output_mtok=Decimal('0.2')), ), ModelInfo( id='zai-org/GLM-4.5-Air-FP8', match=ClauseOr( or_=[ ClauseEquals(equals='zai-org/glm-4.5-air-fp8'), ClauseEquals(equals='zai-org/glm-4.5-air-fp8-fast'), ] ), name='GLM-4.5-Air-FP8', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('1.1')), ), ModelInfo( id='zai-org/GLM-4.6', match=ClauseOr( or_=[ClauseEquals(equals='zai-org/glm-4.6'), ClauseEquals(equals='zai-org/glm-4.6-fast')] ), name='GLM-4.6', context_window=202752, prices=ModelPrice(input_mtok=Decimal('0.6'), output_mtok=Decimal('2.2')), ), ModelInfo( id='zai-org/GLM-4.7-FP8', match=ClauseOr( or_=[ClauseEquals(equals='zai-org/glm-4.7-fp8'), ClauseEquals(equals='zai-org/glm-4.7-fp8-fast')] ), name='GLM-4.7-FP8', context_window=202752, prices=ModelPrice(input_mtok=Decimal('0.45'), output_mtok=Decimal('2')), ), ModelInfo( id='zai-org/GLM-5', match=ClauseOr(or_=[ClauseEquals(equals='zai-org/glm-5'), ClauseEquals(equals='zai-org/glm-5-fast')]), name='GLM-5', context_window=202752, prices=ModelPrice(input_mtok=Decimal('1'), output_mtok=Decimal('3.2')), ), ], ), Provider( id='minimax', name='MiniMax', api_pattern='https://api\\.minimax(i)?\\.(?:com|io)', pricing_urls=['https://platform.minimax.io/docs/guides/pricing-paygo'], price_comments='Prices sourced from MiniMax international platform USD pricing (platform.minimax.io, May 2026). M2.1, M2.1-highspeed, and M2 are legacy models; prices inferred from CNY pricing at the equivalent 7.00 CNY/USD rate used by the international platform for current models.', model_match=ClauseOr( or_=[ ClauseStartsWith(starts_with='MiniMax-M'), ClauseStartsWith(starts_with='minimax-m'), ClauseEquals(equals='minimax-01'), ClauseEquals(equals='M2-her'), ClauseEquals(equals='m2-her'), ] ), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=True), UsageExtractorMapping(path='cache_creation_input_tokens', dest='input_tokens', required=False), UsageExtractorMapping(path='cache_read_input_tokens', dest='input_tokens', required=False), UsageExtractorMapping( path='cache_creation_input_tokens', dest='cache_write_tokens', required=False ), UsageExtractorMapping(path='cache_read_input_tokens', dest='cache_read_tokens', required=False), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['input_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=True), ], api_flavor='responses', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), ], models=[ ModelInfo( id='M2-her', match=ClauseOr(or_=[ClauseEquals(equals='M2-her'), ClauseEquals(equals='m2-her')]), name='MiniMax M2-her', description='MiniMax M2-her, a text dialogue model optimized for role-playing and multi-turn conversations. 64,000 token context window. No cache support.', context_window=64000, prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('1.2')), ), ModelInfo( id='MiniMax-M2', match=ClauseOr( or_=[ ClauseEquals(equals='MiniMax-M2'), ClauseEquals(equals='minimax-m2'), ClauseEquals(equals='MiniMax-M2.1'), ClauseEquals(equals='minimax-m2.1'), ClauseEquals(equals='MiniMax-M2.5'), ClauseEquals(equals='minimax-m2.5'), ] ), name='MiniMax M2', description='MiniMax M2 (legacy), a multimodal language model with 204,800 token context window. Supports agentic capabilities and advanced reasoning.', context_window=204800, prices=ModelPrice( input_mtok=Decimal('0.3'), cache_write_mtok=Decimal('0.375'), cache_read_mtok=Decimal('0.03'), output_mtok=Decimal('1.2'), ), ), ModelInfo( id='MiniMax-M2.1-highspeed', match=ClauseOr( or_=[ClauseContains(contains='M2.1-highspeed'), ClauseContains(contains='m2.1-highspeed')] ), name='MiniMax M2.1 Highspeed', description='MiniMax M2.1 highspeed variant (legacy) with higher throughput.', context_window=204800, prices=ModelPrice( input_mtok=Decimal('0.6'), cache_write_mtok=Decimal('0.375'), cache_read_mtok=Decimal('0.03'), output_mtok=Decimal('2.4'), ), ), ModelInfo( id='MiniMax-M2.5-highspeed', match=ClauseOr( or_=[ClauseContains(contains='M2.5-highspeed'), ClauseContains(contains='m2.5-highspeed')] ), name='MiniMax M2.5 Highspeed', description='MiniMax M2.5 highspeed variant with higher throughput.', context_window=204800, prices=ModelPrice( input_mtok=Decimal('0.6'), cache_write_mtok=Decimal('0.375'), cache_read_mtok=Decimal('0.03'), output_mtok=Decimal('2.4'), ), ), ModelInfo( id='MiniMax-M2.7', match=ClauseOr(or_=[ClauseEquals(equals='MiniMax-M2.7'), ClauseEquals(equals='minimax-m2.7')]), name='MiniMax M2.7', description='MiniMax M2.7, a multimodal language model with 204,800 token context window. Achieves top performance in real-world engineering, office productivity, and character-rich interaction.', context_window=204800, prices=ModelPrice( input_mtok=Decimal('0.3'), cache_write_mtok=Decimal('0.375'), cache_read_mtok=Decimal('0.06'), output_mtok=Decimal('1.2'), ), ), ModelInfo( id='MiniMax-M2.7-highspeed', match=ClauseOr( or_=[ClauseContains(contains='M2.7-highspeed'), ClauseContains(contains='m2.7-highspeed')] ), name='MiniMax M2.7 Highspeed', description='MiniMax M2.7 highspeed variant with higher throughput.', context_window=204800, prices=ModelPrice( input_mtok=Decimal('0.6'), cache_write_mtok=Decimal('0.375'), cache_read_mtok=Decimal('0.06'), output_mtok=Decimal('2.4'), ), ), ModelInfo( id='minimax-01', match=ClauseEquals(equals='minimax-01'), name='MiniMax-01', description='MiniMax-01 combines MiniMax-Text-01 for text generation and MiniMax-VL-01 for image understanding.', context_window=1000192, price_comments='Imported from OpenRouter pricing; verify against MiniMax pricing when native API pricing is published.', prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('1.1')), ), ModelInfo( id='minimax-m1', match=ClauseEquals(equals='minimax-m1'), name='MiniMax M1', description='MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing efficient long-context processing.', price_comments='Imported from OpenRouter pricing; verify against MiniMax pricing when native API pricing is published.', prices=ModelPrice(input_mtok=Decimal('0.4'), output_mtok=Decimal('2.2')), ), ModelInfo( id='minimax-m3', match=ClauseEquals(equals='minimax-m3'), name='MiniMax M3', description='MiniMax-M3 is a multimodal foundation model from MiniMax. It supports text, image, and video inputs with text output, a 1M-token context window, and long-horizon agentic work.', context_window=1000000, price_comments='Prices from MiniMax pay-as-you-go page (https://platform.minimax.io/docs/guides/pricing-paygo, 2026-07-01), standard service tier "Permanent 50% off" effective rate. Inputs over 512K tokens bill at 2x per MiniMax\'s length-based tiering.', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('0.3'), tiers=[Tier(start=512000, price=Decimal('0.6'))]), cache_read_mtok=TieredPrices( base=Decimal('0.06'), tiers=[Tier(start=512000, price=Decimal('0.12'))] ), output_mtok=TieredPrices(base=Decimal('1.2'), tiers=[Tier(start=512000, price=Decimal('2.4'))]), ), ), ], ), Provider( id='mistral', name='Mistral', api_pattern='https://api\\.mistral\\.ai', pricing_urls=['https://mistral.ai/pricing/api'], model_match=ClauseRegex( regex='^(?![^/]+/)(?:labs-)?(?:(?:mi|code|dev|magi|mini)stral|mixtral|pixtral|voxtral|open-(?:mistral|mixtral))' ), provider_match=ClauseStartsWith(starts_with='mistral'), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ), UsageExtractor( root='usage_info', mappings=[UsageExtractorMapping(path='pages_processed', dest='input_document_pages', required=True)], api_flavor='ocr', model_path='model', ), UsageExtractor( root='usage_info', mappings=[ UsageExtractorMapping(path='pages_processed', dest='input_document_pages', required=True), UsageExtractorMapping(path='pages_processed', dest='input_annotated_document_pages', required=True), ], api_flavor='ocr_annotated', model_path='model', ), ], models=[ ModelInfo( id='codestral', match=ClauseOr(or_=[ClauseEquals(equals='codestral-latest'), ClauseEquals(equals='codestral-2501')]), name='Codestral', description="Mistral's cutting-edge language model for coding. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation.", context_window=256000, prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('0.9')), ), ModelInfo( id='codestral-2508', match=ClauseEquals(equals='codestral-2508'), name='Codestral 2508', description="Mistral's cutting-edge language model for coding released end of July 2025. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation.", context_window=256000, prices=ModelPrice( input_mtok=Decimal('0.3'), cache_read_mtok=Decimal('0.03'), output_mtok=Decimal('0.9') ), ), ModelInfo( id='devstral-2512', match=ClauseEquals(equals='devstral-2512'), name='Devstral 2 2512', description='Devstral 2 is a state-of-the-art open-source model by Mistral AI specializing in agentic coding. It is a 123B-parameter dense transformer model supporting a 256K context window.', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.4'), cache_read_mtok=Decimal('0.04'), output_mtok=Decimal('2')), ), ModelInfo( id='devstral-small', match=ClauseOr( or_=[ ClauseEquals(equals='devstral-small'), ClauseEquals(equals='devstral-small-2505'), ClauseEquals(equals='devstral-small-2507'), ClauseEquals(equals='devstral-small-latest'), ClauseEquals(equals='labs-devstral-small-2512'), ] ), name='Devstral Small', description='Devstral-Small-2505 is a 24B parameter agentic LLM fine-tuned from Mistral-Small-3.1, jointly developed by Mistral AI and All Hands AI for advanced software engineering tasks. It is optimized for codebase exploration, multi-file editing, and integration into coding agents, achieving state-of-the-art results on SWE-Bench Verified (46.8%).', price_comments="The previous $0.06/$0.12 rates were not Mistral's; every Devstral Small generation is documented at $0.10/$0.30. Ref: https://github.com/mistralai/platform-docs-public/tree/main/src/schema/models/models (devstral-small-*.ts)", deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.3')), ), ModelInfo( id='devstral-small:free', match=ClauseEquals(equals='devstral-small:free'), name='Devstral Small (free)', description='Devstral-Small-2505 is a 24B parameter agentic LLM fine-tuned from Mistral-Small-3.1, jointly developed by Mistral AI and All Hands AI for advanced software engineering tasks. It is optimized for codebase exploration, multi-file editing, and integration into coding agents, achieving state-of-the-art results on SWE-Bench Verified (46.8%).', prices=ModelPrice(), ), ModelInfo( id='magistral-medium', match=ClauseOr(or_=[ClauseStartsWith(starts_with='magistral-medium')]), name='Magistral Medium', description="Magistral is Mistral's first reasoning model. It is ideal for general purpose use requiring longer thought processing and better accuracy than with non-reasoning LLMs. From legal research and financial forecasting to software development and creative storytelling — this model solves multi-step challenges where transparency and precision are critical.", prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('5')), ), ModelInfo( id='magistral-small', match=ClauseStartsWith(starts_with='magistral-small-'), name='Magistral Small', description='Magistral Small is a 24B parameter instruction-tuned model based on Mistral-Small-3.1 (2503), enhanced through supervised fine-tuning on traces from Magistral Medium and further refined via reinforcement learning. It is optimized for reasoning and supports a wide multilingual range, including over 20 languages.', prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('1.5')), ), ModelInfo( id='ministral-14b-2512', match=ClauseEquals(equals='ministral-14b-2512'), name='Ministral 3 14B 2512', description='The largest model in the Ministral 3 family, Ministral 3 14B offers frontier capabilities and performance comparable to its larger Mistral Small 3.2 24B counterpart.', context_window=262144, prices=ModelPrice( input_mtok=Decimal('0.2'), cache_read_mtok=Decimal('0.02'), output_mtok=Decimal('0.2') ), ), ModelInfo( id='ministral-3b', match=ClauseEquals(equals='ministral-3b'), name='Ministral 3B', description="Ministral 3B is a 3B parameter model optimized for on-device and edge computing. It excels in knowledge, commonsense reasoning, and function-calling, outperforming larger models like Mistral 7B on most benchmarks. Supporting up to 128k context length, it's ideal for orchestrating agentic workflows and specialist tasks with efficient inference.", prices=ModelPrice(input_mtok=Decimal('0.04'), output_mtok=Decimal('0.04')), ), ModelInfo( id='ministral-3b-2512', match=ClauseEquals(equals='ministral-3b-2512'), name='Ministral 3 3B 2512', description='The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.', context_window=131072, prices=ModelPrice( input_mtok=Decimal('0.1'), cache_read_mtok=Decimal('0.01'), output_mtok=Decimal('0.1') ), ), ModelInfo( id='ministral-8b', match=ClauseOr(or_=[ClauseEquals(equals='ministral-8b'), ClauseEquals(equals='ministral-8b-2410')]), name='Ministral 8B 24.10', description='Ministral 8B is an 8B parameter model featuring a unique interleaved sliding-window attention pattern for faster, memory-efficient inference. Designed for edge use cases, it supports up to 128k context length and excels in knowledge and reasoning tasks. It outperforms peers in the sub-10B category, making it perfect for low-latency, privacy-first applications.', price_comments='The previous $1 output price was incorrect; Mistral documented $0.10 from launch. Ref: https://github.com/mistralai/platform-docs-public/blob/4422b455194a/src/schema/models/models/ministral-8b-24-1.ts', prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ), ModelInfo( id='ministral-8b-2512', match=ClauseEquals(equals='ministral-8b-2512'), name='Ministral 3 8B 2512', description='Ministral 3 8B is an efficient text and vision model for edge deployment.', context_window=262144, price_comments='Ref: https://mistral.ai/pricing/api', prices=ModelPrice( input_mtok=Decimal('0.15'), cache_read_mtok=Decimal('0.015'), output_mtok=Decimal('0.15') ), ), ModelInfo( id='ministral-8b-latest', match=ClauseEquals(equals='ministral-8b-latest'), name='Ministral 8B Latest', price_comments='The latest alias moved from Ministral 8B 24.10 to Ministral 3 8B on 2025-12-02. Ref: https://github.com/mistralai/platform-docs-public/commit/4975b09514f95978cfeeea814562000348548107', prices=[ ConditionalPrice( constraint=None, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2025, 12, 2)), prices=ModelPrice( input_mtok=Decimal('0.15'), cache_read_mtok=Decimal('0.015'), output_mtok=Decimal('0.15') ), ), ], ), ModelInfo( id='mistral-7b', match=ClauseOr(or_=[ClauseEquals(equals='mistral-7b'), ClauseEquals(equals='open-mistral-7b')]), name='Mistral 7B', prices=ModelPrice(input_mtok=Decimal('0.25'), output_mtok=Decimal('0.25')), ), ModelInfo( id='mistral-embed', match=ClauseEquals(equals='mistral-embed'), context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ), ModelInfo( id='mistral-large', match=ClauseOr( or_=[ ClauseEquals(equals='mistral-large'), ClauseEquals(equals='mistral-large-latest'), ClauseEquals(equals='mistral-large-2407'), ClauseEquals(equals='mistral-large-2411'), ] ), name='Mistral Large', description="This is Mistral AI's flagship model, Mistral Large 2 (version `mistral-large-2407`). It's a proprietary weights-available model and excels at reasoning, code, JSON, chat, and more. Read the launch announcement here.", prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('6')), ), ModelInfo( id='mistral-large-2512', match=ClauseEquals(equals='mistral-large-2512'), name='Mistral Large 3 2512', description="Mistral Large 3 2512 is Mistral's most capable model to date, featuring a sparse mixture-of-experts architecture with 41B active parameters (675B total), and released under the Apache 2.0 license.", context_window=262144, prices=ModelPrice( input_mtok=Decimal('0.5'), cache_read_mtok=Decimal('0.05'), output_mtok=Decimal('1.5') ), ), ModelInfo( id='mistral-medium-2312', match=ClauseEquals(equals='mistral-medium-2312'), name='Mistral Medium 1', description="Mistral's first enterprise-grade model, released in December 2023.", price_comments='Retired on 2025-06-16; retained for historical usage records at its original $2.70/$8.10 per MTok rates. Ref: https://github.com/mistralai/platform-docs-public/blob/222f4ba9114f84ce9f2d718b9a12fbac1e527f4b/src/schema/models/models/mistral-medium-1-0-23-12.ts', prices=ModelPrice(input_mtok=Decimal('2.7'), output_mtok=Decimal('8.1')), ), ModelInfo( id='mistral-medium-3-1', match=ClauseOr( or_=[ ClauseEquals(equals='mistral-medium'), ClauseEquals(equals='mistral-medium-2505'), ClauseEquals(equals='mistral-medium-2508'), ] ), name='Mistral Medium 3 and 3.1', description='Mistral Medium 3 is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances state-of-the-art reasoning and multimodal performance with 8× lower cost compared to traditional large models, making it suitable for scalable deployments across professional and industrial use cases.', price_comments='Mistral Medium 3 and 3.1 retain their original $0.40/$2 per MTok rates. Ref: https://github.com/mistralai/platform-docs-public/blob/222f4ba9114f84ce9f2d718b9a12fbac1e527f4b/src/schema/models/models/mistral-medium-3-1-25-08.ts', prices=ModelPrice(input_mtok=Decimal('0.4'), cache_read_mtok=Decimal('0.04'), output_mtok=Decimal('2')), ), ModelInfo( id='mistral-medium-3-5', match=ClauseOr( or_=[ ClauseEquals(equals='mistral-medium-3.5'), ClauseEquals(equals='mistral-medium-3-5'), ClauseEquals(equals='mistral-medium-3'), ] ), name='Mistral Medium 3.5', description='Mistral Medium 3.5 is a frontier-class multimodal model optimized for agentic and coding use cases.', context_window=262144, price_comments='Ref: https://mistral.ai/pricing/api', prices=ModelPrice( input_mtok=Decimal('1.5'), cache_read_mtok=Decimal('0.15'), output_mtok=Decimal('7.5') ), ), ModelInfo( id='mistral-medium-latest', match=ClauseEquals(equals='mistral-medium-latest'), name='Mistral Medium Latest', price_comments='The latest alias moved from Mistral Medium 3.1 to Mistral Medium 3.5 on 2026-06-16. Ref: https://github.com/mistralai/platform-docs-public/commit/cc58c1186b1ca8ad65658f5dfd3ebd29de778c7f', prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=Decimal('0.4'), cache_read_mtok=Decimal('0.04'), output_mtok=Decimal('2') ) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2026, 6, 16)), prices=ModelPrice( input_mtok=Decimal('1.5'), cache_read_mtok=Decimal('0.15'), output_mtok=Decimal('7.5') ), ), ], ), ModelInfo( id='mistral-nemo', match=ClauseOr(or_=[ClauseEquals(equals='mistral-nemo'), ClauseEquals(equals='open-mistral-nemo')]), name='Mistral NeMo', description='A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA.', prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.15')), ), ModelInfo( id='mistral-nemo:free', match=ClauseEquals(equals='mistral-nemo:free'), name='Mistral Nemo (free)', description='A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA.', prices=ModelPrice(), ), ModelInfo( id='mistral-ocr-2503', match=ClauseOr( or_=[ClauseEquals(equals='mistral-ocr-2503'), ClauseEquals(equals='mistral-ocr-2503-completion')] ), name='Mistral OCR', price_comments='Mistral launched OCR at 1,000 pages per US dollar: https://mistral.ai/news/mistral-ocr/', deprecated=True, prices=ModelPrice(input_document_kpages=Decimal('1')), ), ModelInfo( id='mistral-ocr-2505', match=ClauseOr( or_=[ClauseEquals(equals='mistral-ocr-2505'), ClauseEquals(equals='mistral-ocr-2505-completion')] ), name='Mistral OCR 2', price_comments='OCR 2 retained the original OCR page price and introduced structured annotations on May 22, 2025: https://docs.mistral.ai/resources/changelogs', deprecated=True, prices=ModelPrice(input_document_kpages=Decimal('1'), input_annotated_document_kpages=Decimal('3')), ), ModelInfo( id='mistral-ocr-2512', match=ClauseOr( or_=[ClauseEquals(equals='mistral-ocr-2512'), ClauseEquals(equals='mistral-ocr-2512-completion')] ), name='Mistral OCR 3', context_window=16384, price_comments='https://docs.mistral.ai/models/ocr-3-25-12', prices=ModelPrice(input_document_kpages=Decimal('2'), input_annotated_document_kpages=Decimal('3')), ), ModelInfo( id='mistral-ocr-4-0', match=ClauseOr( or_=[ClauseEquals(equals='mistral-ocr-4-0'), ClauseEquals(equals='mistral-ocr-4-0-completion')] ), name='Mistral OCR 4.0', context_window=16384, price_comments='https://docs.mistral.ai/models/ocr-4-0', prices=ModelPrice(input_document_kpages=Decimal('4'), input_annotated_document_kpages=Decimal('5')), ), ModelInfo( id='mistral-ocr-4-1', match=ClauseOr( or_=[ ClauseEquals(equals='mistral-ocr-4'), ClauseEquals(equals='mistral-ocr-4-completion'), ClauseEquals(equals='mistral-ocr-4-1'), ClauseEquals(equals='mistral-ocr-4-1-completion'), ] ), name='Mistral OCR 4.1', context_window=16384, price_comments='https://docs.mistral.ai/models/ocr-4-1', prices=ModelPrice(input_document_kpages=Decimal('4'), input_annotated_document_kpages=Decimal('5')), ), ModelInfo( id='mistral-ocr-latest', match=ClauseOr( or_=[ ClauseEquals(equals='mistral-ocr-latest'), ClauseEquals(equals='mistral-ocr-latest-completion'), ] ), name='Mistral OCR Latest', price_comments='The latest alias moved to OCR 3 on December 18, 2025, OCR 4.0 on June 23, 2026, and OCR 4.1 on July 16, 2026. OCR 4.1 kept the OCR 4.0 rates: https://docs.mistral.ai/resources/changelogs', prices=[ ConditionalPrice(constraint=None, prices=ModelPrice(input_document_kpages=Decimal('1'))), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2025, 5, 22)), prices=ModelPrice( input_document_kpages=Decimal('1'), input_annotated_document_kpages=Decimal('3') ), ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2025, 12, 18)), prices=ModelPrice( input_document_kpages=Decimal('2'), input_annotated_document_kpages=Decimal('3') ), ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2026, 6, 23)), prices=ModelPrice( input_document_kpages=Decimal('4'), input_annotated_document_kpages=Decimal('5') ), ), ], ), ModelInfo( id='mistral-saba', match=ClauseOr(or_=[ClauseEquals(equals='mistral-saba'), ClauseEquals(equals='mistral-saba-latest')]), name='Mistral Saba', description='Mistral Saba is a 24B-parameter language model specifically designed for the Middle East and South Asia, delivering accurate and contextually relevant responses while maintaining efficient performance. Trained on curated regional datasets, it supports multiple Indian-origin languages—including Tamil and Malayalam—alongside Arabic. This makes it a versatile option for a range of regional and multilingual applications. Read more at the blog post here', prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.6')), ), ModelInfo( id='mistral-small-24b-instruct-2501', match=ClauseEquals(equals='mistral-small-24b-instruct-2501'), name='Mistral Small 3', description='Mistral Small 3 is a 24B-parameter language model optimized for low-latency performance across common AI tasks. Released under the Apache 2.0 license, it features both pre-trained and instruction-tuned versions designed for efficient local deployment.', price_comments="Can't find pricing on this model, so just trusting open router", prices=ModelPrice(input_mtok=Decimal('0.05'), output_mtok=Decimal('0.08')), ), ModelInfo( id='mistral-small-24b-instruct-2501:free', match=ClauseEquals(equals='mistral-small-24b-instruct-2501:free'), name='Mistral Small 3 (free)', description='Mistral Small 3 is a 24B-parameter language model optimized for low-latency performance across common AI tasks. Released under the Apache 2.0 license, it features both pre-trained and instruction-tuned versions designed for efficient local deployment.', prices=ModelPrice(), ), ModelInfo( id='mistral-small-2603', match=ClauseEquals(equals='mistral-small-2603'), name='Mistral Small 4', description='Mistral Small 4 is the next major release in the Mistral Small family, unifying the capabilities of several flagship Mistral models into a single system.', context_window=262144, prices=ModelPrice( input_mtok=Decimal('0.15'), cache_read_mtok=Decimal('0.015'), output_mtok=Decimal('0.6') ), ), ModelInfo( id='mistral-small-3.1-24b-instruct', match=ClauseEquals(equals='mistral-small-3.1-24b-instruct'), name='Mistral Small 3.1 24B', description='Mistral Small 3.1 24B Instruct is an upgraded variant of Mistral Small 3 (2501), featuring 24 billion parameters with advanced multimodal capabilities.', price_comments='Imported from OpenRouter pricing; verify against Mistral pricing when native API pricing is published.', prices=ModelPrice(input_mtok=Decimal('0.351'), output_mtok=Decimal('0.555')), ), ModelInfo( id='mistral-small-3.2-24b-instruct', match=ClauseEquals(equals='mistral-small-3.2-24b-instruct'), name='Mistral Small 3.2 24B', description='Mistral-Small-3.2-24B-Instruct-2506 is an updated 24B parameter model from Mistral optimized for instruction following, repetition reduction, and improved function calling.', price_comments='Imported from OpenRouter pricing; verify against Mistral pricing when native API pricing is published.', prices=ModelPrice(input_mtok=Decimal('0.075'), output_mtok=Decimal('0.2')), ), ModelInfo( id='mistral-small-latest', match=ClauseEquals(equals='mistral-small-latest'), name='Mistral Small 3.2', description='SOTA. Multimodal. Multilingual. Apache 2.0.', prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.3')), ), ModelInfo( id='mistral-tiny', match=ClauseEquals(equals='mistral-tiny'), name='Mistral Tiny', description='Note: This model is being deprecated. Recommended replacement is the newer Ministral 8B', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.25'), output_mtok=Decimal('0.25')), ), ModelInfo( id='mixtral-8x22b-instruct', match=ClauseEquals(equals='mixtral-8x22b-instruct'), name='Mixtral 8x22B Instruct', description="Mistral's official instruct fine-tuned version of Mixtral 8x22B. It uses 39B active parameters out of 141B, offering unparalleled cost efficiency for its size. Its strengths include:\n- strong math, coding, and reasoning\n- large context length (64k)\n- fluency in English, French, Italian, German, and Spanish", prices=ModelPrice(input_mtok=Decimal('0.9'), output_mtok=Decimal('0.9')), ), ModelInfo( id='mixtral-8x7b', match=ClauseOr( or_=[ClauseStartsWith(starts_with='mixtral-8x7b'), ClauseEquals(equals='open-mixtral-8x7b')] ), name='Mixtral 8x7B', prices=ModelPrice(input_mtok=Decimal('0.7'), output_mtok=Decimal('0.7')), ), ModelInfo( id='pixtral-12b', match=ClauseOr(or_=[ClauseEquals(equals='pixtral-12b'), ClauseEquals(equals='pixtral-12b-latest')]), name='Pixtral 12B', description='The first multi-modal, text+image-to-text model from Mistral AI. Its weights were launched via torrent: https://x.com/mistralai/status/1833758285167722836.', prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.15')), ), ModelInfo( id='pixtral-large', match=ClauseOr( or_=[ClauseEquals(equals='pixtral-large-latest'), ClauseEquals(equals='pixtral-large-2411')] ), name='Pixtral Large 2411', description='Pixtral Large is a 124B parameter, open-weight, multimodal model built on top of Mistral Large 2. The model is able to understand documents, charts and natural images.', prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('6')), ), ModelInfo( id='voxtral-mini-2602', match=ClauseOr( or_=[ClauseEquals(equals='voxtral-mini-latest'), ClauseEquals(equals='voxtral-mini-2602')] ), name='Voxtral Mini Transcribe 2', context_window=16384, price_comments='See https://docs.mistral.ai/models/voxtral-mini-transcribe-26-02.', prices=ModelPrice(audio_hours=Decimal('0.18'), input_audio_hours=Decimal('0.18')), ), ModelInfo( id='voxtral-small-24b-2507', match=ClauseOr( or_=[ ClauseEquals(equals='voxtral-small-24b-2507'), ClauseEquals(equals='voxtral-small-2507'), ClauseEquals(equals='voxtral-small-latest'), ] ), name='Voxtral Small 24B 2507', description='Voxtral Small is an enhancement of Mistral Small 3, incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance. It excels at speech transcription, translation and audio understanding.', context_window=32768, price_comments='Mistral raised output pricing from $0.30 to $0.40 per MTok on 2026-08-11. Ref: https://github.com/mistralai/platform-docs-public/commit/1996c3f1eca754d02436a37fcc899440794a18a5', prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=Decimal('0.1'), cache_read_mtok=Decimal('0.01'), output_mtok=Decimal('0.3') ) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2026, 8, 11)), prices=ModelPrice( input_mtok=Decimal('0.1'), cache_read_mtok=Decimal('0.01'), output_mtok=Decimal('0.4') ), ), ], ), ], ), Provider( id='modal', name='Modal', api_pattern='https://[^/]+\\.modal\\.(?:run|direct)(?:/|$)', pricing_urls=['https://modal.com/library'], provider_match=ClauseContains(contains='modal'), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping(path='reasoning_tokens', dest='output_reasoning_tokens', required=False), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['input_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['input_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['output_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=True), ], api_flavor='responses', model_path='model', ), ], models=[ ModelInfo( id='moonshotai/Kimi-K3', match=ClauseEquals(equals='moonshotai/Kimi-K3'), name='Kimi K3', description="Moonshot AI's 2.8-trillion-parameter multimodal reasoning model, served by Modal as a Shared Endpoint.", context_window=1048576, price_comments='Modal Shared Endpoint pricing. Reasoning tokens use the same $15/MTok rate as completion tokens. See https://modal.com/library/moonshot/kimi-k3.', prices=ModelPrice(input_mtok=Decimal('3'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15')), ), ModelInfo( id='thinkingmachines/Inkling-NVFP4', match=ClauseEquals(equals='thinkingmachines/Inkling-NVFP4'), name='Inkling NVFP4', description="Thinking Machines Lab's reasoning model, served by Modal as a Shared Endpoint.", price_comments='Modal Shared Endpoint dashboard pricing. Reasoning tokens use the same $5/MTok rate as completion tokens.', prices=ModelPrice(input_mtok=Decimal('1.2'), cache_read_mtok=Decimal('0.27'), output_mtok=Decimal('5')), ), ], ), Provider( id='moonshotai', name='MoonshotAi', api_pattern='https://api\\.moonshot\\.', pricing_urls=[ 'https://platform.kimi.ai/docs/models.md', 'https://platform.kimi.ai/docs/pricing/chat-k3.md', 'https://platform.kimi.ai/docs/pricing/chat-k27-code.md', 'https://platform.kimi.ai/docs/pricing/chat-k26.md', 'https://platform.kimi.ai/docs/pricing/chat-k25.md', 'https://platform.kimi.ai/docs/pricing/chat-v1.md', ], model_match=ClauseOr(or_=[ClauseStartsWith(starts_with='kimi'), ClauseStartsWith(starts_with='moonshot')]), provider_match=ClauseContains(contains='moonshot'), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ) ], models=[ ModelInfo( id='kimi-k2', match=ClauseEquals(equals='kimi-k2'), name='Kimi K2 0711', description='Kimi K2 Instruct is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass.', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.57'), output_mtok=Decimal('2.3')), ), ModelInfo( id='kimi-k2-0711-preview', match=ClauseEquals(equals='kimi-k2-0711-preview'), name='Kimi K2 0711 Preview', description='MoE foundation model with exceptional coding and agent capabilities, featuring 1 trillion total parameters and 32 billion activated parameters.', context_window=131072, deprecated=True, prices=ModelPrice( input_mtok=Decimal('0.6'), cache_read_mtok=Decimal('0.15'), output_mtok=Decimal('2.5') ), ), ModelInfo( id='kimi-k2-0905-preview', match=ClauseEquals(equals='kimi-k2-0905-preview'), name='Kimi K2 0905 Preview', description='Based on kimi-k2-0711-preview, with enhanced agentic coding abilities, improved frontend code quality and practicality, and better context understanding. MoE foundation model with 1 trillion total parameters and 32 billion activated parameters.', context_window=262144, deprecated=True, prices=ModelPrice( input_mtok=Decimal('0.6'), cache_read_mtok=Decimal('0.15'), output_mtok=Decimal('2.5') ), ), ModelInfo( id='kimi-k2-thinking', match=ClauseEquals(equals='kimi-k2-thinking'), name='Kimi K2 Thinking', description='A thinking model with general agentic and reasoning capabilities, specializing in deep reasoning tasks.', context_window=262144, deprecated=True, prices=ModelPrice( input_mtok=Decimal('0.6'), cache_read_mtok=Decimal('0.15'), output_mtok=Decimal('2.5') ), ), ModelInfo( id='kimi-k2-thinking-turbo', match=ClauseEquals(equals='kimi-k2-thinking-turbo'), name='Kimi K2 Thinking Turbo', description='High-speed version of kimi-k2-thinking, suitable for scenarios requiring both deep reasoning and extremely fast responses.', context_window=262144, deprecated=True, prices=ModelPrice( input_mtok=Decimal('1.15'), cache_read_mtok=Decimal('0.15'), output_mtok=Decimal('8') ), ), ModelInfo( id='kimi-k2-turbo-preview', match=ClauseStartsWith(starts_with='kimi-k2-turbo'), name='Kimi K2 Turbo Preview', description='High-speed version of kimi-k2, always aligned with the latest kimi-k2. Same model parameters as kimi-k2, output speed up to 60 tokens/sec (max 100 tokens/sec).', context_window=262144, deprecated=True, prices=ModelPrice( input_mtok=Decimal('1.15'), cache_read_mtok=Decimal('0.15'), output_mtok=Decimal('8') ), ), ModelInfo( id='kimi-k2.5', match=ClauseStartsWith(starts_with='kimi-k2.5'), name='Kimi K2.5', description="Kimi's most versatile model featuring a native multimodal architecture that supports both visual and text input, thinking and non-thinking modes, and dialogue and agent tasks. Supports automatic context caching, ToolCalls, JSON Mode, Partial Mode, and internet search.", context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.6'), cache_read_mtok=Decimal('0.1'), output_mtok=Decimal('3')), ), ModelInfo( id='kimi-k2.6', match=ClauseStartsWith(starts_with='kimi-k2.6'), name='Kimi K2.6', description="Kimi's most capable model with enhanced long-context coding stability, improved instruction compliance and self-correction capabilities. Native multimodal architecture supporting text, image, and video input, thinking and non-thinking modes, and agent tasks. Supports automatic context caching, ToolCalls, JSON Mode, Partial Mode, and internet search.", context_window=262144, prices=ModelPrice( input_mtok=Decimal('0.95'), cache_read_mtok=Decimal('0.16'), output_mtok=Decimal('4') ), ), ModelInfo( id='kimi-k2.7-code', match=ClauseEquals(equals='kimi-k2.7-code'), name='Kimi K2.7 Code', description="Kimi's most intelligent coding model, capable of completing programming tasks with higher success rates in long context. It features a native multimodal architecture that supports text, image, video input, thinking modes, dialogue, and agent tasks.", context_window=262144, price_comments='Ref: https://platform.kimi.ai/docs/pricing/chat-k27-code.md', prices=ModelPrice( input_mtok=Decimal('0.95'), cache_read_mtok=Decimal('0.19'), output_mtok=Decimal('4') ), ), ModelInfo( id='kimi-k2.7-code-highspeed', match=ClauseEquals(equals='kimi-k2.7-code-highspeed'), name='Kimi K2.7 Code HighSpeed', description='High-speed version of Kimi K2.7 Code, with output speed of approximately 180 tokens/sec and up to 260 tokens/sec in short context scenarios.', context_window=262144, price_comments='Ref: https://platform.kimi.ai/docs/pricing/chat-k27-code.md', prices=ModelPrice(input_mtok=Decimal('1.9'), cache_read_mtok=Decimal('0.38'), output_mtok=Decimal('8')), ), ModelInfo( id='kimi-k3', match=ClauseEquals(equals='kimi-k3'), name='Kimi K3', description="Kimi's flagship reasoning model with always-on thinking, native multimodal (image and video) input, tool use, and structured output. 2.8 trillion total parameters MoE.", context_window=1048576, price_comments='Ref: https://platform.kimi.ai/docs/pricing/chat-k3.md', prices=ModelPrice(input_mtok=Decimal('3'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15')), ), ModelInfo( id='moonshot-v1-128k', match=ClauseOr( or_=[ ClauseEquals(equals='moonshot-v1-128k'), ClauseEquals(equals='moonshot-v1-128k-vision-preview'), ] ), name='Moonshot V1 128K', context_window=131072, prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('5')), ), ModelInfo( id='moonshot-v1-32k', match=ClauseOr( or_=[ClauseEquals(equals='moonshot-v1-32k'), ClauseEquals(equals='moonshot-v1-32k-vision-preview')] ), name='Moonshot V1 32K', context_window=32768, prices=ModelPrice(input_mtok=Decimal('1'), output_mtok=Decimal('3')), ), ModelInfo( id='moonshot-v1-8k', match=ClauseOr( or_=[ClauseEquals(equals='moonshot-v1-8k'), ClauseEquals(equals='moonshot-v1-8k-vision-preview')] ), name='Moonshot V1 8K', context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('2')), ), ], ), Provider( id='novita', name='Novita', api_pattern='https://api\\.novita\\.ai', pricing_urls=['https://novita.ai/pricing'], models=[ ModelInfo( id='Sao10K/L3-8B-Stheno-v3.2', match=ClauseEquals(equals='Sao10K/L3-8B-Stheno-v3.2'), context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.05'), output_mtok=Decimal('0.05')), ), ModelInfo( id='cognitivecomputations/dolphin-mixtral-8x22b', match=ClauseEquals(equals='cognitivecomputations/dolphin-mixtral-8x22b'), prices=ModelPrice(input_mtok=Decimal('0.9'), output_mtok=Decimal('0.9')), ), ModelInfo( id='deepseek/deepseek-r1', match=ClauseEquals(equals='deepseek/deepseek-r1'), context_window=64000, prices=ModelPrice(input_mtok=Decimal('4'), output_mtok=Decimal('4')), ), ModelInfo( id='deepseek/deepseek-r1-distill-llama-70b', match=ClauseEquals(equals='deepseek/deepseek-r1-distill-llama-70b'), context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.8'), output_mtok=Decimal('0.8')), ), ModelInfo( id='deepseek/deepseek-r1-distill-llama-8b', match=ClauseEquals(equals='deepseek/deepseek-r1-distill-llama-8b'), prices=ModelPrice(input_mtok=Decimal('0.04'), output_mtok=Decimal('0.04')), ), ModelInfo( id='deepseek/deepseek-r1-distill-qwen-14b', match=ClauseEquals(equals='deepseek/deepseek-r1-distill-qwen-14b'), context_window=32768, prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.15')), ), ModelInfo( id='deepseek/deepseek-r1-distill-qwen-32b', match=ClauseEquals(equals='deepseek/deepseek-r1-distill-qwen-32b'), context_window=64000, prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('0.3')), ), ModelInfo( id='deepseek/deepseek_v3', match=ClauseEquals(equals='deepseek/deepseek_v3'), context_window=64000, prices=ModelPrice(input_mtok=Decimal('0.89'), output_mtok=Decimal('0.89')), ), ModelInfo( id='google/gemma-2-9b-it', match=ClauseEquals(equals='google/gemma-2-9b-it'), prices=ModelPrice(input_mtok=Decimal('0.08'), output_mtok=Decimal('0.08')), ), ModelInfo( id='gryphe/mythomax-l2-13b', match=ClauseEquals(equals='gryphe/mythomax-l2-13b'), context_window=4096, prices=ModelPrice(input_mtok=Decimal('0.09'), output_mtok=Decimal('0.09')), ), ModelInfo( id='jondurbin/airoboros-l2-70b', match=ClauseEquals(equals='jondurbin/airoboros-l2-70b'), prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('0.5')), ), ModelInfo( id='meta-llama/llama-3-70b-instruct', match=ClauseEquals(equals='meta-llama/llama-3-70b-instruct'), context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.51'), output_mtok=Decimal('0.74')), ), ModelInfo( id='meta-llama/llama-3-8b-instruct', match=ClauseEquals(equals='meta-llama/llama-3-8b-instruct'), context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.04'), output_mtok=Decimal('0.04')), ), ModelInfo( id='meta-llama/llama-3.1-70b-instruct', match=ClauseEquals(equals='meta-llama/llama-3.1-70b-instruct'), prices=ModelPrice(input_mtok=Decimal('0.34'), output_mtok=Decimal('0.39')), ), ModelInfo( id='meta-llama/llama-3.1-8b-instruct', match=ClauseOr( or_=[ ClauseEquals(equals='meta-llama/llama-3.1-8b-instruct'), ClauseEquals(equals='meta-llama/llama-3.1-8b-instruct-max'), ] ), context_window=16384, prices=ModelPrice(input_mtok=Decimal('0.05'), output_mtok=Decimal('0.05')), ), ModelInfo( id='meta-llama/llama-3.1-8b-instruct-bf16', match=ClauseEquals(equals='meta-llama/llama-3.1-8b-instruct-bf16'), prices=ModelPrice(input_mtok=Decimal('0.06'), output_mtok=Decimal('0.06')), ), ModelInfo( id='meta-llama/llama-3.2-11b-vision-instruct', match=ClauseEquals(equals='meta-llama/llama-3.2-11b-vision-instruct'), prices=ModelPrice(input_mtok=Decimal('0.06'), output_mtok=Decimal('0.06')), ), ModelInfo( id='meta-llama/llama-3.2-1b-instruct', match=ClauseEquals(equals='meta-llama/llama-3.2-1b-instruct'), context_window=131000, prices=ModelPrice(input_mtok=Decimal('0.02'), output_mtok=Decimal('0.02')), ), ModelInfo( id='meta-llama/llama-3.2-3b-instruct', match=ClauseEquals(equals='meta-llama/llama-3.2-3b-instruct'), context_window=32768, prices=ModelPrice(input_mtok=Decimal('0.03'), output_mtok=Decimal('0.05')), ), ModelInfo( id='meta-llama/llama-3.3-70b-instruct', match=ClauseEquals(equals='meta-llama/llama-3.3-70b-instruct'), context_window=12288, prices=ModelPrice(input_mtok=Decimal('0.39'), output_mtok=Decimal('0.39')), ), ModelInfo( id='microsoft/wizardlm-2-8x22b', match=ClauseEquals(equals='microsoft/wizardlm-2-8x22b'), context_window=65535, prices=ModelPrice(input_mtok=Decimal('0.62'), output_mtok=Decimal('0.62')), ), ModelInfo( id='mistralai/mistral-7b-instruct', match=ClauseEquals(equals='mistralai/mistral-7b-instruct'), prices=ModelPrice(input_mtok=Decimal('0.059'), output_mtok=Decimal('0.059')), ), ModelInfo( id='mistralai/mistral-nemo', match=ClauseEquals(equals='mistralai/mistral-nemo'), context_window=60288, prices=ModelPrice(input_mtok=Decimal('0.17'), output_mtok=Decimal('0.17')), ), ModelInfo( id='nousresearch/hermes-2-pro-llama-3-8b', match=ClauseEquals(equals='nousresearch/hermes-2-pro-llama-3-8b'), context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.14'), output_mtok=Decimal('0.14')), ), ModelInfo( id='nousresearch/nous-hermes-llama2-13b', match=ClauseEquals(equals='nousresearch/nous-hermes-llama2-13b'), context_window=4096, prices=ModelPrice(input_mtok=Decimal('0.17'), output_mtok=Decimal('0.17')), ), ModelInfo( id='openchat/openchat-7b', match=ClauseEquals(equals='openchat/openchat-7b'), context_window=4096, prices=ModelPrice(input_mtok=Decimal('0.06'), output_mtok=Decimal('0.06')), ), ModelInfo( id='qwen/qwen-2-7b-instruct', match=ClauseEquals(equals='qwen/qwen-2-7b-instruct'), context_window=32768, prices=ModelPrice(input_mtok=Decimal('0.054'), output_mtok=Decimal('0.054')), ), ModelInfo( id='qwen/qwen-2-vl-72b-instruct', match=ClauseEquals(equals='qwen/qwen-2-vl-72b-instruct'), context_window=32768, prices=ModelPrice(input_mtok=Decimal('0.45'), output_mtok=Decimal('0.45')), ), ModelInfo( id='qwen/qwen-2.5-72b-instruct', match=ClauseEquals(equals='qwen/qwen-2.5-72b-instruct'), context_window=32000, prices=ModelPrice(input_mtok=Decimal('0.38'), output_mtok=Decimal('0.4')), ), ModelInfo( id='sao10k/l3-70b-euryale-v2.1', match=ClauseEquals(equals='sao10k/l3-70b-euryale-v2.1'), context_window=8192, prices=ModelPrice(input_mtok=Decimal('1.48'), output_mtok=Decimal('1.48')), ), ModelInfo( id='sao10k/l3-8b-lunaris', match=ClauseEquals(equals='sao10k/l3-8b-lunaris'), context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.05'), output_mtok=Decimal('0.05')), ), ModelInfo( id='sao10k/l31-70b-euryale-v2.2', match=ClauseEquals(equals='sao10k/l31-70b-euryale-v2.2'), context_window=8192, prices=ModelPrice(input_mtok=Decimal('1.48'), output_mtok=Decimal('1.48')), ), ModelInfo( id='sophosympatheia/midnight-rose-70b', match=ClauseEquals(equals='sophosympatheia/midnight-rose-70b'), prices=ModelPrice(input_mtok=Decimal('0.8'), output_mtok=Decimal('0.8')), ), ModelInfo( id='teknium/openhermes-2.5-mistral-7b', match=ClauseEquals(equals='teknium/openhermes-2.5-mistral-7b'), context_window=4096, prices=ModelPrice(input_mtok=Decimal('0.17'), output_mtok=Decimal('0.17')), ), ], ), Provider( id='openai', name='OpenAI', api_pattern='https://api\\.openai\\.com', pricing_urls=[ 'https://developers.openai.com/api/docs/pricing', 'https://developers.openai.com/api/docs/guides/prompt-caching', 'https://platform.openai.com/docs/pricing', 'https://openai.com/api/pricing/', 'https://platform.openai.com/docs/models', 'https://help.openai.com/en/articles/7127956-how-much-does-gpt-4-cost', ], model_match=ClauseOr( or_=[ClauseStartsWith(starts_with='gpt-'), ClauseEquals(equals='whisper-1'), ClauseRegex(regex='^o[134]')] ), provider_match=ClauseContains(contains='openai'), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['input_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['input_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['output_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=True), ], api_flavor='responses', model_path='model', ), UsageExtractor( root=['response', 'usage'], mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=False), UsageExtractorMapping( path=['input_token_details', 'text_tokens'], dest='input_text_tokens', required=False ), UsageExtractorMapping( path=['input_token_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['input_token_details', 'image_tokens'], dest='input_image_tokens', required=False ), UsageExtractorMapping( path=['input_token_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['input_token_details', 'cached_tokens_details', 'text_tokens'], dest='cache_text_read_tokens', required=False, ), UsageExtractorMapping( path=['input_token_details', 'cached_tokens_details', 'audio_tokens'], dest='cache_audio_read_tokens', required=False, ), UsageExtractorMapping( path=['input_token_details', 'cached_tokens_details', 'image_tokens'], dest='cache_image_read_tokens', required=False, ), UsageExtractorMapping( path=['output_token_details', 'text_tokens'], dest='output_text_tokens', required=False ), UsageExtractorMapping( path=['output_token_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=False), ], api_flavor='realtime', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['input_tokens_details', 'text_tokens'], dest='input_text_tokens', required=True ), UsageExtractorMapping( path=['input_tokens_details', 'image_tokens'], dest='input_image_tokens', required=True ), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=True), UsageExtractorMapping( path=['output_tokens_details', 'text_tokens'], dest='output_text_tokens', required=False ), UsageExtractorMapping( path=['output_tokens_details', 'image_tokens'], dest='output_image_tokens', required=False ), ], api_flavor='images', model_path='model', ), UsageExtractor( root='usage', mappings=[UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True)], api_flavor='embeddings', model_path='model', ), ], models=[ ModelInfo( id='ada', match=ClauseOr(or_=[ClauseEquals(equals='ada'), ClauseEquals(equals='text-ada-001')]), prices=ModelPrice(input_mtok=Decimal('0.4'), output_mtok=Decimal('0.4')), ), ModelInfo( id='babbage', match=ClauseEquals(equals='babbage'), prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('0.5')), ), ModelInfo( id='chatgpt-4o-latest', match=ClauseEquals(equals='chatgpt-4o-latest'), name='ChatGPT-4o', description='OpenAI ChatGPT 4o is continually updated by OpenAI to point to the current version of GPT-4o used by ChatGPT. It therefore differs slightly from the API version of GPT-4o in that it has additional RLHF. It is intended for research and evaluation.', context_window=128000, prices=ModelPrice(input_mtok=Decimal('5'), output_mtok=Decimal('15')), ), ModelInfo( id='codex-mini', match=ClauseOr(or_=[ClauseEquals(equals='codex-mini'), ClauseEquals(equals='codex-mini-latest')]), name='Codex Mini', description='codex-mini-latest is a fine-tuned version of o4-mini specifically for use in Codex CLI. For direct use in the API, we recommend starting with gpt-4.1.', context_window=200000, prices=ModelPrice( input_mtok=Decimal('1.5'), cache_read_mtok=Decimal('0.375'), output_mtok=Decimal('6') ), ), ModelInfo( id='computer-use', match=ClauseStartsWith(starts_with='computer-use'), name='Computer use', context_window=8192, prices=ModelPrice(input_mtok=Decimal('3'), output_mtok=Decimal('12')), ), ModelInfo( id='curie', match=ClauseOr(or_=[ClauseEquals(equals='curie'), ClauseEquals(equals='text-curie-001')]), prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('2')), ), ModelInfo( id='davinci', match=ClauseOr(or_=[ClauseEquals(equals='davinci'), ClauseEquals(equals='text-davinci-001')]), prices=ModelPrice(input_mtok=Decimal('20'), output_mtok=Decimal('20')), ), ModelInfo( id='ft:gpt-3.5-turbo-', match=ClauseStartsWith(starts_with='ft:gpt-3.5-turbo'), description='GPT-3.5 Turbo fine tuned.', prices=ModelPrice(input_mtok=Decimal('3'), output_mtok=Decimal('6')), ), ModelInfo( id='ft:gpt-4o', match=ClauseStartsWith(starts_with='ft:gpt-4o-2024-'), description='GPT-4o fine tuned.', prices=ModelPrice(input_mtok=Decimal('3.75'), output_mtok=Decimal('15')), ), ModelInfo( id='ft:gpt-4o-mini', match=ClauseStartsWith(starts_with='ft:gpt-4o-mini-2024-'), description='GPT-4o Mini fine tuned.', prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('1.2')), ), ModelInfo( id='gpt-3.5-0301', match=ClauseOr(or_=[ClauseEquals(equals='gpt-3.5-turbo-0301'), ClauseEquals(equals='gpt-3.5-0301')]), prices=ModelPrice(input_mtok=Decimal('1.5'), output_mtok=Decimal('2')), ), ModelInfo( id='gpt-3.5-turbo', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-3.5-turbo'), ClauseEquals(equals='gpt-35-turbo'), ClauseEquals(equals='gpt-3.5-turbo-0125'), ] ), name='gpt 3.5 turbo', description='GPT-3.5 Turbo offers a balance between cost and performance.', context_window=16385, prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('1.5')), ), ModelInfo( id='gpt-3.5-turbo-0613', match=ClauseEquals(equals='gpt-3.5-turbo-0613'), context_window=16385, prices=ModelPrice(input_mtok=Decimal('1.5'), output_mtok=Decimal('2')), ), ModelInfo( id='gpt-3.5-turbo-1106', match=ClauseEquals(equals='gpt-3.5-turbo-1106'), context_window=16385, prices=ModelPrice(input_mtok=Decimal('1'), output_mtok=Decimal('2')), ), ModelInfo( id='gpt-3.5-turbo-16k', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-3.5-turbo-16k'), ClauseEquals(equals='gpt-3.5-turbo-16k-0613'), ClauseEquals(equals='gpt-35-turbo-16k-0613'), ClauseEquals(equals='gpt-35-turbo-16k'), ] ), name='GPT-3.5 Turbo 16k', description='This model offers four times the context length of gpt-3.5-turbo, allowing it to support approximately 20 pages of text in a single request at a higher cost. Training data: up to Sep 2021.', context_window=16385, prices=ModelPrice(input_mtok=Decimal('3'), output_mtok=Decimal('4')), ), ModelInfo( id='gpt-3.5-turbo-instruct', match=ClauseOr( or_=[ ClauseStartsWith(starts_with='gpt-3.5-turbo-instruct'), ClauseEquals(equals='gpt-3.5-turbo-instruct-0914'), ] ), name='gpt 3.5 turbo instruct', description='GPT-3.5 Turbo offers a balance between cost and performance.', context_window=16385, prices=ModelPrice(input_mtok=Decimal('1.5'), output_mtok=Decimal('2')), ), ModelInfo( id='gpt-4', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-4'), ClauseEquals(equals='gpt-4-0314'), ClauseEquals(equals='gpt-4-0613'), ClauseStartsWith(starts_with='ft:gpt-4-0'), ] ), name='gpt 4', description='GPT-4 is the latest and most advanced model in the GPT series, demonstrating sophisticated capabilities in complex reasoning, theory of mind, and narrative understanding.', context_window=8192, prices=ModelPrice(input_mtok=Decimal('30'), output_mtok=Decimal('60')), ), ModelInfo( id='gpt-4-32k', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-4-32k'), ClauseEquals(equals='gpt-4-32k-0314'), ClauseEquals(equals='gpt-4-32k-0613'), ] ), name='gpt 4', description='GPT-4 is the latest and most advanced model in the GPT series, demonstrating sophisticated capabilities in complex reasoning, theory of mind, and narrative understanding.', context_window=32000, price_comments='see https://help.openai.com/en/articles/7127956-how-much-does-gpt-4-cost', prices=ModelPrice(input_mtok=Decimal('60'), output_mtok=Decimal('120')), ), ModelInfo( id='gpt-4-turbo', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-4-turbo'), ClauseEquals(equals='gpt-4-turbo-2024-04-09'), ClauseEquals(equals='gpt-4-turbo-0125-preview'), ClauseEquals(equals='gpt-4-0125-preview'), ClauseEquals(equals='gpt-4-1106-preview'), ClauseEquals(equals='gpt-4-turbo-preview'), ] ), name='gpt 4 turbo', description='GPT-4 Turbo offers a balance between cost and performance.', context_window=128000, prices=ModelPrice(input_mtok=Decimal('10'), output_mtok=Decimal('30')), ), ModelInfo( id='gpt-4-vision-preview', match=ClauseOr( or_=[ClauseEquals(equals='gpt-4-vision-preview'), ClauseEquals(equals='gpt-4-1106-vision-preview')] ), name='gpt 4 vision', description='GPT-4 Vision is a model that offers a balance between cost and performance.', context_window=128000, prices=ModelPrice(input_mtok=Decimal('10'), output_mtok=Decimal('30')), ), ModelInfo( id='gpt-4.1', match=ClauseOr(or_=[ClauseEquals(equals='gpt-4.1'), ClauseEquals(equals='gpt-4.1-2025-04-14')]), name='gpt 4.1', description="GPT-4.1 is OpenAI's latest flagship model, offering major improvements in coding, instruction following, and long context understanding with up to 1 million tokens of context.", context_window=1000000, prices=ModelPrice( input_mtok=Decimal('2'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('8'), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-4.1-mini', match=ClauseOr( or_=[ClauseEquals(equals='gpt-4.1-mini'), ClauseEquals(equals='gpt-4.1-mini-2025-04-14')] ), name='gpt 4.1 mini', description='GPT-4.1 Mini is a significant leap in small model performance, matching or exceeding GPT-4o in many benchmarks while reducing latency by nearly half and cost by 83%.', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('0.4'), cache_read_mtok=Decimal('0.1'), output_mtok=Decimal('1.6'), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-4.1-nano', match=ClauseOr( or_=[ClauseEquals(equals='gpt-4.1-nano'), ClauseEquals(equals='gpt-4.1-nano-2025-04-14')] ), name='gpt 4.1 nano', description="GPT-4.1 Nano is OpenAI's fastest and cheapest model, delivering exceptional performance for its size with a 1 million token context window, ideal for classification and autocompletion tasks.", context_window=1000000, prices=ModelPrice( input_mtok=Decimal('0.1'), cache_read_mtok=Decimal('0.025'), output_mtok=Decimal('0.4') ), ), ModelInfo( id='gpt-4.5-preview', match=ClauseStartsWith(starts_with='gpt-4.5-preview'), name='GPT-4.5 (Preview)', description="GPT-4.5 (Preview) is a research preview of OpenAI's latest language model, designed to advance capabilities in reasoning, creativity, and multi-turn conversation. It builds on previous iterations with improvements in world knowledge, contextual coherence, and the ability to follow user intent more effectively.", prices=ModelPrice( input_mtok=Decimal('75'), cache_read_mtok=Decimal('37.5'), output_mtok=Decimal('150') ), ), ModelInfo( id='gpt-4o', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-4o'), ClauseEquals(equals='gpt-4o-2024-08-06'), ClauseEquals(equals='gpt-4o-2024-11-20'), ] ), name='gpt 4o', description='GPT-4 Optimized (GPT-4o) is designed for high performance in reasoning, creativity, and technical tasks while maintaining consistent output quality.', context_window=128000, prices=ModelPrice( input_mtok=Decimal('2.5'), cache_read_mtok=Decimal('1.25'), output_mtok=Decimal('10'), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-4o-2024-05-13', match=ClauseEquals(equals='gpt-4o-2024-05-13'), name='gpt 4o (2024-05-13)', description='Original GPT-4o snapshot, retained at its launch rates, which are higher than later gpt-4o snapshots and have no cached-input discount.', context_window=128000, price_comments='Split out of the shared gpt-4o entry: OpenAI lists this snapshot separately at $5/$15 with no cached-input price. Ref: https://developers.openai.com/api/docs/pricing.md', prices=ModelPrice( input_mtok=Decimal('5'), output_mtok=Decimal('15'), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-4o-audio-preview', match=ClauseStartsWith(starts_with='gpt-4o-audio-preview'), name='gpt 4o audio preview', description='Audio model for gpt-4o', context_window=128000, prices=ModelPrice(input_mtok=Decimal('2.5'), output_mtok=Decimal('10')), ), ModelInfo( id='gpt-4o-mini', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-4o-mini'), ClauseEquals(equals='gpt-4o-mini-2024-07-18'), ClauseEquals(equals='gpt-4o-mini-search-preview'), ClauseEquals(equals='gpt-4o-mini-search-preview-2025-03-11'), ] ), name='gpt 4o mini', description='GPT-4o Mini is a cost-optimized variant of GPT-4o, designed for high-efficiency processing while maintaining strong performance. It excels in rapid inference and resource-efficient operations, making it ideal for production deployments requiring a balance of cost and capability.', context_window=128000, prices=ModelPrice( input_mtok=Decimal('0.15'), cache_read_mtok=Decimal('0.075'), output_mtok=Decimal('0.6'), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-4o-mini-2024-07-18.ft-', match=ClauseStartsWith(starts_with='gpt-4o-mini-2024-07-18.ft-'), description='GPT-4o Mini fine tuned.', prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('1.2')), ), ModelInfo( id='gpt-4o-mini-audio-preview', match=ClauseStartsWith(starts_with='gpt-4o-mini-audio'), name='gpt 4o mini audio preview', description='Audio model for gpt-4o mini', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.6')), ), ModelInfo( id='gpt-4o-mini-realtime-preview', match=ClauseStartsWith(starts_with='gpt-4o-mini-realtime'), context_window=16000, prices=ModelPrice( input_mtok=Decimal('0.6'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('2.4'), input_audio_mtok=Decimal('10'), cache_audio_read_mtok=Decimal('0.3'), output_audio_mtok=Decimal('20'), ), ), ModelInfo( id='gpt-4o-mini-transcribe', match=ClauseEquals(equals='gpt-4o-mini-transcribe'), context_window=16000, prices=ModelPrice(input_mtok=Decimal('1.25'), output_mtok=Decimal('5'), input_audio_mtok=Decimal('3')), ), ModelInfo( id='gpt-4o-mini-tts', match=ClauseEquals(equals='gpt-4o-mini-tts'), prices=ModelPrice(input_mtok=Decimal('0.6'), output_mtok=Decimal('12')), ), ModelInfo( id='gpt-4o-realtime-preview', match=ClauseStartsWith(starts_with='gpt-4o-realtime'), context_window=32000, prices=ModelPrice( input_mtok=Decimal('5'), cache_read_mtok=Decimal('2.5'), output_mtok=Decimal('20'), input_audio_mtok=Decimal('40'), cache_audio_read_mtok=Decimal('2.5'), output_audio_mtok=Decimal('80'), ), ), ModelInfo( id='gpt-4o-search-preview', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-4o-search-preview'), ClauseEquals(equals='gpt-4o-search-preview-2025-03-11'), ] ), name='GPT-4o Search Preview', description='GPT-4o Search Previewis a specialized model for web search in Chat Completions. It is trained to understand and execute web search queries.', context_window=128000, prices=ModelPrice(input_mtok=Decimal('2.5'), output_mtok=Decimal('10')), ), ModelInfo( id='gpt-4o-transcribe', match=ClauseOr( or_=[ClauseEquals(equals='gpt-4o-transcribe'), ClauseEquals(equals='gpt-4o-transcribe-diarize')] ), context_window=16000, price_comments='See https://developers.openai.com/api/docs/models/gpt-4o-transcribe-diarize.', prices=ModelPrice(input_mtok=Decimal('2.5'), output_mtok=Decimal('10'), input_audio_mtok=Decimal('6')), ), ModelInfo( id='gpt-4o:extended', match=ClauseEquals(equals='gpt-4o:extended'), name='GPT-4o (extended)', description='GPT-4o ("o" for "omni") is OpenAI\'s latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of GPT-4 Turbo while being twice as fast and 50% more cost-effective. GPT-4o also offers improved performance in processing non-English languages and enhanced visual capabilities.', prices=ModelPrice(input_mtok=Decimal('6'), output_mtok=Decimal('18')), ), ModelInfo( id='gpt-5', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-5'), ClauseEquals(equals='gpt-5-2025-08-07'), ClauseEquals(equals='gpt-5-chat'), ClauseEquals(equals='gpt-5-chat-latest'), ClauseEquals(equals='gpt-5-codex'), ] ), name='GPT-5', description="GPT-5 is OpenAI's flagship model for coding, reasoning, and agentic tasks across domains.", context_window=400000, prices=ModelPrice( input_mtok=Decimal('1.25'), cache_read_mtok=Decimal('0.125'), output_mtok=Decimal('10'), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-5-image', match=ClauseEquals(equals='gpt-5-image'), price_comments='Seen on OpenRouter before OpenAI', prices=ModelPrice(input_mtok=Decimal('10'), cache_read_mtok=Decimal('1.25'), output_mtok=Decimal('10')), ), ModelInfo( id='gpt-5-image-mini', match=ClauseEquals(equals='gpt-5-image-mini'), price_comments='Seen on OpenRouter before OpenAI', prices=ModelPrice(input_mtok=Decimal('2.5'), cache_read_mtok=Decimal('0.25'), output_mtok=Decimal('2')), ), ModelInfo( id='gpt-5-mini', match=ClauseOr(or_=[ClauseEquals(equals='gpt-5-mini'), ClauseEquals(equals='gpt-5-mini-2025-08-07')]), name='GPT-5 mini', description="GPT-5 mini is a faster, more cost-efficient version of GPT-5. It's great for well-defined tasks and precise prompts.", context_window=400000, prices=ModelPrice( input_mtok=Decimal('0.25'), cache_read_mtok=Decimal('0.025'), output_mtok=Decimal('2'), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-5-nano', match=ClauseOr(or_=[ClauseEquals(equals='gpt-5-nano'), ClauseStartsWith(starts_with='gpt-5-nano-')]), name='GPT-5 nano', description="GPT-5 Nano is OpenAI's fastest, cheapest version of GPT-5. It's great for summarization and classification tasks.", context_window=400000, prices=ModelPrice( input_mtok=Decimal('0.05'), cache_read_mtok=Decimal('0.005'), output_mtok=Decimal('0.4'), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-5-pro', match=ClauseOr(or_=[ClauseEquals(equals='gpt-5-pro'), ClauseEquals(equals='gpt-5-pro-2025-10-06')]), context_window=400000, prices=ModelPrice( input_mtok=Decimal('15'), output_mtok=Decimal('120'), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-5.1', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-5.1'), ClauseEquals(equals='gpt-5.1-2025-11-13'), ClauseEquals(equals='gpt-5.1-codex'), ClauseEquals(equals='gpt-5.1-codex-max'), ClauseEquals(equals='gpt-5.1-chat'), ClauseEquals(equals='gpt-5.1-chat-latest'), ClauseEquals(equals='gpt-5-1'), ClauseEquals(equals='gpt-5-1-2025-11-13'), ClauseEquals(equals='gpt-5-1-codex'), ClauseEquals(equals='gpt-5-1-codex-max'), ClauseEquals(equals='gpt-5-1-chat'), ClauseEquals(equals='gpt-5-1-chat-latest'), ] ), name='GPT-5.1', description='The best model for coding and agentic tasks across industries', context_window=400000, prices=ModelPrice( input_mtok=Decimal('1.25'), cache_read_mtok=Decimal('0.125'), output_mtok=Decimal('10'), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-5.1-codex-mini', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-5.1-codex-mini'), ClauseEquals(equals='gpt-5.1-mini'), ClauseEquals(equals='gpt-5-1-codex-mini'), ClauseEquals(equals='gpt-5-1-mini'), ] ), name='GPT-5.1 Codex Mini', context_window=400000, prices=ModelPrice( input_mtok=Decimal('0.25'), cache_read_mtok=Decimal('0.025'), output_mtok=Decimal('2') ), ), ModelInfo( id='gpt-5.2', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-5.2'), ClauseEquals(equals='gpt-5.2-2025-12-11'), ClauseEquals(equals='gpt-5-2'), ClauseEquals(equals='gpt-5-2-2025-12-11'), ClauseEquals(equals='gpt-5.2-chat'), ClauseEquals(equals='gpt-5.2-chat-latest'), ClauseEquals(equals='gpt-5-2-chat'), ClauseEquals(equals='gpt-5-2-chat-latest'), ClauseEquals(equals='gpt-5.2-codex'), ClauseEquals(equals='gpt-5-2-codex'), ] ), name='GPT-5.2', description='The best model for coding and agentic tasks across industries', context_window=400000, prices=ModelPrice( input_mtok=Decimal('1.75'), cache_read_mtok=Decimal('0.175'), output_mtok=Decimal('14'), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-5.2-pro', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-5.2-pro'), ClauseEquals(equals='gpt-5.2-pro-2025-12-11'), ClauseEquals(equals='gpt-5-2-pro-2025-12-11'), ] ), description='Version of GPT-5.2 that produces smarter and more precise responses.', context_window=400000, prices=ModelPrice( input_mtok=Decimal('21'), output_mtok=Decimal('168'), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-5.3', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-5.3'), ClauseEquals(equals='gpt-5-3'), ClauseEquals(equals='gpt-5.3-chat'), ClauseEquals(equals='gpt-5.3-chat-latest'), ClauseEquals(equals='gpt-5-3-chat'), ClauseEquals(equals='gpt-5-3-chat-latest'), ] ), name='GPT-5.3 Chat', description='GPT-5.3 Instant model used in ChatGPT', context_window=128000, prices=ModelPrice( input_mtok=Decimal('1.75'), cache_read_mtok=Decimal('0.175'), output_mtok=Decimal('14') ), ), ModelInfo( id='gpt-5.3-codex', match=ClauseOr(or_=[ClauseEquals(equals='gpt-5.3-codex'), ClauseEquals(equals='gpt-5-3-codex')]), name='GPT-5.3-Codex', description='The most capable agentic coding model', context_window=400000, prices=ModelPrice( input_mtok=Decimal('1.75'), cache_read_mtok=Decimal('0.175'), output_mtok=Decimal('14') ), ), ModelInfo( id='gpt-5.4', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-5.4'), ClauseEquals(equals='gpt-5.4-2026-03-05'), ClauseEquals(equals='gpt-5-4'), ClauseEquals(equals='gpt-5-4-2026-03-05'), ] ), name='GPT-5.4', description="OpenAI's most capable model with a 1.05M token context window.", context_window=1050000, price_comments='OpenAI lists the standard rates for prompts with <272K input tokens, so the long-context rates begin at exactly 272K. Tier starts are encoded as 271999 because the pricing engines select a tier when the token count is greater than start. Ref: https://developers.openai.com/api/docs/pricing', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('2.5'), tiers=[Tier(start=271999, price=Decimal('5'))]), cache_read_mtok=TieredPrices( base=Decimal('0.25'), tiers=[Tier(start=271999, price=Decimal('0.5'))] ), output_mtok=TieredPrices(base=Decimal('15'), tiers=[Tier(start=271999, price=Decimal('22.5'))]), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-5.4-image-2', match=ClauseEquals(equals='gpt-5.4-image-2'), name='GPT-5.4 Image 2', description="GPT-5.4 Image 2 combines OpenAI's GPT-5.4 model with state-of-the-art image generation capabilities from GPT Image 2. It enables rich multimodal workflows across reasoning, coding, and image generation.", price_comments='Imported from OpenRouter pricing; verify against OpenAI pricing when native API pricing is published.', prices=ModelPrice(input_mtok=Decimal('8'), cache_read_mtok=Decimal('2'), output_mtok=Decimal('15')), ), ModelInfo( id='gpt-5.4-mini', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-5.4-mini'), ClauseEquals(equals='gpt-5.4-mini-2026-03-17'), ClauseEquals(equals='gpt-5-4-mini'), ClauseEquals(equals='gpt-5-4-mini-2026-03-17'), ] ), name='GPT-5.4 mini', description='Our strongest mini model yet for coding, computer use, and subagents.', context_window=400000, prices=ModelPrice( input_mtok=Decimal('0.75'), cache_read_mtok=Decimal('0.075'), output_mtok=Decimal('4.5'), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-5.4-nano', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-5.4-nano'), ClauseEquals(equals='gpt-5.4-nano-2026-03-17'), ClauseEquals(equals='gpt-5-4-nano'), ClauseEquals(equals='gpt-5-4-nano-2026-03-17'), ] ), name='GPT-5.4 nano', description='Our cheapest GPT-5.4-class model for simple high-volume tasks.', context_window=400000, prices=ModelPrice( input_mtok=Decimal('0.2'), cache_read_mtok=Decimal('0.02'), output_mtok=Decimal('1.25'), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-5.4-pro', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-5.4-pro'), ClauseEquals(equals='gpt-5.4-pro-2026-03-05'), ClauseEquals(equals='gpt-5-4-pro'), ClauseEquals(equals='gpt-5-4-pro-2026-03-05'), ] ), name='GPT-5.4 Pro', description='Version of GPT-5.4 that produces smarter and more precise responses.', context_window=1050000, price_comments='OpenAI lists the standard rates for prompts with <272K input tokens, so the long-context rates begin at exactly 272K. Tier starts are encoded as 271999 because the pricing engines select a tier when the token count is greater than start. Ref: https://developers.openai.com/api/docs/pricing', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('30'), tiers=[Tier(start=271999, price=Decimal('60'))]), output_mtok=TieredPrices(base=Decimal('180'), tiers=[Tier(start=271999, price=Decimal('270'))]), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-5.5', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-5.5'), ClauseEquals(equals='gpt-5.5-2026-04-23'), ClauseEquals(equals='gpt-5.5-2026-04-24'), ClauseEquals(equals='gpt-5-5'), ClauseEquals(equals='gpt-5-5-2026-04-23'), ClauseEquals(equals='gpt-5-5-2026-04-24'), ClauseEquals(equals='gpt-5.5-chat'), ClauseEquals(equals='gpt-5.5-chat-latest'), ClauseEquals(equals='gpt-5-5-chat'), ClauseEquals(equals='gpt-5-5-chat-latest'), ClauseEquals(equals='gpt-5.5-codex'), ClauseEquals(equals='gpt-5-5-codex'), ] ), name='GPT-5.5', description='The best model for coding and agentic tasks across industries', context_window=1000000, price_comments='OpenAI lists the standard rates for prompts with <272K input tokens, so the long-context rates begin at exactly 272K and bill the full request at 2x input and cached input and 1.5x output. Tier starts are encoded as 271999 because the pricing engines select a tier when the token count is greater than start. Ref: https://developers.openai.com/api/docs/models/gpt-5.5', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('5'), tiers=[Tier(start=271999, price=Decimal('10'))]), cache_read_mtok=TieredPrices(base=Decimal('0.5'), tiers=[Tier(start=271999, price=Decimal('1'))]), output_mtok=TieredPrices(base=Decimal('30'), tiers=[Tier(start=271999, price=Decimal('45'))]), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-5.5-pro', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-5.5-pro'), ClauseEquals(equals='gpt-5.5-pro-2026-04-23'), ClauseEquals(equals='gpt-5-5-pro'), ClauseEquals(equals='gpt-5-5-pro-2026-04-23'), ] ), name='GPT-5.5 Pro', description='Version of GPT-5.5 that produces smarter and more precise responses.', context_window=1000000, price_comments='OpenAI lists the standard rates for prompts with <272K input tokens, so the long-context rates begin at exactly 272K and bill the full request at 2x input and 1.5x output. Tier starts are encoded as 271999 because the pricing engines select a tier when the token count is greater than start. Ref: https://developers.openai.com/api/docs/pricing', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('30'), tiers=[Tier(start=271999, price=Decimal('60'))]), output_mtok=TieredPrices(base=Decimal('180'), tiers=[Tier(start=271999, price=Decimal('270'))]), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-5.6-luna', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-5.6-luna'), ClauseEquals(equals='gpt-5-6-luna'), ClauseRegex(regex='^gpt-5\\.6-luna-\\d{4}-\\d{2}-\\d{2}$'), ClauseRegex(regex='^gpt-5-6-luna-\\d{4}-\\d{2}-\\d{2}$'), ] ), name='GPT-5.6 Luna', description='GPT-5.6 model optimized for cost-sensitive workloads.', context_window=1050000, price_comments='Cache writes are billed at 1.25x the uncached input rate. OpenAI lists the standard rates for prompts with <272K input tokens, so the 2x input and 1.5x output long-context rates begin at exactly 272K. Tier starts are encoded as 271999 because the pricing engines select a tier when the token count is greater than start. OpenAI reduced Luna prices by 80% on 2026-07-30. Refs: https://developers.openai.com/api/docs/models/gpt-5.6-luna, https://developers.openai.com/api/docs/changelog', prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('1'), tiers=[Tier(start=271999, price=Decimal('2'))]), cache_write_mtok=TieredPrices( base=Decimal('1.25'), tiers=[Tier(start=271999, price=Decimal('2.5'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.1'), tiers=[Tier(start=271999, price=Decimal('0.2'))] ), output_mtok=TieredPrices(base=Decimal('6'), tiers=[Tier(start=271999, price=Decimal('9'))]), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2026, 7, 30)), prices=ModelPrice( input_mtok=TieredPrices( base=Decimal('0.2'), tiers=[Tier(start=271999, price=Decimal('0.4'))] ), cache_write_mtok=TieredPrices( base=Decimal('0.25'), tiers=[Tier(start=271999, price=Decimal('0.5'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.02'), tiers=[Tier(start=271999, price=Decimal('0.04'))] ), output_mtok=TieredPrices( base=Decimal('1.2'), tiers=[Tier(start=271999, price=Decimal('1.8'))] ), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ], ), ModelInfo( id='gpt-5.6-sol', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-5.6-sol'), ClauseEquals(equals='gpt-5-6-sol'), ClauseEquals(equals='gpt-5.6'), ClauseEquals(equals='gpt-5-6'), ClauseRegex(regex='^gpt-5\\.6-sol-\\d{4}-\\d{2}-\\d{2}$'), ClauseRegex(regex='^gpt-5-6-sol-\\d{4}-\\d{2}-\\d{2}$'), ] ), name='GPT-5.6 Sol', description='Frontier model for complex professional work.', context_window=1050000, price_comments='Cache writes are billed at 1.25x the uncached input rate. OpenAI lists the standard rates for prompts with <272K input tokens, so the 2x input and 1.5x output long-context rates begin at exactly 272K. Tier starts are encoded as 271999 because the pricing engines select a tier when the token count is greater than start. OpenAI reduced Sol input prices by 20% and output prices by 33% on 2026-08-21. Refs: https://developers.openai.com/api/docs/models/gpt-5.6-sol, https://developers.openai.com/api/docs/changelog', prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('5'), tiers=[Tier(start=271999, price=Decimal('10'))]), cache_write_mtok=TieredPrices( base=Decimal('6.25'), tiers=[Tier(start=271999, price=Decimal('12.5'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.5'), tiers=[Tier(start=271999, price=Decimal('1'))] ), output_mtok=TieredPrices( base=Decimal('30'), tiers=[Tier(start=271999, price=Decimal('45'))] ), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2026, 8, 21)), prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('4'), tiers=[Tier(start=271999, price=Decimal('8'))]), cache_write_mtok=TieredPrices( base=Decimal('5'), tiers=[Tier(start=271999, price=Decimal('10'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.4'), tiers=[Tier(start=271999, price=Decimal('0.8'))] ), output_mtok=TieredPrices( base=Decimal('20'), tiers=[Tier(start=271999, price=Decimal('30'))] ), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ], ), ModelInfo( id='gpt-5.6-terra', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-5.6-terra'), ClauseEquals(equals='gpt-5-6-terra'), ClauseRegex(regex='^gpt-5\\.6-terra-\\d{4}-\\d{2}-\\d{2}$'), ClauseRegex(regex='^gpt-5-6-terra-\\d{4}-\\d{2}-\\d{2}$'), ] ), name='GPT-5.6 Terra', description='GPT-5.6 model that balances intelligence and cost.', context_window=1050000, price_comments='Cache writes are billed at 1.25x the uncached input rate. OpenAI lists the standard rates for prompts with <272K input tokens, so the 2x input and 1.5x output long-context rates begin at exactly 272K. Tier starts are encoded as 271999 because the pricing engines select a tier when the token count is greater than start. OpenAI reduced Terra prices by 20% on 2026-07-30. Refs: https://developers.openai.com/api/docs/models/gpt-5.6-terra, https://developers.openai.com/api/docs/changelog', prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=TieredPrices( base=Decimal('2.5'), tiers=[Tier(start=271999, price=Decimal('5'))] ), cache_write_mtok=TieredPrices( base=Decimal('3.125'), tiers=[Tier(start=271999, price=Decimal('6.25'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.25'), tiers=[Tier(start=271999, price=Decimal('0.5'))] ), output_mtok=TieredPrices( base=Decimal('15'), tiers=[Tier(start=271999, price=Decimal('22.5'))] ), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2026, 7, 30)), prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('2'), tiers=[Tier(start=271999, price=Decimal('4'))]), cache_write_mtok=TieredPrices( base=Decimal('2.5'), tiers=[Tier(start=271999, price=Decimal('5'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.2'), tiers=[Tier(start=271999, price=Decimal('0.4'))] ), output_mtok=TieredPrices( base=Decimal('12'), tiers=[Tier(start=271999, price=Decimal('18'))] ), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ], ), ModelInfo( id='gpt-6-astra', match=ClauseOr( or_=[ClauseEquals(equals='gpt-6-astra'), ClauseRegex(regex='^gpt-6-astra-\\d{4}-\\d{2}-\\d{2}$')] ), name='GPT-6 Astra', description='Our most capable model, built for the hardest end-to-end work.', context_window=1050000, price_comments='Cache writes are billed at 1.25x the uncached input rate. OpenAI lists the standard rates for prompts with <272K input tokens, so the 2x input and 1.5x output long-context rates begin at exactly 272K. Tier starts are encoded as 271999 because the pricing engines select a tier when the token count is greater than start. Refs: https://developers.openai.com/api/docs/models/gpt-6-astra, https://developers.openai.com/api/docs/pricing', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('10'), tiers=[Tier(start=271999, price=Decimal('20'))]), cache_write_mtok=TieredPrices( base=Decimal('12.5'), tiers=[Tier(start=271999, price=Decimal('25'))] ), cache_read_mtok=TieredPrices(base=Decimal('1'), tiers=[Tier(start=271999, price=Decimal('2'))]), output_mtok=TieredPrices(base=Decimal('50'), tiers=[Tier(start=271999, price=Decimal('75'))]), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-6-luna', match=ClauseOr( or_=[ClauseEquals(equals='gpt-6-luna'), ClauseRegex(regex='^gpt-6-luna-\\d{4}-\\d{2}-\\d{2}$')] ), name='GPT-6 Luna', description='Efficient model for focused, high-volume tasks.', context_window=1050000, price_comments='Cache reads cost 10% of input; cache writes cost 1.25x. Prompts with more than 272K input tokens cost 2x for input and cache tokens and 1.5x for output. Tier starts use 272000 because the pricing engines select a tier when token count exceeds start. Ref: https://developers.openai.com/api/docs/models/gpt-6-luna', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('0.1'), tiers=[Tier(start=272000, price=Decimal('0.2'))]), cache_write_mtok=TieredPrices( base=Decimal('0.125'), tiers=[Tier(start=272000, price=Decimal('0.25'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.01'), tiers=[Tier(start=272000, price=Decimal('0.02'))] ), output_mtok=TieredPrices(base=Decimal('0.5'), tiers=[Tier(start=272000, price=Decimal('0.75'))]), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-6-sol', match=ClauseOr( or_=[ClauseEquals(equals='gpt-6-sol'), ClauseRegex(regex='^gpt-6-sol-\\d{4}-\\d{2}-\\d{2}$')] ), name='GPT-6 Sol', description='Model for complex coding and agentic workflows.', context_window=1050000, price_comments='Cache reads cost 10% of input; cache writes cost 1.25x. Prompts with more than 272K input tokens cost 2x for input and cache tokens and 1.5x for output. Tier starts use 272000 because the pricing engines select a tier when token count exceeds start. Ref: https://developers.openai.com/api/docs/models/gpt-6-sol', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('2'), tiers=[Tier(start=272000, price=Decimal('4'))]), cache_write_mtok=TieredPrices(base=Decimal('2.5'), tiers=[Tier(start=272000, price=Decimal('5'))]), cache_read_mtok=TieredPrices(base=Decimal('0.2'), tiers=[Tier(start=272000, price=Decimal('0.4'))]), output_mtok=TieredPrices(base=Decimal('10'), tiers=[Tier(start=272000, price=Decimal('15'))]), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='gpt-audio', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-audio'), ClauseEquals(equals='gpt-audio-2025-08-28'), ClauseEquals(equals='gpt-audio-1.5'), ] ), name='GPT Audio', description="The gpt-audio model is OpenAI's first generally available audio model. The new snapshot features an upgraded decoder for more natural-sounding voices and maintains better voice consistency.", context_window=128000, prices=ModelPrice( input_mtok=Decimal('2.5'), output_mtok=Decimal('10'), input_audio_mtok=Decimal('32'), output_audio_mtok=Decimal('64'), ), ), ModelInfo( id='gpt-audio-mini', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-audio-mini'), ClauseEquals(equals='gpt-audio-mini-2025-10-06'), ClauseEquals(equals='gpt-audio-mini-2025-12-15'), ] ), name='GPT Audio Mini', description='A cost-efficient version of GPT Audio. The new snapshot features an upgraded decoder for more natural sounding voices and maintains better voice consistency.', context_window=128000, prices=ModelPrice( input_mtok=Decimal('0.6'), output_mtok=Decimal('2.4'), input_audio_mtok=Decimal('10'), output_audio_mtok=Decimal('20'), ), ), ModelInfo( id='gpt-chat-latest', match=ClauseEquals(equals='gpt-chat-latest'), name='GPT Chat Latest', description="GPT Chat Latest points to OpenAI's stable API alias `chat-latest` that always resolves to the latest Instant chat model used in ChatGPT.", price_comments='Imported from OpenRouter pricing; verify against OpenAI pricing when native API pricing is published.', prices=ModelPrice(input_mtok=Decimal('5'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('30')), ), ModelInfo( id='gpt-image-1', match=ClauseEquals(equals='gpt-image-1'), name='GPT Image 1', description="OpenAI's previous image generation model.", price_comments='See https://developers.openai.com/api/docs/models/gpt-image-1.', prices=ModelPrice( input_mtok=Decimal('5'), cache_read_mtok=Decimal('1.25'), output_mtok=Decimal('40'), input_image_mtok=Decimal('10'), cache_image_read_mtok=Decimal('2.5'), ), ), ModelInfo( id='gpt-image-1-mini', match=ClauseOr(or_=[ClauseEquals(equals='gpt-image-1-mini')]), name='GPT Image 1 Mini', description='A cost-efficient image generation model from OpenAI with text input pricing.', price_comments='See https://developers.openai.com/api/docs/models/gpt-image-1-mini.', prices=ModelPrice( input_mtok=Decimal('2'), cache_read_mtok=Decimal('0.2'), output_mtok=Decimal('8'), input_image_mtok=Decimal('2.5'), cache_image_read_mtok=Decimal('0.25'), ), ), ModelInfo( id='gpt-image-1.5', match=ClauseOr( or_=[ClauseEquals(equals='gpt-image-1.5'), ClauseEquals(equals='gpt-image-1.5-2025-12-16')] ), name='GPT Image 1.5', description='An improved image generation model from OpenAI supporting text input and output pricing.', price_comments='See https://developers.openai.com/api/docs/models/gpt-image-1.5.', prices=ModelPrice( input_mtok=Decimal('5'), cache_read_mtok=Decimal('1.25'), output_mtok=Decimal('10'), input_image_mtok=Decimal('8'), cache_image_read_mtok=Decimal('2'), output_image_mtok=Decimal('32'), ), ), ModelInfo( id='gpt-image-2', match=ClauseOr(or_=[ClauseEquals(equals='gpt-image-2'), ClauseEquals(equals='gpt-image-2-2026-04-21')]), name='GPT Image 2', description="OpenAI's latest image generation model with text input pricing.", price_comments='See https://developers.openai.com/api/docs/models/gpt-image-2.', prices=ModelPrice( input_mtok=Decimal('5'), cache_read_mtok=Decimal('1.25'), output_mtok=Decimal('30'), input_image_mtok=Decimal('8'), cache_image_read_mtok=Decimal('2'), ), ), ModelInfo( id='gpt-oss-120b', match=ClauseEquals(equals='gpt-oss-120b'), name='gpt-oss-120b', description='gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases.', context_window=131072, price_comments='Imported from OpenRouter pricing; verify against OpenAI pricing when native API pricing is published.', prices=ModelPrice(input_mtok=Decimal('0.039'), output_mtok=Decimal('0.18')), ), ModelInfo( id='gpt-oss-20b', match=ClauseEquals(equals='gpt-oss-20b'), name='gpt-oss-20b', description='gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass.', context_window=131072, price_comments='Imported from OpenRouter pricing; verify against OpenAI pricing when native API pricing is published.', prices=ModelPrice(input_mtok=Decimal('0.029'), output_mtok=Decimal('0.14')), ), ModelInfo( id='gpt-oss-safeguard-20b', match=ClauseEquals(equals='gpt-oss-safeguard-20b'), name='gpt-oss-safeguard-20b', description='gpt-oss-safeguard-20b is a safety reasoning model from OpenAI built upon gpt-oss-20b. This open-weight, 21B-parameter Mixture-of-Experts (MoE) model offers lower latency for safety tasks.', context_window=131072, price_comments='Imported from OpenRouter pricing; verify against OpenAI pricing when native API pricing is published.', prices=ModelPrice( input_mtok=Decimal('0.075'), cache_read_mtok=Decimal('0.037'), output_mtok=Decimal('0.3') ), ), ModelInfo( id='gpt-realtime', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-realtime'), ClauseEquals(equals='gpt-realtime-2025-08-28'), ClauseEquals(equals='gpt-realtime-1.5'), ] ), context_window=32000, price_comments='See https://developers.openai.com/api/docs/models/gpt-realtime.', prices=ModelPrice( input_mtok=Decimal('4'), cache_read_mtok=Decimal('0.4'), output_mtok=Decimal('16'), input_audio_mtok=Decimal('32'), cache_audio_read_mtok=Decimal('0.4'), output_audio_mtok=Decimal('64'), input_image_mtok=Decimal('5'), cache_image_read_mtok=Decimal('0.5'), ), ), ModelInfo( id='gpt-realtime-2', match=ClauseOr(or_=[ClauseEquals(equals='gpt-realtime-2'), ClauseEquals(equals='gpt-realtime-2.1')]), context_window=128000, price_comments='See https://developers.openai.com/api/docs/models/gpt-realtime-2.1.', prices=ModelPrice( input_mtok=Decimal('4'), cache_read_mtok=Decimal('0.4'), output_mtok=Decimal('24'), input_audio_mtok=Decimal('32'), cache_audio_read_mtok=Decimal('0.4'), output_audio_mtok=Decimal('64'), input_image_mtok=Decimal('5'), cache_image_read_mtok=Decimal('0.5'), ), ), ModelInfo( id='gpt-realtime-mini', match=ClauseOr( or_=[ ClauseEquals(equals='gpt-realtime-mini'), ClauseEquals(equals='gpt-realtime-2.1-mini'), ClauseEquals(equals='gpt-realtime-mini-2025-12-15'), ClauseEquals(equals='gpt-realtime-mini-2025-10-06'), ] ), price_comments='See https://developers.openai.com/api/docs/models/gpt-realtime-2.1-mini.', prices=ModelPrice( input_mtok=Decimal('0.6'), cache_read_mtok=Decimal('0.06'), output_mtok=Decimal('2.4'), input_audio_mtok=Decimal('10'), cache_audio_read_mtok=Decimal('0.3'), output_audio_mtok=Decimal('20'), input_image_mtok=Decimal('0.8'), cache_image_read_mtok=Decimal('0.08'), ), ), ModelInfo( id='gpt-transcribe', match=ClauseEquals(equals='gpt-transcribe'), price_comments='See https://developers.openai.com/api/docs/models/gpt-transcribe.', prices=ModelPrice(audio_hours=Decimal('0.27'), input_audio_hours=Decimal('0.27')), ), ModelInfo( id='moderation', match=ClauseContains(contains='moderation'), description='All OpenAI moderation models and endpoints are free of charge', prices=ModelPrice(), ), ModelInfo( id='o1', match=ClauseOr(or_=[ClauseEquals(equals='o1'), ClauseEquals(equals='o1-2024-12-17')]), name='o1', description='O1 is a model that offers a balance between cost and performance.', context_window=200000, prices=ModelPrice(input_mtok=Decimal('15'), cache_read_mtok=Decimal('7.5'), output_mtok=Decimal('60')), ), ModelInfo( id='o1-mini', match=ClauseOr(or_=[ClauseEquals(equals='o1-mini'), ClauseEquals(equals='o1-mini-2024-09-12')]), name='o1 mini', description='O1 Mini is a model that offers a balance between cost and performance.', context_window=128000, prices=ModelPrice( input_mtok=Decimal('1.1'), cache_read_mtok=Decimal('0.55'), output_mtok=Decimal('4.4') ), ), ModelInfo( id='o1-preview', match=ClauseOr(or_=[ClauseEquals(equals='o1-preview'), ClauseEquals(equals='o1-preview-2024-09-12')]), name='o1 preview', description="Preview release of OpenAI's first reasoning model.", context_window=128000, prices=ModelPrice(input_mtok=Decimal('15'), cache_read_mtok=Decimal('7.5'), output_mtok=Decimal('60')), ), ModelInfo( id='o1-pro', match=ClauseOr(or_=[ClauseEquals(equals='o1-pro'), ClauseEquals(equals='o1-pro-2025-03-19')]), name='o1-pro', description='The o1 series of models are trained with reinforcement learning to think before they answer and perform complex reasoning. The o1-pro model uses more compute to think harder and provide consistently better answers.', context_window=200000, prices=ModelPrice(input_mtok=Decimal('150'), output_mtok=Decimal('600')), ), ModelInfo( id='o3', match=ClauseOr(or_=[ClauseEquals(equals='o3'), ClauseEquals(equals='o3-2025-04-16')]), name='o3', description='o3 is a well-rounded and powerful model across domains. It sets a new standard for math, science, coding, and visual reasoning tasks. It also excels at technical writing and instruction-following. Use it to think through multi-step problems that involve analysis across text, code, and images. Note that BYOK is required for this model. Set up here: https://openrouter.ai/settings/integrations', context_window=200000, prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=Decimal('10'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('40'), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2025, 6, 10)), prices=ModelPrice( input_mtok=Decimal('2'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('8'), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ], ), ModelInfo( id='o3-deep-research', match=ClauseOr( or_=[ClauseEquals(equals='o3-deep-research'), ClauseEquals(equals='o3-deep-research-2025-06-26')] ), context_window=200000, prices=ModelPrice(input_mtok=Decimal('10'), cache_read_mtok=Decimal('2.5'), output_mtok=Decimal('40')), ), ModelInfo( id='o3-mini', match=ClauseOr( or_=[ ClauseEquals(equals='o3-mini'), ClauseEquals(equals='o3-mini-2025-01-31'), ClauseEquals(equals='o3-mini-high'), ] ), name='o3 Mini', description='OpenAI o3-mini is a cost-efficient language model optimized for STEM reasoning tasks, particularly excelling in science, mathematics, and coding.', context_window=200000, prices=ModelPrice( input_mtok=Decimal('1.1'), cache_read_mtok=Decimal('0.55'), output_mtok=Decimal('4.4') ), ), ModelInfo( id='o3-pro', match=ClauseOr(or_=[ClauseEquals(equals='o3-pro'), ClauseEquals(equals='o3-pro-2025-06-10')]), name='o3 Pro', description='The o-series of models are trained with reinforcement learning to think before they answer and perform complex reasoning. The o3-pro model uses more compute to think harder and provide consistently better answers.', context_window=200000, prices=ModelPrice(input_mtok=Decimal('20'), output_mtok=Decimal('80')), ), ModelInfo( id='o4-mini', match=ClauseOr( or_=[ ClauseEquals(equals='o4-mini-2025-04-16'), ClauseEquals(equals='o4-mini-high'), ClauseEquals(equals='o4-mini'), ] ), name='o4 Mini High', description='OpenAI o4-mini-high is the same model as o4-mini with reasoning_effort set to high.', context_window=200000, prices=ModelPrice( input_mtok=Decimal('1.1'), cache_read_mtok=Decimal('0.275'), output_mtok=Decimal('4.4'), web_searches_kcount=Decimal('10'), storage_searches_kcount=Decimal('2.5'), ), ), ModelInfo( id='o4-mini-deep-research', match=ClauseOr( or_=[ ClauseEquals(equals='o4-mini-deep-research'), ClauseEquals(equals='o4-mini-deep-research-2025-06-26'), ] ), context_window=200000, prices=ModelPrice(input_mtok=Decimal('2'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('8')), ), ModelInfo( id='text-davinci-002', match=ClauseEquals(equals='text-davinci-002'), prices=ModelPrice(input_mtok=Decimal('20'), output_mtok=Decimal('20')), ), ModelInfo( id='text-davinci-003', match=ClauseEquals(equals='text-davinci-003'), prices=ModelPrice(input_mtok=Decimal('20'), output_mtok=Decimal('20')), ), ModelInfo( id='text-embedding-3-large', match=ClauseEquals(equals='text-embedding-3-large'), name='text embedding 3', description='Text Embedding 3 is a model that offers a balance between cost and performance.', context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.13')), ), ModelInfo( id='text-embedding-3-small', match=ClauseEquals(equals='text-embedding-3-small'), name='text embedding 3', description='Text Embedding 3 is a model that offers a balance between cost and performance.', context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.02')), ), ModelInfo( id='text-embedding-ada-002', match=ClauseOr( or_=[ ClauseEquals(equals='text-embedding-ada'), ClauseEquals(equals='text-embedding-ada-002'), ClauseEquals(equals='text-embedding-ada-002-v2'), ] ), name='text embedding ada', description='Text Embedding Ada is a model that offers a balance between cost and performance.', context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.1')), ), ModelInfo( id='whisper-1', match=ClauseEquals(equals='whisper-1'), price_comments='See https://developers.openai.com/api/docs/models/whisper-1.', prices=ModelPrice(audio_hours=Decimal('0.36'), input_audio_hours=Decimal('0.36')), ), ], ), Provider( id='openrouter', name='OpenRouter', api_pattern='https://(api\\.)?openrouter\\.ai', pricing_urls=['https://openrouter.ai/models'], extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ) ], models=[ ModelInfo( id='01-ai/yi-large', match=ClauseEquals(equals='01-ai/yi-large'), prices=ModelPrice(input_mtok=Decimal('3'), output_mtok=Decimal('3')), ), ModelInfo( id='aetherwiing/mn-starcannon-12b', match=ClauseEquals(equals='aetherwiing/mn-starcannon-12b'), prices=ModelPrice(input_mtok=Decimal('0.8'), output_mtok=Decimal('1.2')), ), ModelInfo( id='agentica-org/deepcoder-14b-preview:free', match=ClauseEquals(equals='agentica-org/deepcoder-14b-preview:free'), prices=ModelPrice(), ), ModelInfo( id='ai21/jamba-1-5-large', match=ClauseEquals(equals='ai21/jamba-1-5-large'), prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('8')), ), ModelInfo( id='ai21/jamba-1-5-mini', match=ClauseEquals(equals='ai21/jamba-1-5-mini'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.4')), ), ModelInfo( id='ai21/jamba-1.6-large', match=ClauseEquals(equals='ai21/jamba-1.6-large'), prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('8')), ), ModelInfo( id='ai21/jamba-1.6-mini', match=ClauseEquals(equals='ai21/jamba-1.6-mini'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.4')), ), ModelInfo( id='ai21/jamba-instruct', match=ClauseEquals(equals='ai21/jamba-instruct'), prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('0.7')), ), ModelInfo( id='ai21/jamba-large-1.7', match=ClauseEquals(equals='ai21/jamba-large-1.7'), name='Jamba Large 1.7', prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('8')), ), ModelInfo( id='aion-labs/aion-1.0', match=ClauseEquals(equals='aion-labs/aion-1.0'), name='Aion-1.0', prices=ModelPrice(input_mtok=Decimal('4'), output_mtok=Decimal('8')), ), ModelInfo( id='aion-labs/aion-1.0-mini', match=ClauseEquals(equals='aion-labs/aion-1.0-mini'), name='Aion-1.0-Mini', prices=ModelPrice(input_mtok=Decimal('0.7'), output_mtok=Decimal('1.4')), ), ModelInfo( id='aion-labs/aion-2.0', match=ClauseEquals(equals='aion-labs/aion-2.0'), name='Aion-2.0', context_window=131072, prices=ModelPrice( input_mtok=Decimal('0.8'), cache_read_mtok=Decimal('0.2'), output_mtok=Decimal('1.6') ), ), ModelInfo( id='aion-labs/aion-rp-llama-3.1-8b', match=ClauseEquals(equals='aion-labs/aion-rp-llama-3.1-8b'), context_window=32768, prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='alfredpros/codellama-7b-instruct-solidity', match=ClauseEquals(equals='alfredpros/codellama-7b-instruct-solidity'), prices=ModelPrice(input_mtok=Decimal('0.8'), output_mtok=Decimal('1.2')), ), ModelInfo( id='all-hands/openhands-lm-32b-v0.1', match=ClauseEquals(equals='all-hands/openhands-lm-32b-v0.1'), prices=ModelPrice(input_mtok=Decimal('2.6'), output_mtok=Decimal('3.4')), ), ModelInfo( id='allenai/molmo-7b-d:free', match=ClauseEquals(equals='allenai/molmo-7b-d:free'), prices=ModelPrice() ), ModelInfo( id='allenai/olmo-3-32b-think', match=ClauseEquals(equals='allenai/olmo-3-32b-think'), name='Olmo 3 32B Think', context_window=65536, prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.5')), ), ModelInfo( id='alpindale/goliath-120b', match=ClauseEquals(equals='alpindale/goliath-120b'), prices=ModelPrice(input_mtok=Decimal('6.5625'), output_mtok=Decimal('9.375')), ), ModelInfo( id='alpindale/magnum-72b', match=ClauseEquals(equals='alpindale/magnum-72b'), prices=ModelPrice(input_mtok=Decimal('1.5'), output_mtok=Decimal('2.25')), ), ModelInfo( id='amazon/nova-2-lite-v1', match=ClauseEquals(equals='amazon/nova-2-lite-v1'), name='Nova 2 Lite', context_window=1000000, prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('2.5')), ), ModelInfo( id='amazon/nova-lite-v1', match=ClauseEquals(equals='amazon/nova-lite-v1'), context_window=300000, prices=ModelPrice(input_mtok=Decimal('0.06'), output_mtok=Decimal('0.24')), ), ModelInfo( id='amazon/nova-micro-v1', match=ClauseEquals(equals='amazon/nova-micro-v1'), context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.035'), output_mtok=Decimal('0.14')), ), ModelInfo( id='amazon/nova-premier-v1', match=ClauseEquals(equals='amazon/nova-premier-v1'), name='Nova Premier 1.0', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('2.5'), cache_read_mtok=Decimal('0.625'), output_mtok=Decimal('12.5') ), ), ModelInfo( id='amazon/nova-pro-v1', match=ClauseEquals(equals='amazon/nova-pro-v1'), context_window=300000, prices=ModelPrice(input_mtok=Decimal('0.8'), output_mtok=Decimal('3.2')), ), ModelInfo( id='anthracite-org/magnum-v2-72b', match=ClauseEquals(equals='anthracite-org/magnum-v2-72b'), prices=ModelPrice(input_mtok=Decimal('3'), output_mtok=Decimal('3')), ), ModelInfo( id='anthracite-org/magnum-v4-72b', match=ClauseEquals(equals='anthracite-org/magnum-v4-72b'), context_window=32768, prices=ModelPrice(input_mtok=Decimal('1.5'), output_mtok=Decimal('2.25')), ), ModelInfo( id='anthropic/claude-2', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-2'), ClauseEquals(equals='anthropic/claude-2.0'), ClauseEquals(equals='anthropic/claude-2.0:beta'), ClauseEquals(equals='anthropic/claude-2.1'), ClauseEquals(equals='anthropic/claude-2.1:beta'), ClauseEquals(equals='anthropic/claude-2:beta'), ] ), prices=ModelPrice(input_mtok=Decimal('8'), output_mtok=Decimal('24')), ), ModelInfo( id='anthropic/claude-3-haiku', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-3-haiku'), ClauseEquals(equals='anthropic/claude-3-haiku:beta'), ] ), context_window=200000, prices=ModelPrice(input_mtok=Decimal('0.25'), output_mtok=Decimal('1.25')), ), ModelInfo( id='anthropic/claude-3-opus', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-3-opus'), ClauseEquals(equals='anthropic/claude-3-opus:beta'), ] ), prices=ModelPrice(input_mtok=Decimal('15'), output_mtok=Decimal('75')), ), ModelInfo( id='anthropic/claude-3-sonnet', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-3-sonnet'), ClauseEquals(equals='anthropic/claude-3-sonnet:beta'), ] ), prices=ModelPrice(input_mtok=Decimal('3'), output_mtok=Decimal('15')), ), ModelInfo( id='anthropic/claude-3.5-haiku', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-3.5-haiku'), ClauseEquals(equals='anthropic/claude-3.5-haiku-20241022'), ClauseEquals(equals='anthropic/claude-3.5-haiku-20241022:beta'), ClauseEquals(equals='anthropic/claude-3.5-haiku:beta'), ] ), prices=ModelPrice(input_mtok=Decimal('0.8'), output_mtok=Decimal('4')), ), ModelInfo( id='anthropic/claude-3.5-sonnet', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-3.5-sonnet'), ClauseEquals(equals='anthropic/claude-3.5-sonnet-20240620'), ClauseEquals(equals='anthropic/claude-3.5-sonnet-20240620:beta'), ClauseEquals(equals='anthropic/claude-3.5-sonnet:beta'), ] ), prices=ModelPrice(input_mtok=Decimal('3'), output_mtok=Decimal('15')), ), ModelInfo( id='anthropic/claude-3.7-sonnet', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-3.7-sonnet'), ClauseEquals(equals='anthropic/claude-3.7-sonnet:beta'), ClauseEquals(equals='anthropic/claude-3.7-sonnet:thinking'), ] ), prices=ModelPrice(input_mtok=Decimal('3'), output_mtok=Decimal('15')), ), ModelInfo( id='anthropic/claude-fable-5', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-fable-5'), ClauseEquals(equals='anthropic/claude-fable-5:beta'), ] ), context_window=1000000, price_comments='Flat pricing across full 1M context window (no tiered pricing). Ref: https://platform.claude.com/docs/en/about-claude/pricing#long-context-pricing', prices=ModelPrice( input_mtok=Decimal('10'), cache_write_mtok=Decimal('12.5'), cache_read_mtok=Decimal('1'), output_mtok=Decimal('50'), ), ), ModelInfo( id='anthropic/claude-fable-5.1', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-fable-5.1'), ClauseEquals(equals='anthropic/claude-fable-5.1:beta'), ] ), context_window=1000000, price_comments='Flat pricing across full 1M context window (no tiered pricing). Cache hits are 0.025x base input (not the usual 0.1x), unique to Fable 5.1 and Mythos 5.1. Ref: https://platform.claude.com/docs/en/about-claude/pricing#model-pricing Cache-read rate confirmed via https://openrouter.ai/api/v1/models', prices=ModelPrice( input_mtok=Decimal('10'), cache_write_mtok=Decimal('12.5'), cache_read_mtok=Decimal('0.25'), output_mtok=Decimal('50'), ), ), ModelInfo( id='anthropic/claude-haiku-4.5', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-haiku-4.5'), ClauseEquals(equals='anthropic/claude-4.5-haiku-20251001'), ClauseEquals(equals='anthropic/claude-4.5-haiku-20251001:beta'), ClauseEquals(equals='anthropic/claude-haiku-4.5-20251001'), ClauseEquals(equals='anthropic/claude-haiku-4.5-20251001:beta'), ClauseEquals(equals='anthropic/claude-haiku-4.5:beta'), ] ), context_window=200000, prices=ModelPrice( input_mtok=Decimal('1'), cache_write_mtok=Decimal('1.25'), cache_read_mtok=Decimal('0.1'), output_mtok=Decimal('5'), ), ), ModelInfo( id='anthropic/claude-opus-4', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-opus-4'), ClauseEquals(equals='anthropic/claude-opus-4.1'), ] ), name='Claude Opus 4', context_window=200000, prices=ModelPrice( input_mtok=Decimal('15'), cache_write_mtok=Decimal('18.75'), cache_read_mtok=Decimal('1.5'), output_mtok=Decimal('75'), ), ), ModelInfo( id='anthropic/claude-opus-4.5', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-opus-4.5'), ClauseEquals(equals='anthropic/claude-4.5-opus-20251124'), ClauseEquals(equals='anthropic/claude-4.5-opus-20251124:beta'), ClauseEquals(equals='anthropic/claude-opus-4.5-20251124'), ClauseEquals(equals='anthropic/claude-opus-4.5-20251124:beta'), ClauseEquals(equals='anthropic/claude-opus-4.5:beta'), ] ), context_window=200000, prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), ), ), ModelInfo( id='anthropic/claude-opus-4.6', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-opus-4.6'), ClauseEquals(equals='anthropic/claude-4.6-opus-20260205'), ClauseEquals(equals='anthropic/claude-4.6-opus-20260205:beta'), ClauseEquals(equals='anthropic/claude-opus-4.6-20260205'), ClauseEquals(equals='anthropic/claude-opus-4.6-20260205:beta'), ClauseEquals(equals='anthropic/claude-opus-4.6:beta'), ] ), context_window=1000000, price_comments='Flat pricing across full 1M context window (no tiered pricing). Ref: https://platform.claude.com/docs/en/about-claude/pricing#long-context-pricing', prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), ), ), ModelInfo( id='anthropic/claude-opus-4.6-fast', match=ClauseEquals(equals='anthropic/claude-opus-4.6-fast'), name='Claude Opus 4.6 (Fast)', prices=ModelPrice( input_mtok=Decimal('30'), cache_write_mtok=Decimal('37.5'), cache_read_mtok=Decimal('3'), output_mtok=Decimal('150'), ), ), ModelInfo( id='anthropic/claude-opus-4.7', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-opus-4.7'), ClauseEquals(equals='anthropic/claude-opus-4.7:beta'), ] ), context_window=1000000, price_comments='Flat pricing across full 1M context window (no tiered pricing). Ref: https://platform.claude.com/docs/en/about-claude/pricing#long-context-pricing', prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), ), ), ModelInfo( id='anthropic/claude-opus-4.7-fast', match=ClauseEquals(equals='anthropic/claude-opus-4.7-fast'), name='Claude Opus 4.7 (Fast)', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('30'), cache_write_mtok=Decimal('37.5'), cache_read_mtok=Decimal('3'), output_mtok=Decimal('150'), ), ), ModelInfo( id='anthropic/claude-opus-4.8', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-opus-4.8'), ClauseEquals(equals='anthropic/claude-opus-4.8:beta'), ] ), context_window=1000000, price_comments='Flat pricing across full 1M context window (no tiered pricing). Ref: https://platform.claude.com/docs/en/about-claude/pricing#long-context-pricing', prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), ), ), ModelInfo( id='anthropic/claude-opus-4.8-fast', match=ClauseEquals(equals='anthropic/claude-opus-4.8-fast'), name='Claude Opus 4.8 (Fast)', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('10'), cache_write_mtok=Decimal('12.5'), cache_read_mtok=Decimal('1'), output_mtok=Decimal('50'), ), ), ModelInfo( id='anthropic/claude-opus-5', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-opus-5'), ClauseEquals(equals='anthropic/claude-opus-5:beta'), ] ), context_window=1000000, price_comments='Flat pricing across full 1M context window (no tiered pricing). Ref: https://platform.claude.com/docs/en/about-claude/pricing#long-context-pricing', prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), ), ), ModelInfo( id='anthropic/claude-opus-5-fast', match=ClauseEquals(equals='anthropic/claude-opus-5-fast'), name='Claude Opus 5 (Fast)', context_window=1000000, price_comments='Fast mode premium pricing, applies across the full context window. Ref: https://platform.claude.com/docs/en/about-claude/pricing#fast-mode-pricing', prices=ModelPrice( input_mtok=Decimal('10'), cache_write_mtok=Decimal('12.5'), cache_read_mtok=Decimal('1'), output_mtok=Decimal('50'), ), ), ModelInfo( id='anthropic/claude-opus-5.5', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-opus-5.5'), ClauseEquals(equals='anthropic/claude-opus-5.5:beta'), ] ), context_window=1000000, price_comments='Flat pricing across full 1M context window (no tiered pricing). Cache hits are 0.05x base input (not the usual 0.1x), unique to Opus 5.5. Ref: https://platform.claude.com/docs/en/about-claude/pricing#model-pricing Cache-read rate confirmed via https://openrouter.ai/api/v1/models', prices=ModelPrice( input_mtok=Decimal('4'), cache_write_mtok=Decimal('5'), cache_read_mtok=Decimal('0.2'), output_mtok=Decimal('20'), ), ), ModelInfo( id='anthropic/claude-sonnet-4', match=ClauseEquals(equals='anthropic/claude-sonnet-4'), name='Claude Sonnet 4', prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), ), ), ModelInfo( id='anthropic/claude-sonnet-4.5', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-sonnet-4.5'), ClauseEquals(equals='anthropic/claude-4.5-sonnet-20250929'), ClauseEquals(equals='anthropic/claude-4.5-sonnet-20250929:beta'), ClauseEquals(equals='anthropic/claude-sonnet-4.5-20250929'), ClauseEquals(equals='anthropic/claude-sonnet-4.5-20250929:beta'), ClauseEquals(equals='anthropic/claude-sonnet-4.5:beta'), ] ), context_window=1000000, price_comments='Tiered pricing: Unlike 4.6 models, Sonnet 4.5 has long-context surcharge. Ref: https://platform.claude.com/docs/en/about-claude/pricing#long-context-pricing', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('3'), tiers=[Tier(start=200000, price=Decimal('6'))]), cache_write_mtok=TieredPrices( base=Decimal('3.75'), tiers=[Tier(start=200000, price=Decimal('7.5'))] ), cache_read_mtok=TieredPrices(base=Decimal('0.3'), tiers=[Tier(start=200000, price=Decimal('0.6'))]), output_mtok=TieredPrices(base=Decimal('15'), tiers=[Tier(start=200000, price=Decimal('22.5'))]), ), ), ModelInfo( id='anthropic/claude-sonnet-4.6', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-sonnet-4.6'), ClauseEquals(equals='anthropic/claude-4.6-sonnet-20260217'), ClauseEquals(equals='anthropic/claude-4.6-sonnet-20260217:beta'), ClauseEquals(equals='anthropic/claude-sonnet-4.6-20260217'), ClauseEquals(equals='anthropic/claude-sonnet-4.6-20260217:beta'), ClauseEquals(equals='anthropic/claude-sonnet-4.6:beta'), ] ), context_window=1000000, price_comments='Flat pricing across full 1M context window (no tiered pricing). Ref: https://platform.claude.com/docs/en/about-claude/pricing#long-context-pricing', prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), ), ), ModelInfo( id='anthropic/claude-sonnet-5', match=ClauseOr( or_=[ ClauseEquals(equals='anthropic/claude-sonnet-5'), ClauseEquals(equals='anthropic/claude-sonnet-5:beta'), ] ), context_window=1000000, price_comments='Flat pricing across the full 1M context window (no tiered pricing). Anthropic made the introductory $2/$10 per MTok rates permanent and cancelled the previously scheduled increase. Refs: https://openrouter.ai/anthropic/claude-sonnet-5, https://platform.claude.com/docs/en/about-claude/pricing', prices=ModelPrice( input_mtok=Decimal('2'), cache_write_mtok=Decimal('2.5'), cache_read_mtok=Decimal('0.2'), output_mtok=Decimal('10'), ), ), ModelInfo( id='anubis-pro-105b-v1', match=ClauseEquals(equals='anubis-pro-105b-v1'), name='Anubis Pro 105B V1', prices=ModelPrice(input_mtok=Decimal('0.8'), output_mtok=Decimal('1')), ), ModelInfo( id='arcee-ai/coder-large', match=ClauseEquals(equals='arcee-ai/coder-large'), name='Coder Large', prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('0.8')), ), ModelInfo( id='arcee-ai/trinity-large-thinking', match=ClauseEquals(equals='arcee-ai/trinity-large-thinking'), name='Trinity Large Thinking', context_window=262144, prices=ModelPrice( input_mtok=Decimal('0.22'), cache_read_mtok=Decimal('0.06'), output_mtok=Decimal('0.85') ), ), ModelInfo( id='arcee-ai/trinity-mini', match=ClauseEquals(equals='arcee-ai/trinity-mini'), name='Trinity Mini', prices=ModelPrice(input_mtok=Decimal('0.045'), output_mtok=Decimal('0.15')), ), ModelInfo( id='arcee-ai/virtuoso-large', match=ClauseEquals(equals='arcee-ai/virtuoso-large'), name='Virtuoso Large', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.75'), output_mtok=Decimal('1.2')), ), ModelInfo( id='arcee-blitz', match=ClauseEquals(equals='arcee-blitz'), name='Arcee Blitz', prices=ModelPrice(input_mtok=Decimal('0.45'), output_mtok=Decimal('0.75')), ), ModelInfo( id='arliai/qwq-32b-arliai-rpr-v1:free', match=ClauseEquals(equals='arliai/qwq-32b-arliai-rpr-v1:free'), prices=ModelPrice(), ), ModelInfo( id='baidu/ernie-4.5-vl-424b-a47b', match=ClauseEquals(equals='baidu/ernie-4.5-vl-424b-a47b'), name='ERNIE 4.5 VL 424B A47B', context_window=123000, prices=ModelPrice(input_mtok=Decimal('0.42'), output_mtok=Decimal('1.25')), ), ModelInfo( id='bytedance-research/ui-tars-72b:free', match=ClauseEquals(equals='bytedance-research/ui-tars-72b:free'), prices=ModelPrice(), ), ModelInfo( id='bytedance-seed/seed-1.6', match=ClauseEquals(equals='bytedance-seed/seed-1.6'), name='Seed 1.6', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.25'), output_mtok=Decimal('2')), ), ModelInfo( id='bytedance-seed/seed-1.6-flash', match=ClauseEquals(equals='bytedance-seed/seed-1.6-flash'), name='Seed 1.6 Flash', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.075'), output_mtok=Decimal('0.3')), ), ModelInfo( id='bytedance-seed/seed-2.0-lite', match=ClauseEquals(equals='bytedance-seed/seed-2.0-lite'), name='Seed-2.0-Lite', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.25'), output_mtok=Decimal('2')), ), ModelInfo( id='bytedance-seed/seed-2.0-mini', match=ClauseEquals(equals='bytedance-seed/seed-2.0-mini'), name='Seed-2.0-Mini', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.4')), ), ModelInfo( id='bytedance/ui-tars-1.5-7b', match=ClauseEquals(equals='bytedance/ui-tars-1.5-7b'), name='UI-TARS 7B', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.2')), ), ModelInfo( id='caller-large', match=ClauseEquals(equals='caller-large'), name='Caller Large', prices=ModelPrice(input_mtok=Decimal('0.55'), output_mtok=Decimal('0.85')), ), ModelInfo( id='chatgpt-4o-latest', match=ClauseEquals(equals='chatgpt-4o-latest'), name='ChatGPT-4o', prices=ModelPrice(input_mtok=Decimal('5'), output_mtok=Decimal('15')), ), ModelInfo( id='claude-2', match=ClauseOr( or_=[ ClauseEquals(equals='claude-2'), ClauseEquals(equals='claude-2.0'), ClauseEquals(equals='claude-2.0:beta'), ClauseEquals(equals='claude-2.1'), ClauseEquals(equals='claude-2.1:beta'), ClauseEquals(equals='claude-2:beta'), ] ), name='Claude v2', prices=ModelPrice(input_mtok=Decimal('8'), output_mtok=Decimal('24')), ), ModelInfo( id='claude-3-opus', match=ClauseOr(or_=[ClauseEquals(equals='claude-3-opus'), ClauseEquals(equals='claude-3-opus:beta')]), name='Claude 3 Opus', prices=ModelPrice( input_mtok=Decimal('15'), cache_write_mtok=Decimal('18.75'), cache_read_mtok=Decimal('1.5'), output_mtok=Decimal('75'), ), ), ModelInfo( id='claude-3-sonnet', match=ClauseOr( or_=[ClauseEquals(equals='claude-3-sonnet'), ClauseEquals(equals='claude-3-sonnet:beta')] ), name='Claude 3 Sonnet', prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), ), ), ModelInfo( id='claude-3.5-sonnet', match=ClauseOr( or_=[ ClauseEquals(equals='claude-3.5-sonnet'), ClauseEquals(equals='claude-3.5-sonnet-20240620'), ClauseEquals(equals='claude-3.5-sonnet-20240620:beta'), ClauseEquals(equals='claude-3.5-sonnet:beta'), ] ), name='Claude 3.5 Sonnet', prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), ), ), ModelInfo( id='claude-3.7-sonnet', match=ClauseOr( or_=[ ClauseEquals(equals='claude-3.7-sonnet'), ClauseEquals(equals='claude-3.7-sonnet:beta'), ClauseEquals(equals='claude-3.7-sonnet:thinking'), ] ), name='Claude 3.7 Sonnet', prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), ), ), ModelInfo( id='codellama-7b-instruct-solidity', match=ClauseEquals(equals='codellama-7b-instruct-solidity'), name='CodeLLaMa 7B Instruct Solidity', prices=ModelPrice(input_mtok=Decimal('0.8'), output_mtok=Decimal('1.2')), ), ModelInfo( id='codestral-2501', match=ClauseEquals(equals='codestral-2501'), name='Codestral 2501', prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('0.9')), ), ModelInfo( id='codex-mini', match=ClauseEquals(equals='codex-mini'), name='Codex Mini', prices=ModelPrice( input_mtok=Decimal('1.5'), cache_read_mtok=Decimal('0.375'), output_mtok=Decimal('6') ), ), ModelInfo( id='cognitivecomputations/dolphin-mistral-24b-venice-edition:free', match=ClauseEquals(equals='cognitivecomputations/dolphin-mistral-24b-venice-edition:free'), name='Uncensored (free)', prices=ModelPrice(), ), ModelInfo( id='cognitivecomputations/dolphin-mixtral-8x22b', match=ClauseEquals(equals='cognitivecomputations/dolphin-mixtral-8x22b'), prices=ModelPrice(input_mtok=Decimal('0.9'), output_mtok=Decimal('0.9')), ), ModelInfo( id='cognitivecomputations/dolphin-mixtral-8x7b', match=ClauseEquals(equals='cognitivecomputations/dolphin-mixtral-8x7b'), prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('0.5')), ), ModelInfo( id='cognitivecomputations/dolphin3.0-mistral-24b:free', match=ClauseEquals(equals='cognitivecomputations/dolphin3.0-mistral-24b:free'), prices=ModelPrice(), ), ModelInfo( id='cognitivecomputations/dolphin3.0-r1-mistral-24b:free', match=ClauseEquals(equals='cognitivecomputations/dolphin3.0-r1-mistral-24b:free'), prices=ModelPrice(), ), ModelInfo( id='cohere/command', match=ClauseEquals(equals='cohere/command'), prices=ModelPrice(input_mtok=Decimal('1'), output_mtok=Decimal('2')), ), ModelInfo( id='cohere/command-a', match=ClauseEquals(equals='cohere/command-a'), context_window=256000, prices=ModelPrice(input_mtok=Decimal('2.5'), output_mtok=Decimal('10')), ), ModelInfo( id='cohere/command-r', match=ClauseOr( or_=[ClauseEquals(equals='cohere/command-r'), ClauseEquals(equals='cohere/command-r-03-2024')] ), prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('1.5')), ), ModelInfo( id='cohere/command-r-08-2024', match=ClauseEquals(equals='cohere/command-r-08-2024'), context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.6')), ), ModelInfo( id='cohere/command-r-plus', match=ClauseOr( or_=[ ClauseEquals(equals='cohere/command-r-plus'), ClauseEquals(equals='cohere/command-r-plus-04-2024'), ] ), prices=ModelPrice(input_mtok=Decimal('3'), output_mtok=Decimal('15')), ), ModelInfo( id='cohere/command-r-plus-08-2024', match=ClauseEquals(equals='cohere/command-r-plus-08-2024'), context_window=128000, prices=ModelPrice(input_mtok=Decimal('2.5'), output_mtok=Decimal('10')), ), ModelInfo( id='cohere/command-r7b-12-2024', match=ClauseEquals(equals='cohere/command-r7b-12-2024'), context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.0375'), output_mtok=Decimal('0.15')), ), ModelInfo( id='command', match=ClauseEquals(equals='command'), name='Command', prices=ModelPrice(input_mtok=Decimal('1'), output_mtok=Decimal('2')), ), ModelInfo( id='command-r', match=ClauseOr(or_=[ClauseEquals(equals='command-r'), ClauseEquals(equals='command-r-03-2024')]), name='Command R', prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('1.5')), ), ModelInfo( id='command-r-plus', match=ClauseOr( or_=[ClauseEquals(equals='command-r-plus'), ClauseEquals(equals='command-r-plus-04-2024')] ), name='Command R+', prices=ModelPrice(input_mtok=Decimal('3'), output_mtok=Decimal('15')), ), ModelInfo( id='deepcoder-14b-preview:free', match=ClauseEquals(equals='deepcoder-14b-preview:free'), name='Deepcoder 14B Preview (free)', prices=ModelPrice(), ), ModelInfo( id='deepcogito/cogito-v2.1-671b', match=ClauseEquals(equals='deepcogito/cogito-v2.1-671b'), name='Cogito v2.1 671B', prices=ModelPrice(input_mtok=Decimal('1.25'), output_mtok=Decimal('1.25')), ), ModelInfo( id='deephermes-3-llama-3-8b-preview:free', match=ClauseEquals(equals='deephermes-3-llama-3-8b-preview:free'), name='DeepHermes 3 Llama 3 8B Preview (free)', prices=ModelPrice(), ), ModelInfo( id='deepseek-chat-v3-0324:free', match=ClauseEquals(equals='deepseek-chat-v3-0324:free'), name='DeepSeek V3 0324 (free)', prices=ModelPrice(), ), ModelInfo( id='deepseek-chat:free', match=ClauseEquals(equals='deepseek-chat:free'), name='DeepSeek V3 (free)', prices=ModelPrice(), ), ModelInfo( id='deepseek-prover-v2', match=ClauseEquals(equals='deepseek-prover-v2'), name='DeepSeek Prover V2', prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('2.18')), ), ModelInfo( id='deepseek-r1-0528-qwen3-8b', match=ClauseEquals(equals='deepseek-r1-0528-qwen3-8b'), name='Deepseek R1 0528 Qwen3 8B', prices=ModelPrice(input_mtok=Decimal('0.05'), output_mtok=Decimal('0.1')), ), ModelInfo( id='deepseek-r1-0528-qwen3-8b:free', match=ClauseEquals(equals='deepseek-r1-0528-qwen3-8b:free'), name='Deepseek R1 0528 Qwen3 8B (free)', prices=ModelPrice(), ), ModelInfo( id='deepseek-r1-0528:free', match=ClauseEquals(equals='deepseek-r1-0528:free'), name='R1 0528 (free)', prices=ModelPrice(), ), ModelInfo( id='deepseek-r1-distill-llama-70b:free', match=ClauseEquals(equals='deepseek-r1-distill-llama-70b:free'), name='R1 Distill Llama 70B (free)', prices=ModelPrice(), ), ModelInfo( id='deepseek-r1-distill-llama-8b', match=ClauseEquals(equals='deepseek-r1-distill-llama-8b'), name='R1 Distill Llama 8B', prices=ModelPrice(input_mtok=Decimal('0.04'), output_mtok=Decimal('0.04')), ), ModelInfo( id='deepseek-r1-distill-qwen-1.5b', match=ClauseEquals(equals='deepseek-r1-distill-qwen-1.5b'), name='R1 Distill Qwen 1.5B', prices=ModelPrice(input_mtok=Decimal('0.18'), output_mtok=Decimal('0.18')), ), ModelInfo( id='deepseek-r1-distill-qwen-14b', match=ClauseEquals(equals='deepseek-r1-distill-qwen-14b'), name='R1 Distill Qwen 14B', prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.15')), ), ModelInfo( id='deepseek-r1-distill-qwen-14b:free', match=ClauseEquals(equals='deepseek-r1-distill-qwen-14b:free'), name='R1 Distill Qwen 14B (free)', prices=ModelPrice(), ), ModelInfo( id='deepseek-r1-distill-qwen-32b:free', match=ClauseEquals(equals='deepseek-r1-distill-qwen-32b:free'), name='R1 Distill Qwen 32B (free)', prices=ModelPrice(), ), ModelInfo( id='deepseek-r1-distill-qwen-7b', match=ClauseEquals(equals='deepseek-r1-distill-qwen-7b'), name='R1 Distill Qwen 7B', prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.2')), ), ModelInfo( id='deepseek-r1:free', match=ClauseEquals(equals='deepseek-r1:free'), name='R1 (free)', prices=ModelPrice(), ), ModelInfo( id='deepseek-r1t-chimera:free', match=ClauseEquals(equals='deepseek-r1t-chimera:free'), name='DeepSeek R1T Chimera (free)', prices=ModelPrice(), ), ModelInfo( id='deepseek-v3-base:free', match=ClauseEquals(equals='deepseek-v3-base:free'), name='DeepSeek V3 Base (free)', prices=ModelPrice(), ), ModelInfo( id='deepseek/deepseek-chat', match=ClauseEquals(equals='deepseek/deepseek-chat'), name='DeepSeek V3', prices=ModelPrice(input_mtok=Decimal('0.38'), output_mtok=Decimal('0.89')), ), ModelInfo( id='deepseek/deepseek-chat-v3-0324', match=ClauseEquals(equals='deepseek/deepseek-chat-v3-0324'), name='DeepSeek V3 0324', context_window=163840, prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('0.88')), ), ModelInfo( id='deepseek/deepseek-chat-v3-0324:free', match=ClauseEquals(equals='deepseek/deepseek-chat-v3-0324:free'), prices=ModelPrice(), ), ModelInfo( id='deepseek/deepseek-chat-v3.1', match=ClauseEquals(equals='deepseek/deepseek-chat-v3.1'), name='DeepSeek V3.1', prices=ModelPrice( input_mtok=Decimal('0.21'), cache_read_mtok=Decimal('0.13'), output_mtok=Decimal('0.79') ), ), ModelInfo( id='deepseek/deepseek-chat:free', match=ClauseEquals(equals='deepseek/deepseek-chat:free'), prices=ModelPrice(), ), ModelInfo( id='deepseek/deepseek-r1', match=ClauseEquals(equals='deepseek/deepseek-r1'), name='R1', context_window=64000, prices=ModelPrice(input_mtok=Decimal('0.45'), output_mtok=Decimal('2.15')), ), ModelInfo( id='deepseek/deepseek-r1-0528', match=ClauseEquals(equals='deepseek/deepseek-r1-0528'), name='R1 0528', prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('2.15')), ), ModelInfo( id='deepseek/deepseek-r1-distill-llama-70b', match=ClauseEquals(equals='deepseek/deepseek-r1-distill-llama-70b'), name='R1 Distill Llama 70B', context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.4')), ), ModelInfo( id='deepseek/deepseek-r1-distill-llama-70b:free', match=ClauseEquals(equals='deepseek/deepseek-r1-distill-llama-70b:free'), prices=ModelPrice(), ), ModelInfo( id='deepseek/deepseek-r1-distill-llama-8b', match=ClauseEquals(equals='deepseek/deepseek-r1-distill-llama-8b'), prices=ModelPrice(input_mtok=Decimal('0.04'), output_mtok=Decimal('0.04')), ), ModelInfo( id='deepseek/deepseek-r1-distill-qwen-1.5b', match=ClauseEquals(equals='deepseek/deepseek-r1-distill-qwen-1.5b'), prices=ModelPrice(input_mtok=Decimal('0.18'), output_mtok=Decimal('0.18')), ), ModelInfo( id='deepseek/deepseek-r1-distill-qwen-14b', match=ClauseEquals(equals='deepseek/deepseek-r1-distill-qwen-14b'), prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.15')), ), ModelInfo( id='deepseek/deepseek-r1-distill-qwen-14b:free', match=ClauseEquals(equals='deepseek/deepseek-r1-distill-qwen-14b:free'), prices=ModelPrice(), ), ModelInfo( id='deepseek/deepseek-r1-distill-qwen-32b', match=ClauseEquals(equals='deepseek/deepseek-r1-distill-qwen-32b'), name='R1 Distill Qwen 32B', prices=ModelPrice(input_mtok=Decimal('0.12'), output_mtok=Decimal('0.18')), ), ModelInfo( id='deepseek/deepseek-r1-distill-qwen-32b:free', match=ClauseEquals(equals='deepseek/deepseek-r1-distill-qwen-32b:free'), prices=ModelPrice(), ), ModelInfo( id='deepseek/deepseek-r1-zero:free', match=ClauseEquals(equals='deepseek/deepseek-r1-zero:free'), prices=ModelPrice(), ), ModelInfo( id='deepseek/deepseek-r1:free', match=ClauseEquals(equals='deepseek/deepseek-r1:free'), prices=ModelPrice(), ), ModelInfo( id='deepseek/deepseek-v3-base:free', match=ClauseEquals(equals='deepseek/deepseek-v3-base:free'), prices=ModelPrice(), ), ModelInfo( id='deepseek/deepseek-v3.1-terminus', match=ClauseEquals(equals='deepseek/deepseek-v3.1-terminus'), name='DeepSeek V3.1 Terminus', context_window=163840, prices=ModelPrice(input_mtok=Decimal('0.23'), output_mtok=Decimal('0.9')), ), ModelInfo( 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Small', prices=ModelPrice(input_mtok=Decimal('0.06'), output_mtok=Decimal('0.12')), ), ModelInfo( id='devstral-small:free', match=ClauseEquals(equals='devstral-small:free'), name='Devstral Small (free)', prices=ModelPrice(), ), ModelInfo( id='dobby-mini-unhinged-plus-llama-3.1-8b', match=ClauseEquals(equals='dobby-mini-unhinged-plus-llama-3.1-8b'), name='Dobby Mini Plus Llama 3.1 8B', prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='dolphin-mixtral-8x22b', match=ClauseEquals(equals='dolphin-mixtral-8x22b'), name='Dolphin 2.9.2 Mixtral 8x22B 🐬', prices=ModelPrice(input_mtok=Decimal('0.9'), output_mtok=Decimal('0.9')), ), ModelInfo( id='dolphin3.0-mistral-24b:free', match=ClauseEquals(equals='dolphin3.0-mistral-24b:free'), name='Dolphin3.0 Mistral 24B (free)', prices=ModelPrice(), ), ModelInfo( id='dolphin3.0-r1-mistral-24b:free', match=ClauseEquals(equals='dolphin3.0-r1-mistral-24b:free'), name='Dolphin3.0 R1 Mistral 24B (free)', prices=ModelPrice(), ), ModelInfo( id='eleutherai/llemma_7b', match=ClauseEquals(equals='eleutherai/llemma_7b'), prices=ModelPrice(input_mtok=Decimal('0.8'), output_mtok=Decimal('1.2')), ), ModelInfo( id='essentialai/rnj-1-instruct', match=ClauseEquals(equals='essentialai/rnj-1-instruct'), name='Rnj 1 Instruct', prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.15')), ), ModelInfo( id='eva-llama-3.33-70b', match=ClauseEquals(equals='eva-llama-3.33-70b'), name='EVA Llama 3.33 70B', prices=ModelPrice(input_mtok=Decimal('4'), output_mtok=Decimal('6')), ), ModelInfo( id='eva-qwen-2.5-32b', match=ClauseEquals(equals='eva-qwen-2.5-32b'), name='EVA Qwen2.5 32B', prices=ModelPrice(input_mtok=Decimal('2.6'), output_mtok=Decimal('3.4')), ), ModelInfo( id='eva-qwen-2.5-72b', match=ClauseEquals(equals='eva-qwen-2.5-72b'), name='EVA Qwen2.5 72B', prices=ModelPrice(input_mtok=Decimal('4'), output_mtok=Decimal('6')), ), ModelInfo( id='eva-unit-01/eva-llama-3.33-70b', 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match=ClauseEquals(equals='google/gemini-flash-1.5-8b-exp'), prices=ModelPrice(), ), ModelInfo( id='google/gemini-pro', match=ClauseOr( or_=[ClauseEquals(equals='google/gemini-pro'), ClauseEquals(equals='google/gemini-pro-vision')] ), prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('1.5')), ), ModelInfo( id='google/gemini-pro-1.5', match=ClauseEquals(equals='google/gemini-pro-1.5'), prices=ModelPrice(input_mtok=Decimal('1.25'), output_mtok=Decimal('5')), ), ModelInfo( id='google/gemma-2-27b-it', match=ClauseEquals(equals='google/gemma-2-27b-it'), name='Gemma 2 27B', context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.8'), output_mtok=Decimal('0.8')), ), ModelInfo( id='google/gemma-2-9b-it', match=ClauseEquals(equals='google/gemma-2-9b-it'), prices=ModelPrice(input_mtok=Decimal('0.07'), output_mtok=Decimal('0.07')), ), ModelInfo( id='google/gemma-2-9b-it:free', match=ClauseEquals(equals='google/gemma-2-9b-it:free'), prices=ModelPrice(), ), ModelInfo( id='google/gemma-3-12b-it', match=ClauseEquals(equals='google/gemma-3-12b-it'), name='Gemma 3 12B', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.05'), output_mtok=Decimal('0.1')), ), ModelInfo( id='google/gemma-3-12b-it:free', match=ClauseEquals(equals='google/gemma-3-12b-it:free'), prices=ModelPrice(), ), ModelInfo( id='google/gemma-3-1b-it:free', match=ClauseEquals(equals='google/gemma-3-1b-it:free'), prices=ModelPrice(), ), ModelInfo( id='google/gemma-3-27b-it', match=ClauseEquals(equals='google/gemma-3-27b-it'), name='Gemma 3 27B', prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.2')), ), ModelInfo( id='google/gemma-3-27b-it:free', match=ClauseEquals(equals='google/gemma-3-27b-it:free'), prices=ModelPrice(), ), ModelInfo( id='google/gemma-3-4b-it', match=ClauseEquals(equals='google/gemma-3-4b-it'), name='Gemma 3 4B', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.02'), output_mtok=Decimal('0.04')), ), ModelInfo( id='google/gemma-3-4b-it:free', match=ClauseEquals(equals='google/gemma-3-4b-it:free'), prices=ModelPrice(), ), ModelInfo( id='google/gemma-3n-e4b-it', match=ClauseEquals(equals='google/gemma-3n-e4b-it'), name='Gemma 3n 4B', prices=ModelPrice(input_mtok=Decimal('0.06'), output_mtok=Decimal('0.12')), ), ModelInfo( id='google/gemma-4-26b-a4b-it', match=ClauseEquals(equals='google/gemma-4-26b-a4b-it'), name='Gemma 4 26B A4B', prices=ModelPrice(input_mtok=Decimal('0.06'), output_mtok=Decimal('0.33')), ), ModelInfo( id='google/gemma-4-26b-a4b-it:free', match=ClauseEquals(equals='google/gemma-4-26b-a4b-it:free'), name='Gemma 4 26B A4B (free)', context_window=262144, prices=ModelPrice(), ), ModelInfo( id='google/gemma-4-31b-it', match=ClauseEquals(equals='google/gemma-4-31b-it'), name='Gemma 4 31B', context_window=262144, price_comments='OpenRouter reports the cheapest active endpoint for this model. It was Venice ($0.12/$0.36) when checked on 2026-06-09 and DeepInfra ($0.09/$0.34) on 2026-08-27; OpenRouter publishes no history, so the dated entry starts on the day the lower rate was verified. Ref: https://openrouter.ai/api/v1/models/google/gemma-4-31b-it/endpoints', prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=Decimal('0.12'), cache_read_mtok=Decimal('0.09'), output_mtok=Decimal('0.36') ) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2026, 8, 27)), prices=ModelPrice( input_mtok=Decimal('0.09'), cache_read_mtok=Decimal('0.05'), output_mtok=Decimal('0.34') ), ), ], ), ModelInfo( id='google/gemma-4-31b-it:free', match=ClauseEquals(equals='google/gemma-4-31b-it:free'), name='Gemma 4 31B (free)', context_window=262144, prices=ModelPrice(), ), ModelInfo( id='google/learnlm-1.5-pro-experimental:free', match=ClauseEquals(equals='google/learnlm-1.5-pro-experimental:free'), prices=ModelPrice(), ), ModelInfo( id='google/lyria-3-clip-preview', match=ClauseEquals(equals='google/lyria-3-clip-preview'), name='Lyria 3 Clip Preview', context_window=1048576, prices=ModelPrice(), ), ModelInfo( id='google/lyria-3-pro-preview', match=ClauseEquals(equals='google/lyria-3-pro-preview'), name='Lyria 3 Pro Preview', context_window=1048576, prices=ModelPrice(), ), ModelInfo( id='google/palm-2-chat-bison', match=ClauseOr( or_=[ ClauseEquals(equals='google/palm-2-chat-bison'), ClauseEquals(equals='google/palm-2-chat-bison-32k'), ] ), prices=ModelPrice(input_mtok=Decimal('1'), output_mtok=Decimal('2')), ), ModelInfo( id='google/palm-2-codechat-bison', match=ClauseOr( or_=[ ClauseEquals(equals='google/palm-2-codechat-bison'), ClauseEquals(equals='google/palm-2-codechat-bison-32k'), ] ), prices=ModelPrice(input_mtok=Decimal('1'), output_mtok=Decimal('2')), ), ModelInfo( id='gpt-3.5-turbo-1106', match=ClauseEquals(equals='gpt-3.5-turbo-1106'), name='GPT-3.5 Turbo 16k (older v1106)', prices=ModelPrice(input_mtok=Decimal('1'), output_mtok=Decimal('2')), ), ModelInfo( id='gpt-4-1106-preview', match=ClauseEquals(equals='gpt-4-1106-preview'), name='GPT-4 Turbo (older v1106)', prices=ModelPrice(input_mtok=Decimal('10'), output_mtok=Decimal('30')), ), ModelInfo( id='gpt-4.5-preview', match=ClauseEquals(equals='gpt-4.5-preview'), name='GPT-4.5 (Preview)', prices=ModelPrice( input_mtok=Decimal('75'), cache_read_mtok=Decimal('37.5'), output_mtok=Decimal('150') ), ), ModelInfo( id='gpt-4o:extended', match=ClauseEquals(equals='gpt-4o:extended'), name='GPT-4o (extended)', prices=ModelPrice(input_mtok=Decimal('6'), output_mtok=Decimal('18')), ), ModelInfo( id='grok-2-1212', match=ClauseEquals(equals='grok-2-1212'), name='Grok 2 1212', prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('10')), ), ModelInfo( id='grok-2-vision-1212', match=ClauseEquals(equals='grok-2-vision-1212'), name='Grok 2 Vision 1212', prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('10')), ), ModelInfo( id='grok-3', match=ClauseOr(or_=[ClauseEquals(equals='grok-3'), ClauseEquals(equals='grok-3-beta')]), name='Grok 3', prices=ModelPrice(input_mtok=Decimal('3'), cache_read_mtok=Decimal('0.75'), 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output_mtok=Decimal('0.04')), ), ModelInfo( id='huggingfaceh4/zephyr-7b-beta:free', match=ClauseEquals(equals='huggingfaceh4/zephyr-7b-beta:free'), prices=ModelPrice(), ), ModelInfo( id='ibm-granite/granite-4.0-h-micro', match=ClauseEquals(equals='ibm-granite/granite-4.0-h-micro'), name='Granite 4.0 Micro', context_window=131000, prices=ModelPrice(input_mtok=Decimal('0.017'), output_mtok=Decimal('0.112')), ), ModelInfo( id='ibm-granite/granite-4.1-8b', match=ClauseEquals(equals='ibm-granite/granite-4.1-8b'), name='Granite 4.1 8B', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.05'), output_mtok=Decimal('0.1')), ), ModelInfo( id='inception/mercury-2', match=ClauseEquals(equals='inception/mercury-2'), name='Mercury 2', context_window=128000, prices=ModelPrice( input_mtok=Decimal('0.25'), cache_read_mtok=Decimal('0.025'), output_mtok=Decimal('0.75') ), ), ModelInfo( id='inclusionai/ling-2.6-1t', match=ClauseEquals(equals='inclusionai/ling-2.6-1t'), name='Ling-2.6-1T', 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context_window=1000000, prices=ModelPrice(), ), ModelInfo( id='nvidia/nemotron-3.5-content-safety:free', match=ClauseEquals(equals='nvidia/nemotron-3.5-content-safety:free'), name='Nemotron 3.5 Content Safety (free)', context_window=128000, prices=ModelPrice(), ), ModelInfo( id='nvidia/nemotron-nano-12b-v2-vl:free', match=ClauseEquals(equals='nvidia/nemotron-nano-12b-v2-vl:free'), name='Nemotron Nano 12B 2 VL (free)', prices=ModelPrice(), ), ModelInfo( id='nvidia/nemotron-nano-9b-v2', match=ClauseEquals(equals='nvidia/nemotron-nano-9b-v2'), name='Nemotron Nano 9B V2', prices=ModelPrice(input_mtok=Decimal('0.04'), output_mtok=Decimal('0.16')), ), ModelInfo( id='nvidia/nemotron-nano-9b-v2:free', match=ClauseEquals(equals='nvidia/nemotron-nano-9b-v2:free'), name='Nemotron Nano 9B V2 (free)', prices=ModelPrice(), ), ModelInfo( id='o1-mini', match=ClauseOr(or_=[ClauseEquals(equals='o1-mini'), ClauseEquals(equals='o1-mini-2024-09-12')]), name='o1-mini', prices=ModelPrice( input_mtok=Decimal('1.1'), cache_read_mtok=Decimal('0.55'), output_mtok=Decimal('4.4') ), ), ModelInfo( id='open-r1/olympiccoder-32b:free', match=ClauseEquals(equals='open-r1/olympiccoder-32b:free'), prices=ModelPrice(), ), ModelInfo( id='open-r1/olympiccoder-7b:free', match=ClauseEquals(equals='open-r1/olympiccoder-7b:free'), prices=ModelPrice(), ), ModelInfo( id='openai/chatgpt-4o-latest', match=ClauseEquals(equals='openai/chatgpt-4o-latest'), prices=ModelPrice(input_mtok=Decimal('5'), output_mtok=Decimal('15')), ), ModelInfo( id='openai/codex-mini', match=ClauseEquals(equals='openai/codex-mini'), prices=ModelPrice( input_mtok=Decimal('1.5'), cache_read_mtok=Decimal('0.375'), output_mtok=Decimal('6') ), ), ModelInfo( id='openai/gpt-3.5-turbo', match=ClauseOr( or_=[ClauseEquals(equals='openai/gpt-3.5-turbo'), ClauseEquals(equals='gpt-3.5-turbo-0125')] ), name='GPT-3.5 Turbo', context_window=16385, prices=ModelPrice(input_mtok=Decimal('0.5'), output_mtok=Decimal('1.5')), ), ModelInfo( id='openai/gpt-3.5-turbo-0613', match=ClauseEquals(equals='openai/gpt-3.5-turbo-0613'), name='GPT-3.5 Turbo (older v0613)', context_window=4095, prices=ModelPrice(input_mtok=Decimal('1'), output_mtok=Decimal('2')), ), ModelInfo( id='openai/gpt-3.5-turbo-1106', match=ClauseEquals(equals='openai/gpt-3.5-turbo-1106'), prices=ModelPrice(input_mtok=Decimal('1'), output_mtok=Decimal('2')), ), ModelInfo( id='openai/gpt-3.5-turbo-16k', match=ClauseEquals(equals='openai/gpt-3.5-turbo-16k'), name='GPT-3.5 Turbo 16k', context_window=16385, prices=ModelPrice(input_mtok=Decimal('3'), output_mtok=Decimal('4')), ), ModelInfo( id='openai/gpt-3.5-turbo-instruct', match=ClauseEquals(equals='openai/gpt-3.5-turbo-instruct'), name='GPT-3.5 Turbo Instruct', context_window=4095, prices=ModelPrice(input_mtok=Decimal('1.5'), output_mtok=Decimal('2')), ), ModelInfo( id='openai/gpt-4', match=ClauseOr(or_=[ClauseEquals(equals='openai/gpt-4'), ClauseEquals(equals='gpt-4-0314')]), name='GPT-4', context_window=8191, prices=ModelPrice(input_mtok=Decimal('30'), output_mtok=Decimal('60')), ), ModelInfo( id='openai/gpt-4-1106-preview', match=ClauseEquals(equals='openai/gpt-4-1106-preview'), prices=ModelPrice(input_mtok=Decimal('10'), output_mtok=Decimal('30')), ), ModelInfo( id='openai/gpt-4-32k', match=ClauseOr( or_=[ClauseEquals(equals='openai/gpt-4-32k'), ClauseEquals(equals='openai/gpt-4-32k-0314')] ), prices=ModelPrice(input_mtok=Decimal('60'), output_mtok=Decimal('120')), ), ModelInfo( id='openai/gpt-4-turbo', match=ClauseOr( or_=[ClauseEquals(equals='openai/gpt-4-turbo'), ClauseEquals(equals='openai/gpt-4-turbo-preview')] ), name='GPT-4 Turbo', context_window=128000, prices=ModelPrice(input_mtok=Decimal('10'), output_mtok=Decimal('30')), ), ModelInfo( id='openai/gpt-4.1', match=ClauseEquals(equals='openai/gpt-4.1'), name='GPT-4.1', context_window=1047576, prices=ModelPrice(input_mtok=Decimal('2'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('8')), ), ModelInfo( id='openai/gpt-4.1-mini', match=ClauseOr( or_=[ ClauseEquals(equals='openai/gpt-4.1-mini'), ClauseEquals(equals='openai/gpt-4.1-mini-2025-04-14'), ] ), name='GPT-4.1 Mini', context_window=1047576, prices=ModelPrice( input_mtok=Decimal('0.4'), cache_read_mtok=Decimal('0.1'), output_mtok=Decimal('1.6') ), ), ModelInfo( id='openai/gpt-4.1-nano', match=ClauseEquals(equals='openai/gpt-4.1-nano'), name='GPT-4.1 Nano', context_window=1047576, prices=ModelPrice( input_mtok=Decimal('0.1'), cache_read_mtok=Decimal('0.025'), output_mtok=Decimal('0.4') ), ), ModelInfo( id='openai/gpt-4.5-preview', match=ClauseEquals(equals='openai/gpt-4.5-preview'), prices=ModelPrice(input_mtok=Decimal('75'), output_mtok=Decimal('150')), ), ModelInfo( id='openai/gpt-4o', match=ClauseOr( or_=[ ClauseEquals(equals='openai/gpt-4o'), ClauseEquals(equals='openai/gpt-4o-2024-08-06'), ClauseEquals(equals='openai/gpt-4o-2024-11-20'), ClauseEquals(equals='openai/gpt-4o-audio-preview'), ] ), context_window=128000, prices=ModelPrice(input_mtok=Decimal('2.5'), output_mtok=Decimal('10')), ), ModelInfo( id='openai/gpt-4o-2024-05-13', match=ClauseEquals(equals='openai/gpt-4o-2024-05-13'), name='GPT-4o (2024-05-13)', context_window=128000, prices=ModelPrice(input_mtok=Decimal('5'), output_mtok=Decimal('15')), ), ModelInfo( id='openai/gpt-4o-mini', match=ClauseOr( or_=[ ClauseEquals(equals='openai/gpt-4o-mini'), ClauseEquals(equals='openai/gpt-4o-mini-2024-07-18'), ] ), name='GPT-4o-mini', context_window=128000, prices=ModelPrice( input_mtok=Decimal('0.15'), cache_read_mtok=Decimal('0.075'), output_mtok=Decimal('0.6') ), ), ModelInfo( id='openai/gpt-4o-mini-search-preview', match=ClauseEquals(equals='openai/gpt-4o-mini-search-preview'), name='GPT-4o-mini Search Preview', prices=ModelPrice(input_mtok=Decimal('0.15'), output_mtok=Decimal('0.6')), ), ModelInfo( id='openai/gpt-4o-search-preview', match=ClauseEquals(equals='openai/gpt-4o-search-preview'), name='GPT-4o Search Preview', prices=ModelPrice(input_mtok=Decimal('2.5'), output_mtok=Decimal('10')), ), ModelInfo( id='openai/gpt-4o:extended', match=ClauseEquals(equals='openai/gpt-4o:extended'), prices=ModelPrice(input_mtok=Decimal('6'), output_mtok=Decimal('18')), ), ModelInfo( id='openai/gpt-5', match=ClauseOr( or_=[ ClauseEquals(equals='openai/gpt-5'), ClauseEquals(equals='openai/gpt-5-2025-08-07'), ClauseEquals(equals='openai/gpt-5-chat'), ClauseEquals(equals='openai/gpt-5-codex'), ClauseEquals(equals='openai/gpt-5.1-codex-max'), ] ), name='GPT-5', context_window=400000, prices=ModelPrice( input_mtok=Decimal('1.25'), cache_read_mtok=Decimal('0.125'), output_mtok=Decimal('10') ), ), ModelInfo( id='openai/gpt-5-image', match=ClauseEquals(equals='openai/gpt-5-image'), name='GPT-5 Image', context_window=400000, prices=ModelPrice(input_mtok=Decimal('10'), cache_read_mtok=Decimal('1.25'), output_mtok=Decimal('10')), ), ModelInfo( id='openai/gpt-5-image-mini', match=ClauseEquals(equals='openai/gpt-5-image-mini'), name='GPT-5 Image Mini', context_window=400000, prices=ModelPrice(input_mtok=Decimal('2.5'), cache_read_mtok=Decimal('0.25'), output_mtok=Decimal('2')), ), ModelInfo( id='openai/gpt-5-mini', match=ClauseOr( or_=[ClauseEquals(equals='openai/gpt-5-mini'), ClauseEquals(equals='openai/gpt-5-mini-2025-08-07')] ), name='GPT-5 Mini', context_window=400000, prices=ModelPrice( input_mtok=Decimal('0.25'), cache_read_mtok=Decimal('0.025'), output_mtok=Decimal('2') ), ), ModelInfo( id='openai/gpt-5-nano', match=ClauseEquals(equals='openai/gpt-5-nano'), name='GPT-5 Nano', context_window=400000, prices=ModelPrice( input_mtok=Decimal('0.05'), cache_read_mtok=Decimal('0.01'), output_mtok=Decimal('0.4') ), ), ModelInfo( id='openai/gpt-5-pro', match=ClauseEquals(equals='openai/gpt-5-pro'), name='GPT-5 Pro', context_window=400000, prices=ModelPrice(input_mtok=Decimal('15'), output_mtok=Decimal('120')), ), ModelInfo( id='openai/gpt-5.1', match=ClauseOr( or_=[ ClauseEquals(equals='openai/gpt-5.1'), ClauseEquals(equals='openai/gpt-5.1-chat'), ClauseEquals(equals='openai/gpt-5.1-codex'), ] ), name='GPT-5.1', context_window=400000, prices=ModelPrice( input_mtok=Decimal('1.25'), cache_read_mtok=Decimal('0.13'), output_mtok=Decimal('10') ), ), ModelInfo( id='openai/gpt-5.1-codex-mini', match=ClauseOr( or_=[ ClauseEquals(equals='openai/gpt-5.1-codex-mini'), ClauseEquals(equals='openai/gpt-5.1-codex-mini-20251113'), ] ), name='GPT-5.1-Codex-Mini', context_window=400000, prices=ModelPrice( input_mtok=Decimal('0.25'), cache_read_mtok=Decimal('0.025'), output_mtok=Decimal('2') ), ), ModelInfo( id='openai/gpt-5.2', match=ClauseOr( or_=[ ClauseEquals(equals='openai/gpt-5.2'), ClauseEquals(equals='openai/gpt-5.2-20251211'), ClauseEquals(equals='openai/gpt-5.2-chat'), ClauseEquals(equals='openai/gpt-5.2-codex'), ] ), name='GPT-5.2', context_window=400000, prices=ModelPrice( input_mtok=Decimal('1.75'), cache_read_mtok=Decimal('0.175'), output_mtok=Decimal('14') ), ), ModelInfo( id='openai/gpt-5.2-pro', match=ClauseOr( or_=[ClauseEquals(equals='openai/gpt-5.2-pro'), ClauseEquals(equals='openai/gpt-5.2-pro-20251211')] ), name='GPT-5.2 Pro', context_window=400000, prices=ModelPrice(input_mtok=Decimal('21'), output_mtok=Decimal('168')), ), ModelInfo( id='openai/gpt-5.3-chat', match=ClauseEquals(equals='openai/gpt-5.3-chat'), name='GPT-5.3 Chat', prices=ModelPrice( input_mtok=Decimal('1.75'), cache_read_mtok=Decimal('0.175'), output_mtok=Decimal('14') ), ), ModelInfo( id='openai/gpt-5.3-codex', match=ClauseOr( or_=[ ClauseEquals(equals='openai/gpt-5.3-codex'), ClauseEquals(equals='openai/gpt-5.3-codex-20260224'), ] ), name='GPT-5.3-Codex', context_window=400000, prices=ModelPrice( input_mtok=Decimal('1.75'), cache_read_mtok=Decimal('0.175'), output_mtok=Decimal('14') ), ), ModelInfo( id='openai/gpt-5.4', match=ClauseOr( or_=[ClauseEquals(equals='openai/gpt-5.4'), ClauseEquals(equals='openai/gpt-5.4-20260305')] ), name='GPT-5.4', context_window=1050000, price_comments='Long-context tier (>272K prompt tokens) is 2x input and 1.5x output, matching OpenAI. Ref: https://openrouter.ai/api/v1/models (pricing.overrides).', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('2.5'), tiers=[Tier(start=272000, price=Decimal('5'))]), cache_read_mtok=TieredPrices( base=Decimal('0.25'), tiers=[Tier(start=272000, price=Decimal('0.5'))] ), output_mtok=TieredPrices(base=Decimal('15'), tiers=[Tier(start=272000, price=Decimal('22.5'))]), ), ), ModelInfo( id='openai/gpt-5.4-image-2', match=ClauseEquals(equals='openai/gpt-5.4-image-2'), name='GPT-5.4 Image 2', context_window=272000, prices=ModelPrice(input_mtok=Decimal('8'), cache_read_mtok=Decimal('2'), output_mtok=Decimal('15')), ), ModelInfo( id='openai/gpt-5.4-mini', match=ClauseEquals(equals='openai/gpt-5.4-mini'), name='GPT-5.4 Mini', context_window=400000, prices=ModelPrice( input_mtok=Decimal('0.75'), cache_read_mtok=Decimal('0.075'), output_mtok=Decimal('4.5') ), ), ModelInfo( id='openai/gpt-5.4-nano', match=ClauseEquals(equals='openai/gpt-5.4-nano'), name='GPT-5.4 Nano', context_window=400000, prices=ModelPrice( input_mtok=Decimal('0.2'), cache_read_mtok=Decimal('0.02'), output_mtok=Decimal('1.25') ), ), ModelInfo( id='openai/gpt-5.4-pro', match=ClauseEquals(equals='openai/gpt-5.4-pro'), name='GPT-5.4 Pro', context_window=1050000, price_comments='Long-context tier (>272K prompt tokens) is 2x input and 1.5x output, matching OpenAI. Ref: https://openrouter.ai/api/v1/models (pricing.overrides).', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('30'), tiers=[Tier(start=272000, price=Decimal('60'))]), output_mtok=TieredPrices(base=Decimal('180'), tiers=[Tier(start=272000, price=Decimal('270'))]), ), ), ModelInfo( id='openai/gpt-5.5', match=ClauseEquals(equals='openai/gpt-5.5'), name='GPT-5.5', context_window=1050000, price_comments='Long-context tier (>272K prompt tokens) is 2x input and 1.5x output, matching OpenAI. Ref: https://openrouter.ai/api/v1/models (pricing.overrides).', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('5'), tiers=[Tier(start=272000, price=Decimal('10'))]), cache_read_mtok=TieredPrices(base=Decimal('0.5'), tiers=[Tier(start=272000, price=Decimal('1'))]), output_mtok=TieredPrices(base=Decimal('30'), tiers=[Tier(start=272000, price=Decimal('45'))]), ), ), ModelInfo( id='openai/gpt-5.5-pro', match=ClauseEquals(equals='openai/gpt-5.5-pro'), name='GPT-5.5 Pro', context_window=1050000, price_comments='Long-context tier (>272K prompt tokens) is 2x input and 1.5x output, matching OpenAI. Ref: https://openrouter.ai/api/v1/models (pricing.overrides).', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('30'), tiers=[Tier(start=272000, price=Decimal('60'))]), output_mtok=TieredPrices(base=Decimal('180'), tiers=[Tier(start=272000, price=Decimal('270'))]), ), ), ModelInfo( id='openai/gpt-5.6-luna', match=ClauseOr( or_=[ ClauseEquals(equals='openai/gpt-5.6-luna'), ClauseRegex(regex='^openai/gpt-5\\.6-luna-\\d{8}$'), ] ), name='GPT-5.6 Luna', context_window=1050000, price_comments='Cache writes are billed at 1.25x the uncached input rate. Long-context tier (>272K prompt tokens) is 2x input and 1.5x output, matching OpenAI. Ref: https://openrouter.ai/api/v1/models (pricing.overrides).', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('0.2'), tiers=[Tier(start=272000, price=Decimal('0.4'))]), cache_write_mtok=TieredPrices( base=Decimal('0.25'), tiers=[Tier(start=272000, price=Decimal('0.5'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.02'), tiers=[Tier(start=272000, price=Decimal('0.04'))] ), output_mtok=TieredPrices(base=Decimal('1.2'), tiers=[Tier(start=272000, price=Decimal('1.8'))]), ), ), ModelInfo( id='openai/gpt-5.6-sol', match=ClauseOr( or_=[ClauseEquals(equals='openai/gpt-5.6-sol'), ClauseRegex(regex='^openai/gpt-5\\.6-sol-\\d{8}$')] ), name='GPT-5.6 Sol', context_window=1050000, price_comments='Cache writes are billed at 1.25x the uncached input rate. Long-context tier (>272K prompt tokens) is 2x input and 1.5x output, matching OpenAI. Ref: https://openrouter.ai/api/v1/models (pricing.overrides).', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('2'), tiers=[Tier(start=272000, price=Decimal('4'))]), cache_write_mtok=TieredPrices(base=Decimal('2.5'), tiers=[Tier(start=272000, price=Decimal('5'))]), cache_read_mtok=TieredPrices(base=Decimal('0.2'), tiers=[Tier(start=272000, price=Decimal('0.4'))]), output_mtok=TieredPrices(base=Decimal('10'), tiers=[Tier(start=272000, price=Decimal('15'))]), ), ), ModelInfo( id='openai/gpt-5.6-terra', match=ClauseOr( or_=[ ClauseEquals(equals='openai/gpt-5.6-terra'), ClauseRegex(regex='^openai/gpt-5\\.6-terra-\\d{8}$'), ] ), name='GPT-5.6 Terra', context_window=1050000, price_comments='Cache writes are billed at 1.25x the uncached input rate. Long-context tier (>272K prompt tokens) is 2x input and 1.5x output, matching OpenAI. Ref: https://openrouter.ai/api/v1/models (pricing.overrides).', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('2'), tiers=[Tier(start=272000, price=Decimal('4'))]), cache_write_mtok=TieredPrices(base=Decimal('2.5'), tiers=[Tier(start=272000, price=Decimal('5'))]), cache_read_mtok=TieredPrices(base=Decimal('0.2'), tiers=[Tier(start=272000, price=Decimal('0.4'))]), output_mtok=TieredPrices(base=Decimal('12'), tiers=[Tier(start=272000, price=Decimal('18'))]), ), ), ModelInfo( id='openai/gpt-6-luna', match=ClauseOr( or_=[ ClauseEquals(equals='openai/gpt-6-luna'), ClauseEquals(equals='openai/gpt-6-luna-pro'), ClauseRegex(regex='^openai/gpt-6-luna-\\d{8}$'), ] ), name='GPT-6 Luna', context_window=1050000, price_comments='OpenRouter lists the base and pro routes at the same rates. Long-context tier (>272K prompt tokens) is 2x input and cache rates and 1.5x output. Ref: https://openrouter.ai/api/v1/models (pricing.overrides).', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('0.1'), tiers=[Tier(start=272000, price=Decimal('0.2'))]), cache_write_mtok=TieredPrices( base=Decimal('0.125'), tiers=[Tier(start=272000, price=Decimal('0.25'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.01'), tiers=[Tier(start=272000, price=Decimal('0.02'))] ), output_mtok=TieredPrices(base=Decimal('0.5'), tiers=[Tier(start=272000, price=Decimal('0.75'))]), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='openai/gpt-6-luna:batch', match=ClauseOr( or_=[ ClauseEquals(equals='openai/gpt-6-luna:batch'), ClauseEquals(equals='openai/gpt-6-luna-pro:batch'), ] ), name='GPT-6 Luna Batch', context_window=1050000, price_comments="OpenRouter's batch routes bill input, cache, and output tokens at half the standard rates; web searches retain their $0.01 per-call rate. Ref: https://openrouter.ai/api/v1/models.", prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('0.05'), tiers=[Tier(start=272000, price=Decimal('0.1'))]), cache_write_mtok=TieredPrices( base=Decimal('0.0625'), tiers=[Tier(start=272000, price=Decimal('0.125'))] ), cache_read_mtok=TieredPrices( base=Decimal('0.005'), tiers=[Tier(start=272000, price=Decimal('0.01'))] ), output_mtok=TieredPrices(base=Decimal('0.25'), tiers=[Tier(start=272000, price=Decimal('0.375'))]), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='openai/gpt-6-sol', match=ClauseOr( or_=[ ClauseEquals(equals='openai/gpt-6-sol'), ClauseEquals(equals='openai/gpt-6-sol-pro'), ClauseRegex(regex='^openai/gpt-6-sol-\\d{8}$'), ] ), name='GPT-6 Sol', context_window=1050000, price_comments='OpenRouter lists the base and pro routes at the same rates. Long-context tier (>272K prompt tokens) is 2x input and cache rates and 1.5x output. Ref: https://openrouter.ai/api/v1/models (pricing.overrides).', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('2'), tiers=[Tier(start=272000, price=Decimal('4'))]), cache_write_mtok=TieredPrices(base=Decimal('2.5'), tiers=[Tier(start=272000, price=Decimal('5'))]), cache_read_mtok=TieredPrices(base=Decimal('0.2'), tiers=[Tier(start=272000, price=Decimal('0.4'))]), output_mtok=TieredPrices(base=Decimal('10'), tiers=[Tier(start=272000, price=Decimal('15'))]), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='openai/gpt-6-sol:batch', match=ClauseOr( or_=[ ClauseEquals(equals='openai/gpt-6-sol:batch'), ClauseEquals(equals='openai/gpt-6-sol-pro:batch'), ] ), name='GPT-6 Sol Batch', context_window=1050000, price_comments="OpenRouter's batch routes bill input, cache, and output tokens at half the standard rates; web searches retain their $0.01 per-call rate. Ref: https://openrouter.ai/api/v1/models.", prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('1'), tiers=[Tier(start=272000, price=Decimal('2'))]), cache_write_mtok=TieredPrices( base=Decimal('1.25'), tiers=[Tier(start=272000, price=Decimal('2.5'))] ), cache_read_mtok=TieredPrices(base=Decimal('0.1'), tiers=[Tier(start=272000, price=Decimal('0.2'))]), output_mtok=TieredPrices(base=Decimal('5'), tiers=[Tier(start=272000, price=Decimal('7.5'))]), web_searches_kcount=Decimal('10'), ), ), ModelInfo( id='openai/gpt-audio', match=ClauseEquals(equals='openai/gpt-audio'), name='GPT Audio', context_window=128000, prices=ModelPrice(input_mtok=Decimal('2.5'), output_mtok=Decimal('10')), ), ModelInfo( id='openai/gpt-audio-mini', match=ClauseEquals(equals='openai/gpt-audio-mini'), name='GPT Audio Mini', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.6'), output_mtok=Decimal('2.4')), ), ModelInfo( id='openai/gpt-chat-latest', 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id='x-ai/grok-4.20-multi-agent', match=ClauseEquals(equals='x-ai/grok-4.20-multi-agent'), name='Grok 4.20 Multi-Agent', context_window=2000000, prices=ModelPrice(input_mtok=Decimal('2'), cache_read_mtok=Decimal('0.2'), output_mtok=Decimal('6')), ), ModelInfo( id='x-ai/grok-4.3', match=ClauseOr( or_=[ClauseEquals(equals='x-ai/grok-4.3'), ClauseRegex(regex='^x-ai/grok-4\\.3-\\d{8}$')] ), name='Grok 4.3', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('1.25'), cache_read_mtok=Decimal('0.2'), output_mtok=Decimal('2.5') ), ), ModelInfo( id='x-ai/grok-beta', match=ClauseEquals(equals='x-ai/grok-beta'), prices=ModelPrice(input_mtok=Decimal('5'), output_mtok=Decimal('15')), ), ModelInfo( id='x-ai/grok-build-0.1', match=ClauseEquals(equals='x-ai/grok-build-0.1'), name='Grok Build 0.1', context_window=256000, prices=ModelPrice(input_mtok=Decimal('1'), cache_read_mtok=Decimal('0.2'), output_mtok=Decimal('2')), ), ModelInfo( id='x-ai/grok-code-fast-1', match=ClauseEquals(equals='x-ai/grok-code-fast-1'), context_window=256000, prices=ModelPrice( input_mtok=Decimal('0.2'), cache_read_mtok=Decimal('0.02'), output_mtok=Decimal('1.5') ), ), ModelInfo( id='x-ai/grok-vision-beta', match=ClauseEquals(equals='x-ai/grok-vision-beta'), prices=ModelPrice(input_mtok=Decimal('5'), output_mtok=Decimal('15')), ), ModelInfo( id='xiaomi/mimo-v2-flash', match=ClauseEquals(equals='xiaomi/mimo-v2-flash'), name='MiMo-V2-Flash', prices=ModelPrice( input_mtok=Decimal('0.1'), cache_read_mtok=Decimal('0.01'), output_mtok=Decimal('0.3') ), ), ModelInfo( id='xiaomi/mimo-v2.5', match=ClauseEquals(equals='xiaomi/mimo-v2.5'), name='MiMo-V2.5', prices=ModelPrice( input_mtok=Decimal('0.14'), cache_read_mtok=Decimal('0.0028'), output_mtok=Decimal('0.28') ), ), ModelInfo( id='xiaomi/mimo-v2.5-pro', match=ClauseEquals(equals='xiaomi/mimo-v2.5-pro'), name='MiMo-V2.5-Pro', prices=ModelPrice( input_mtok=Decimal('0.435'), cache_read_mtok=Decimal('0.0036'), output_mtok=Decimal('0.87') ), ), ModelInfo( id='xwin-lm/xwin-lm-70b', match=ClauseEquals(equals='xwin-lm/xwin-lm-70b'), prices=ModelPrice(input_mtok=Decimal('3.75'), output_mtok=Decimal('3.75')), ), ModelInfo( id='yi-large', match=ClauseEquals(equals='yi-large'), name='Yi Large', prices=ModelPrice(input_mtok=Decimal('3'), output_mtok=Decimal('3')), ), ModelInfo( id='z-ai/glm-4.5', match=ClauseEquals(equals='z-ai/glm-4.5'), name='GLM 4.5', context_window=131072, prices=ModelPrice( input_mtok=Decimal('0.6'), cache_read_mtok=Decimal('0.11'), output_mtok=Decimal('2.2') ), ), ModelInfo( id='z-ai/glm-4.5-air', match=ClauseEquals(equals='z-ai/glm-4.5-air'), name='GLM 4.5 Air', context_window=131072, prices=ModelPrice( input_mtok=Decimal('0.125'), cache_read_mtok=Decimal('0.06'), output_mtok=Decimal('0.85') ), ), ModelInfo( id='z-ai/glm-4.5v', match=ClauseEquals(equals='z-ai/glm-4.5v'), name='GLM 4.5V', context_window=65536, prices=ModelPrice( input_mtok=Decimal('0.6'), cache_read_mtok=Decimal('0.11'), output_mtok=Decimal('1.8') ), ), ModelInfo( id='z-ai/glm-4.6', match=ClauseEquals(equals='z-ai/glm-4.6'), name='GLM 4.6', prices=ModelPrice( input_mtok=Decimal('0.43'), cache_read_mtok=Decimal('0.08'), output_mtok=Decimal('1.74') ), ), ModelInfo( id='z-ai/glm-4.6v', match=ClauseEquals(equals='z-ai/glm-4.6v'), name='GLM 4.6V', context_window=131072, prices=ModelPrice( input_mtok=Decimal('0.3'), cache_read_mtok=Decimal('0.05'), output_mtok=Decimal('0.9') ), ), ModelInfo( id='z-ai/glm-4.7', match=ClauseEquals(equals='z-ai/glm-4.7'), name='GLM 4.7', prices=ModelPrice( input_mtok=Decimal('0.4'), cache_read_mtok=Decimal('0.08'), output_mtok=Decimal('1.75') ), ), ModelInfo( id='z-ai/glm-4.7-flash', match=ClauseOr( or_=[ClauseEquals(equals='z-ai/glm-4.7-flash'), ClauseEquals(equals='z-ai/glm-4.7-flash-20260119')] ), name='GLM 4.7 Flash', prices=ModelPrice( input_mtok=Decimal('0.06'), cache_read_mtok=Decimal('0.01'), output_mtok=Decimal('0.4') ), ), ModelInfo( id='z-ai/glm-5', match=ClauseOr(or_=[ClauseEquals(equals='z-ai/glm-5'), ClauseEquals(equals='z-ai/glm-5-20260211')]), name='GLM 5', prices=ModelPrice( input_mtok=Decimal('0.6'), cache_read_mtok=Decimal('0.12'), output_mtok=Decimal('1.92') ), ), ModelInfo( id='z-ai/glm-5-turbo', match=ClauseOr( or_=[ClauseEquals(equals='z-ai/glm-5-turbo'), ClauseEquals(equals='z-ai/glm-5-turbo-20260315')] ), name='GLM 5 Turbo', context_window=202752, prices=ModelPrice(input_mtok=Decimal('1.2'), cache_read_mtok=Decimal('0.24'), output_mtok=Decimal('4')), ), ModelInfo( id='z-ai/glm-5.1', match=ClauseOr(or_=[ClauseEquals(equals='z-ai/glm-5.1'), ClauseEquals(equals='z-ai/glm-5.1-20260406')]), name='GLM 5.1', prices=ModelPrice( input_mtok=Decimal('0.98'), cache_read_mtok=Decimal('0.182'), output_mtok=Decimal('3.08') ), ), ModelInfo( id='z-ai/glm-5.2', match=ClauseOr(or_=[ClauseEquals(equals='z-ai/glm-5.2'), ClauseEquals(equals='z-ai/glm-5.2-20260616')]), name='GLM 5.2', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('1.4'), cache_read_mtok=Decimal('0.26'), output_mtok=Decimal('4.4') ), ), ModelInfo( id='z-ai/glm-5.3', match=ClauseOr(or_=[ClauseEquals(equals='z-ai/glm-5.3'), ClauseEquals(equals='z-ai/glm-5.3-20260816')]), name='GLM 5.3', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('1.4'), cache_read_mtok=Decimal('0.26'), output_mtok=Decimal('4.4') ), ), ModelInfo( id='z-ai/glm-5.3-flash', match=ClauseEquals(equals='z-ai/glm-5.3-flash'), name='GLM 5.3 Flash', context_window=1310720, price_comments="OpenRouter's model page and models API show a 50% promotion through September 9, 2026 at 16:00 UTC. List prices are $0.15 input, $0.03 cached input, and $0.50 output per million tokens.", prices=ModelPrice( input_mtok=Decimal('0.075'), cache_read_mtok=Decimal('0.015'), output_mtok=Decimal('0.25') ), ), ModelInfo( id='~anthropic/claude-fable-latest', match=ClauseEquals(equals='~anthropic/claude-fable-latest'), name='Claude Fable Latest', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('10'), cache_write_mtok=Decimal('12.5'), cache_read_mtok=Decimal('1'), output_mtok=Decimal('50'), ), ), ModelInfo( id='~anthropic/claude-haiku-latest', match=ClauseEquals(equals='~anthropic/claude-haiku-latest'), name='Anthropic Claude Haiku Latest', context_window=200000, prices=ModelPrice( input_mtok=Decimal('1'), cache_write_mtok=Decimal('1.25'), cache_read_mtok=Decimal('0.1'), output_mtok=Decimal('5'), ), ), ModelInfo( id='~anthropic/claude-opus-latest', match=ClauseEquals(equals='~anthropic/claude-opus-latest'), name='Claude Opus Latest', context_window=1000000, prices=[ ConditionalPrice( prices=ModelPrice( input_mtok=Decimal('5'), cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), ) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2026, 9, 22)), prices=ModelPrice( input_mtok=Decimal('4'), cache_write_mtok=Decimal('5'), cache_read_mtok=Decimal('0.2'), output_mtok=Decimal('20'), ), ), ], ), ModelInfo( id='~anthropic/claude-sonnet-latest', match=ClauseEquals(equals='~anthropic/claude-sonnet-latest'), name='Anthropic Claude Sonnet Latest', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('3'), cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), ), ), ModelInfo( id='~google/gemini-flash-latest', match=ClauseEquals(equals='~google/gemini-flash-latest'), name='Google Gemini Flash Latest', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('1.5'), cache_write_mtok=Decimal('0.08333333333333334'), cache_read_mtok=Decimal('0.15'), output_mtok=Decimal('9'), ), ), ModelInfo( id='~google/gemini-pro-latest', match=ClauseEquals(equals='~google/gemini-pro-latest'), name='Google Gemini Pro Latest', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('2'), cache_write_mtok=Decimal('0.375'), cache_read_mtok=Decimal('0.2'), output_mtok=Decimal('12'), ), ), ModelInfo( id='~moonshotai/kimi-latest', match=ClauseEquals(equals='~moonshotai/kimi-latest'), name='MoonshotAI Kimi Latest', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('0.68'), cache_read_mtok=Decimal('0.34'), output_mtok=Decimal('3.41') ), ), ModelInfo( id='~openai/gpt-latest', match=ClauseEquals(equals='~openai/gpt-latest'), name='OpenAI GPT Latest', context_window=1050000, prices=ModelPrice(input_mtok=Decimal('5'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('30')), ), ModelInfo( id='~openai/gpt-mini-latest', match=ClauseEquals(equals='~openai/gpt-mini-latest'), name='OpenAI GPT Mini Latest', context_window=400000, prices=ModelPrice( input_mtok=Decimal('0.75'), cache_read_mtok=Decimal('0.075'), output_mtok=Decimal('4.5') ), ), ], ), Provider( id='ovhcloud', name='OVHcloud AI Endpoints', api_pattern='https://oai\\.endpoints\\.kepler\\.ai\\.cloud\\.ovh\\.net', pricing_urls=['https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/models'], extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), ], models=[ ModelInfo( id='DeepSeek-R1-Distill-Llama-70B', match=ClauseOr( or_=[ ClauseEquals(equals='DeepSeek-R1-Distill-Llama-70B'), ClauseEquals(equals='deepseek-r1-distill-llama-70b'), ] ), name='DeepSeek-R1-Distill-Llama-70B', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.74'), output_mtok=Decimal('0.74')), ), ModelInfo( id='Llama-3.1-8B-Instruct', match=ClauseOr( or_=[ClauseEquals(equals='Llama-3.1-8B-Instruct'), ClauseEquals(equals='llama-3.1-8b-instruct')] ), name='Llama-3.1-8B-Instruct', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.11'), output_mtok=Decimal('0.11')), ), ModelInfo( id='Meta-Llama-3_3-70B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='Meta-Llama-3_3-70B-Instruct'), ClauseEquals(equals='meta-llama-3_3-70b-instruct'), ] ), name='Meta-Llama-3_3-70B-Instruct', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.74'), output_mtok=Decimal('0.74')), ), ModelInfo( id='Mistral-7B-Instruct-v0.3', match=ClauseOr( or_=[ ClauseEquals(equals='Mistral-7B-Instruct-v0.3'), ClauseEquals(equals='mistral-7b-instruct-v0.3'), ] ), name='Mistral-7B-Instruct-v0.3', context_window=65536, prices=ModelPrice(input_mtok=Decimal('0.11'), output_mtok=Decimal('0.11')), ), ModelInfo( id='Mistral-Nemo-Instruct-2407', match=ClauseOr( or_=[ ClauseEquals(equals='Mistral-Nemo-Instruct-2407'), ClauseEquals(equals='mistral-nemo-instruct-2407'), ] ), name='Mistral-Nemo-Instruct-2407', context_window=65536, prices=ModelPrice(input_mtok=Decimal('0.14'), output_mtok=Decimal('0.14')), ), ModelInfo( id='Mistral-Small-3.2-24B-Instruct-2506', match=ClauseOr( or_=[ ClauseEquals(equals='Mistral-Small-3.2-24B-Instruct-2506'), ClauseEquals(equals='mistral-small-3.2-24b-instruct-2506'), ] ), name='Mistral-Small-3.2-24B-Instruct-2506', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.31')), ), ModelInfo( id='Mixtral-8x7B-Instruct-v0.1', match=ClauseOr( or_=[ ClauseEquals(equals='Mixtral-8x7B-Instruct-v0.1'), ClauseEquals(equals='mixtral-8x7b-instruct-v0.1'), ] ), name='Mixtral-8x7B-Instruct-v0.1', context_window=32768, prices=ModelPrice(input_mtok=Decimal('0.7'), output_mtok=Decimal('0.7')), ), ModelInfo( id='Qwen2.5-VL-72B-Instruct', match=ClauseOr( or_=[ClauseEquals(equals='Qwen2.5-VL-72B-Instruct'), ClauseEquals(equals='qwen2.5-vl-72b-instruct')] ), name='Qwen2.5-VL-72B-Instruct', context_window=32768, prices=ModelPrice(input_mtok=Decimal('1.01'), output_mtok=Decimal('1.01')), ), ModelInfo( id='Qwen3-32B', match=ClauseOr(or_=[ClauseEquals(equals='Qwen3-32B'), ClauseEquals(equals='qwen3-32b')]), name='Qwen3-32B', context_window=32768, prices=ModelPrice(input_mtok=Decimal('0.09'), output_mtok=Decimal('0.25')), ), ModelInfo( id='Qwen3-Coder-30B-A3B-Instruct', match=ClauseOr( or_=[ ClauseEquals(equals='Qwen3-Coder-30B-A3B-Instruct'), ClauseEquals(equals='qwen3-coder-30b-a3b-instruct'), ] ), name='Qwen3-Coder-30B-A3B-Instruct', context_window=262144, prices=ModelPrice(input_mtok=Decimal('0.07'), output_mtok=Decimal('0.26')), ), ModelInfo( id='bge-base-en-v1.5', match=ClauseEquals(equals='bge-base-en-v1.5'), name='bge-base-en-v1.5', context_window=512, prices=ModelPrice(input_mtok=Decimal('0.01')), ), ModelInfo( id='bge-m3', match=ClauseEquals(equals='bge-m3'), name='bge-m3', context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.01')), ), ModelInfo( id='bge-multilingual-gemma2', match=ClauseEquals(equals='bge-multilingual-gemma2'), name='bge-multilingual-gemma2', context_window=8192, prices=ModelPrice(input_mtok=Decimal('0.01')), ), ModelInfo( id='gpt-oss-120b', match=ClauseEquals(equals='gpt-oss-120b'), name='gpt-oss-120b', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.09'), output_mtok=Decimal('0.47')), ), ModelInfo( id='gpt-oss-20b', match=ClauseEquals(equals='gpt-oss-20b'), name='gpt-oss-20b', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.05'), output_mtok=Decimal('0.18')), ), ], ), Provider( id='perplexity', name='Perplexity', api_pattern='https://api\\.perplexity\\.ai', pricing_urls=['https://docs.perplexity.ai/docs/getting-started/pricing'], price_comments='Prices per request vary based on usage, this is not represented here, instead we just take the highest price shown for `requests_kcount`.', extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping(path='reasoning_tokens', dest='output_tokens', required=False), UsageExtractorMapping(path='reasoning_tokens', dest='output_reasoning_tokens', required=False), UsageExtractorMapping(path='citation_tokens', dest='output_tokens', required=False), UsageExtractorMapping(path='citation_tokens', dest='output_citation_tokens', required=False), UsageExtractorMapping(path='num_search_queries', dest='web_searches', required=False), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ) ], models=[ ModelInfo( id='llama-3.1-sonar-large-128k-online', match=ClauseEquals(equals='llama-3.1-sonar-large-128k-online'), name='Llama 3.1 Sonar 70B Online', description="Llama 3.1 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.", prices=ModelPrice(input_mtok=Decimal('1'), output_mtok=Decimal('1')), ), ModelInfo( id='llama-3.1-sonar-small-128k-online', match=ClauseEquals(equals='llama-3.1-sonar-small-128k-online'), name='Llama 3.1 Sonar 8B Online', description="Llama 3.1 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.", prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='r1-1776', match=ClauseEquals(equals='r1-1776'), name='R1 1776', description='R1 1776 is a version of DeepSeek-R1 that has been post-trained to remove censorship constraints related to topics restricted by the Chinese government. The model retains its original reasoning capabilities while providing direct responses to a wider range of queries. R1 1776 is an offline chat model that does not use the perplexity search subsystem.', prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('8')), ), ModelInfo( id='sonar', match=ClauseEquals(equals='sonar'), name='Sonar', description='Sonar is lightweight, affordable, fast, and simple to use — now featuring citations and the ability to customize sources. It is designed for companies seeking to integrate lightweight question-and-answer features optimized for speed.', context_window=128000, prices=ModelPrice(input_mtok=Decimal('1'), output_mtok=Decimal('1'), requests_kcount=Decimal('12')), ), ModelInfo( id='sonar-deep-research', match=ClauseEquals(equals='sonar-deep-research'), name='Sonar Deep Research', description='Sonar Deep Research is a research-focused model designed for multi-step retrieval, synthesis, and reasoning across complex topics. It autonomously searches, reads, and evaluates sources, refining its approach as it gathers information. This enables comprehensive report generation across domains like finance, technology, health, and current events.', context_window=128000, price_comments='Perplexity lists reasoning tokens separately at $3 per million tokens and citation tokens at $2 per million tokens. Search queries cost $5 per thousand.', prices=ModelPrice( input_mtok=Decimal('2'), output_mtok=Decimal('8'), output_reasoning_mtok=Decimal('3'), output_citation_mtok=Decimal('2'), web_searches_kcount=Decimal('5'), ), ), ModelInfo( id='sonar-pro', match=ClauseEquals(equals='sonar-pro'), name='Sonar Pro', description='Note: Sonar Pro pricing includes Perplexity search pricing. See details here', context_window=200000, prices=ModelPrice(input_mtok=Decimal('3'), output_mtok=Decimal('15'), requests_kcount=Decimal('14')), ), ModelInfo( id='sonar-pro-search', match=ClauseEquals(equals='sonar-pro-search'), name='Sonar Pro Search', description="Sonar Pro Search is Perplexity's advanced agentic search system for deeper reasoning and analysis.", price_comments='Imported from OpenRouter pricing; verify against Perplexity pricing when native API pricing is published.', prices=ModelPrice(input_mtok=Decimal('3'), output_mtok=Decimal('15')), ), ModelInfo( id='sonar-reasoning', match=ClauseEquals(equals='sonar-reasoning'), name='Sonar Reasoning', description='Sonar Reasoning is a reasoning model provided by Perplexity based on DeepSeek R1.', prices=ModelPrice(input_mtok=Decimal('1'), output_mtok=Decimal('5'), requests_kcount=Decimal('12')), ), ModelInfo( id='sonar-reasoning-pro', match=ClauseEquals(equals='sonar-reasoning-pro'), name='Sonar Reasoning Pro', description='Sonar Pro pricing includes Perplexity search pricing.', context_window=128000, prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('8'), requests_kcount=Decimal('14')), ), ], ), Provider( id='quicksilverpro', name='QuickSilver Pro', api_pattern='https://api\\.quicksilverpro\\.io', pricing_urls=['https://quicksilverpro.io/#pricing', 'https://quicksilverpro.io/pricing.json'], description='OpenAI-compatible inference gateway serving frontier and open models via one endpoint.', extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['prompt_tokens_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['input_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['input_tokens_details', 'cache_write_tokens'], dest='cache_write_tokens', required=False ), UsageExtractorMapping( path=['output_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=True), ], api_flavor='responses', model_path='model', ), ], models=[ ModelInfo( id='claude-fable-5', match=ClauseEquals(equals='claude-fable-5'), name='Claude Fable 5', context_window=1000000, prices=ModelPrice(input_mtok=Decimal('8'), output_mtok=Decimal('40')), ), ModelInfo( id='claude-haiku-4-5', match=ClauseEquals(equals='claude-haiku-4-5'), name='Claude Haiku 4.5', context_window=200000, prices=ModelPrice(input_mtok=Decimal('0.8'), output_mtok=Decimal('4')), ), ModelInfo( id='claude-opus-4-6', match=ClauseEquals(equals='claude-opus-4-6'), name='Claude Opus 4.6', context_window=1000000, prices=ModelPrice(input_mtok=Decimal('4'), output_mtok=Decimal('20')), ), ModelInfo( id='claude-opus-4-8', match=ClauseEquals(equals='claude-opus-4-8'), name='Claude Opus 4.8', context_window=1000000, prices=ModelPrice(input_mtok=Decimal('4'), output_mtok=Decimal('20')), ), ModelInfo( id='claude-opus-5', match=ClauseEquals(equals='claude-opus-5'), name='Claude Opus 5', context_window=1000000, prices=ModelPrice(input_mtok=Decimal('4'), output_mtok=Decimal('20')), ), ModelInfo( id='claude-sonnet-4-6', match=ClauseEquals(equals='claude-sonnet-4-6'), name='Claude Sonnet 4.6', context_window=1000000, prices=ModelPrice(input_mtok=Decimal('2.4'), output_mtok=Decimal('12')), ), ModelInfo( id='claude-sonnet-5', match=ClauseEquals(equals='claude-sonnet-5'), name='Claude Sonnet 5', context_window=1000000, prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('10')), ), ModelInfo( id='deepseek-v4-flash', match=ClauseEquals(equals='deepseek-v4-flash'), name='DeepSeek V4 Flash', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('0.112'), cache_read_mtok=Decimal('0.0144'), output_mtok=Decimal('0.224') ), ), ModelInfo( id='deepseek-v4-pro', match=ClauseEquals(equals='deepseek-v4-pro'), name='DeepSeek V4 Pro', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('0.435'), cache_read_mtok=Decimal('0.003625'), output_mtok=Decimal('0.87') ), ), ModelInfo( id='gemini-3-flash-preview', match=ClauseEquals(equals='gemini-3-flash-preview'), name='Gemini 3 Flash Preview', context_window=1048576, deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.425'), output_mtok=Decimal('2.55')), ), ModelInfo( id='gemini-3-pro-image', match=ClauseEquals(equals='gemini-3-pro-image'), name='Gemini 3 Pro Image', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('1.7'), output_mtok=Decimal('10.2'), output_image_mtok=Decimal('102') ), ), ModelInfo( id='gemini-3.1-flash-lite', match=ClauseEquals(equals='gemini-3.1-flash-lite'), name='Gemini 3.1 Flash Lite', context_window=1048576, deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.2125'), output_mtok=Decimal('1.275')), ), ModelInfo( id='gemini-3.1-pro-preview', match=ClauseEquals(equals='gemini-3.1-pro-preview'), name='Gemini 3.1 Pro Preview', context_window=1048576, prices=ModelPrice(input_mtok=Decimal('1.7'), output_mtok=Decimal('10.2')), ), ModelInfo( id='gemini-3.5-flash', match=ClauseEquals(equals='gemini-3.5-flash'), name='Gemini 3.5 Flash', context_window=1048576, prices=ModelPrice(input_mtok=Decimal('1.275'), output_mtok=Decimal('7.65')), ), ModelInfo( id='gemini-3.5-flash-lite', match=ClauseEquals(equals='gemini-3.5-flash-lite'), name='Gemini 3.5 Flash-Lite', context_window=1048576, prices=ModelPrice(input_mtok=Decimal('0.255'), output_mtok=Decimal('2.125')), ), ModelInfo( id='gemini-3.6-flash', match=ClauseEquals(equals='gemini-3.6-flash'), name='Gemini 3.6 Flash', context_window=1048576, prices=ModelPrice(input_mtok=Decimal('1.275'), output_mtok=Decimal('6.375')), ), ModelInfo( id='gemini-3.7-flash', match=ClauseEquals(equals='gemini-3.7-flash'), name='Gemini 3.7 Flash', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('0.6375'), cache_read_mtok=Decimal('0.06375'), output_mtok=Decimal('3.1875') ), ), ModelInfo( id='glm-5.2', match=ClauseEquals(equals='glm-5.2'), name='GLM 5.2', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('1.12'), cache_read_mtok=Decimal('0.208'), output_mtok=Decimal('3.52') ), ), ModelInfo( id='glm-5.3', match=ClauseEquals(equals='glm-5.3'), name='GLM 5.3', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('1.12'), cache_read_mtok=Decimal('0.208'), output_mtok=Decimal('3.52') ), ), ModelInfo( id='glm-5.3-flash', match=ClauseEquals(equals='glm-5.3-flash'), name='GLM 5.3 Flash', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('0.06'), cache_read_mtok=Decimal('0.012'), output_mtok=Decimal('0.2') ), ), ModelInfo( id='gpt-5.6-luna', match=ClauseEquals(equals='gpt-5.6-luna'), name='GPT-5.6 Luna', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('0.08'), cache_write_mtok=Decimal('0.1'), cache_read_mtok=Decimal('0.008'), output_mtok=Decimal('0.48'), ), ), ModelInfo( id='gpt-5.6-sol', match=ClauseEquals(equals='gpt-5.6-sol'), name='GPT-5.6 Sol', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('4'), cache_write_mtok=Decimal('5'), cache_read_mtok=Decimal('0.4'), output_mtok=Decimal('24'), ), ), ModelInfo( id='gpt-5.6-terra', match=ClauseEquals(equals='gpt-5.6-terra'), name='GPT-5.6 Terra', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('0.8'), cache_write_mtok=Decimal('1'), cache_read_mtok=Decimal('0.08'), output_mtok=Decimal('4.8'), ), ), ModelInfo( id='gpt-oss-120b', match=ClauseEquals(equals='gpt-oss-120b'), name='GPT-OSS 120B', context_window=131072, prices=ModelPrice( input_mtok=Decimal('0.12'), cache_read_mtok=Decimal('0.06'), output_mtok=Decimal('0.48') ), ), ModelInfo( id='grok-4.5', match=ClauseEquals(equals='grok-4.5'), name='Grok 4.5', context_window=500000, prices=ModelPrice( input_mtok=Decimal('1.6'), cache_read_mtok=Decimal('0.4'), output_mtok=Decimal('4.8') ), ), ModelInfo( id='grok-4.6', match=ClauseEquals(equals='grok-4.6'), name='Grok 4.6', context_window=500000, prices=ModelPrice(input_mtok=Decimal('2'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('6')), ), ModelInfo( id='hy3', match=ClauseEquals(equals='hy3'), name='Hy3', context_window=262144, prices=ModelPrice( input_mtok=Decimal('0.1056'), cache_read_mtok=Decimal('0.0264'), output_mtok=Decimal('0.4224') ), ), ModelInfo( id='kimi-k2.6', match=ClauseEquals(equals='kimi-k2.6'), name='Kimi K2.6', context_window=256000, prices=ModelPrice( input_mtok=Decimal('0.5472'), cache_read_mtok=Decimal('0.292'), output_mtok=Decimal('2.728') ), ), ModelInfo( id='kimi-k2.7-code', match=ClauseEquals(equals='kimi-k2.7-code'), name='Kimi K2.7 Code', context_window=256000, prices=ModelPrice( input_mtok=Decimal('0.584'), cache_read_mtok=Decimal('0.1278'), output_mtok=Decimal('2.8') ), ), ModelInfo( id='kimi-k3', match=ClauseEquals(equals='kimi-k3'), name='Kimi K3', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('2.4'), cache_read_mtok=Decimal('0.24'), output_mtok=Decimal('12') ), ), ModelInfo( id='mimo-v2.5', match=ClauseEquals(equals='mimo-v2.5'), name='MiMo-V2.5', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('0.112'), cache_read_mtok=Decimal('0.00224'), output_mtok=Decimal('0.224') ), ), ModelInfo( id='minimax-m3', match=ClauseEquals(equals='minimax-m3'), name='MiniMax M3', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('0.24'), cache_read_mtok=Decimal('0.048'), output_mtok=Decimal('0.96') ), ), ModelInfo( id='muse-glimmer-30b', match=ClauseEquals(equals='muse-glimmer-30b'), name='Muse Glimmer 30B', context_window=131072, prices=ModelPrice( input_mtok=Decimal('0.28'), cache_read_mtok=Decimal('0.032'), output_mtok=Decimal('1.2') ), ), ModelInfo( id='muse-spark-1.2', match=ClauseEquals(equals='muse-spark-1.2'), name='Muse Spark 1.2', context_window=1048576, prices=ModelPrice(input_mtok=Decimal('1'), cache_read_mtok=Decimal('0.12'), output_mtok=Decimal('3.4')), ), ModelInfo( id='nemotron-3.5-lightning', match=ClauseEquals(equals='nemotron-3.5-lightning'), name='Nemotron 3.5 Lightning', context_window=262144, prices=ModelPrice( input_mtok=Decimal('0.08'), cache_read_mtok=Decimal('0.04'), output_mtok=Decimal('0.2') ), ), ModelInfo( id='qwen3.6-35b', match=ClauseEquals(equals='qwen3.6-35b'), name='Qwen3.6-35B-A3B', context_window=262144, prices=ModelPrice( input_mtok=Decimal('0.112'), cache_read_mtok=Decimal('0.04'), output_mtok=Decimal('0.8') ), ), ModelInfo( id='qwen3.6-plus', match=ClauseEquals(equals='qwen3.6-plus'), name='Qwen3.6 Plus', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('0.26'), cache_write_mtok=Decimal('0.325'), output_mtok=Decimal('1.56') ), ), ModelInfo( id='qwen3.7-flash', match=ClauseEquals(equals='qwen3.7-flash'), name='Qwen3.7 Flash', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('0.024'), cache_read_mtok=Decimal('0.0048'), output_mtok=Decimal('0.104') ), ), ModelInfo( id='qwen3.7-max', match=ClauseEquals(equals='qwen3.7-max'), name='Qwen3.7 Max', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('1.25'), cache_write_mtok=Decimal('1.5625'), cache_read_mtok=Decimal('0.25'), output_mtok=Decimal('3.75'), ), ), ModelInfo( id='qwen3.7-plus', match=ClauseEquals(equals='qwen3.7-plus'), name='Qwen3.7 Plus', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('0.256'), cache_write_mtok=Decimal('0.32'), cache_read_mtok=Decimal('0.0512'), output_mtok=Decimal('1.024'), ), ), ModelInfo( id='qwen3.8-27b', match=ClauseEquals(equals='qwen3.8-27b'), name='Qwen3.8 27B', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('0.34'), cache_write_mtok=Decimal('0.425'), cache_read_mtok=Decimal('0.068'), output_mtok=Decimal('2.04'), ), ), ModelInfo( id='qwen3.8-max', match=ClauseEquals(equals='qwen3.8-max'), name='Qwen3.8 Max', context_window=1048576, prices=ModelPrice( input_mtok=Decimal('2'), cache_write_mtok=Decimal('2.5'), cache_read_mtok=Decimal('0.25'), output_mtok=Decimal('6'), ), ), ], ), Provider( id='together', name='Together AI', api_pattern='https://api\\.together\\.xyz', pricing_urls=['https://www.together.ai/pricing'], provider_match=ClauseOr(or_=[ClauseEquals(equals='together-ai'), ClauseEquals(equals='together_ai')]), models=[ ModelInfo( id='Austism/chronos-hermes-13b', match=ClauseEquals(equals='Austism/chronos-hermes-13b'), prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('0.3')), ), ModelInfo( id='Gryphe/MythoMax-L2-13b', match=ClauseEquals(equals='Gryphe/MythoMax-L2-13b'), prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('0.3')), ), ModelInfo( id='Nexusflow/NexusRaven-V2-13B', match=ClauseEquals(equals='Nexusflow/NexusRaven-V2-13B'), prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('0.3')), ), ModelInfo( id='NousResearch/Nous-Capybara-7B-V1p9', match=ClauseEquals(equals='NousResearch/Nous-Capybara-7B-V1p9'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO', match=ClauseEquals(equals='NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO'), prices=ModelPrice(input_mtok=Decimal('0.9'), output_mtok=Decimal('0.9')), ), ModelInfo( id='NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT', match=ClauseEquals(equals='NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT'), prices=ModelPrice(input_mtok=Decimal('0.9'), output_mtok=Decimal('0.9')), ), ModelInfo( id='NousResearch/Nous-Hermes-2-Yi-34B', match=ClauseEquals(equals='NousResearch/Nous-Hermes-2-Yi-34B'), prices=ModelPrice(input_mtok=Decimal('0.8'), output_mtok=Decimal('0.8')), ), ModelInfo( id='NousResearch/Nous-Hermes-Llama2-13b', match=ClauseEquals(equals='NousResearch/Nous-Hermes-Llama2-13b'), prices=ModelPrice(input_mtok=Decimal('0.225'), output_mtok=Decimal('0.225')), ), ModelInfo( id='NousResearch/Nous-Hermes-llama-2-7b', match=ClauseEquals(equals='NousResearch/Nous-Hermes-llama-2-7b'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='Open-Orca/Mistral-7B-OpenOrca', match=ClauseEquals(equals='Open-Orca/Mistral-7B-OpenOrca'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='Qwen/Qwen1.5-0.5B', match=ClauseOr( or_=[ClauseEquals(equals='Qwen/Qwen1.5-0.5B'), ClauseEquals(equals='Qwen/Qwen1.5-0.5B-Chat')] ), prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ), ModelInfo( id='Qwen/Qwen1.5-1.8B', match=ClauseOr( or_=[ClauseEquals(equals='Qwen/Qwen1.5-1.8B'), ClauseEquals(equals='Qwen/Qwen1.5-1.8B-Chat')] ), prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ), ModelInfo( id='Qwen/Qwen1.5-14B', match=ClauseOr( or_=[ClauseEquals(equals='Qwen/Qwen1.5-14B'), ClauseEquals(equals='Qwen/Qwen1.5-14B-Chat')] ), prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('0.3')), ), ModelInfo( id='Qwen/Qwen1.5-4B', match=ClauseOr( or_=[ClauseEquals(equals='Qwen/Qwen1.5-4B'), ClauseEquals(equals='Qwen/Qwen1.5-4B-Chat')] ), prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ), ModelInfo( id='Qwen/Qwen1.5-72B', match=ClauseEquals(equals='Qwen/Qwen1.5-72B'), prices=ModelPrice(input_mtok=Decimal('0.9'), output_mtok=Decimal('0.9')), ), ModelInfo( id='Qwen/Qwen1.5-7B', match=ClauseOr( or_=[ClauseEquals(equals='Qwen/Qwen1.5-7B'), ClauseEquals(equals='Qwen/Qwen1.5-7B-Chat')] ), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='Undi95/ReMM-SLERP-L2-13B', match=ClauseEquals(equals='Undi95/ReMM-SLERP-L2-13B'), prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('0.3')), ), ModelInfo( id='Undi95/Toppy-M-7B', match=ClauseEquals(equals='Undi95/Toppy-M-7B'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='WizardLM/WizardLM-13B-V1.2', match=ClauseEquals(equals='WizardLM/WizardLM-13B-V1.2'), prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('0.3')), ), ModelInfo( id='allenai/OLMo-7B', match=ClauseOr( or_=[ ClauseEquals(equals='allenai/OLMo-7B'), ClauseEquals(equals='allenai/OLMo-7B-Instruct'), ClauseEquals(equals='allenai/OLMo-7B-Twin-2T'), ] ), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='codellama/CodeLlama-13b-Instruct-hf', match=ClauseEquals(equals='codellama/CodeLlama-13b-Instruct-hf'), prices=ModelPrice(input_mtok=Decimal('0.225'), output_mtok=Decimal('0.225')), ), ModelInfo( id='codellama/CodeLlama-34b-Instruct-hf', match=ClauseEquals(equals='codellama/CodeLlama-34b-Instruct-hf'), prices=ModelPrice(input_mtok=Decimal('0.776'), output_mtok=Decimal('0.776')), ), ModelInfo( id='codellama/CodeLlama-70b-Instruct-hf', match=ClauseEquals(equals='codellama/CodeLlama-70b-Instruct-hf'), prices=ModelPrice(input_mtok=Decimal('0.9'), output_mtok=Decimal('0.9')), ), ModelInfo( id='codellama/CodeLlama-7b-Instruct-hf', match=ClauseEquals(equals='codellama/CodeLlama-7b-Instruct-hf'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='deepseek-ai/deepseek-coder-33b-instruct', match=ClauseEquals(equals='deepseek-ai/deepseek-coder-33b-instruct'), prices=ModelPrice(input_mtok=Decimal('0.8'), output_mtok=Decimal('0.8')), ), ModelInfo( id='garage-bAInd/Platypus2-70B-instruct', match=ClauseEquals(equals='garage-bAInd/Platypus2-70B-instruct'), prices=ModelPrice(input_mtok=Decimal('0.9'), output_mtok=Decimal('0.9')), ), ModelInfo( id='google/gemma-2b', match=ClauseOr(or_=[ClauseEquals(equals='google/gemma-2b'), ClauseEquals(equals='google/gemma-2b-it')]), prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ), ModelInfo( id='google/gemma-7b', match=ClauseOr(or_=[ClauseEquals(equals='google/gemma-7b'), ClauseEquals(equals='google/gemma-7b-it')]), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='lmsys/vicuna-13b-v1.5', match=ClauseEquals(equals='lmsys/vicuna-13b-v1.5'), prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('0.3')), ), ModelInfo( id='lmsys/vicuna-7b-v1.5', match=ClauseEquals(equals='lmsys/vicuna-7b-v1.5'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='meta-llama/Llama-2-13b-chat-hf', match=ClauseEquals(equals='meta-llama/Llama-2-13b-chat-hf'), prices=ModelPrice(input_mtok=Decimal('0.225'), output_mtok=Decimal('0.225')), ), ModelInfo( id='meta-llama/Llama-2-70b-chat-hf', match=ClauseEquals(equals='meta-llama/Llama-2-70b-chat-hf'), prices=ModelPrice(input_mtok=Decimal('0.9'), output_mtok=Decimal('0.9')), ), ModelInfo( id='meta-llama/Llama-2-7b-chat-hf', match=ClauseEquals(equals='meta-llama/Llama-2-7b-chat-hf'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='meta-llama/Llama-3-70b-chat-hf', match=ClauseEquals(equals='meta-llama/Llama-3-70b-chat-hf'), prices=ModelPrice(input_mtok=Decimal('0.9'), output_mtok=Decimal('0.9')), ), ModelInfo( id='meta-llama/Llama-3-8b-chat-hf', match=ClauseEquals(equals='meta-llama/Llama-3-8b-chat-hf'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='meta-llama/Llama-3.3-70B-Instruct-Turbo', match=ClauseEquals(equals='meta-llama/Llama-3.3-70B-Instruct-Turbo'), prices=ModelPrice(input_mtok=Decimal('0.88'), output_mtok=Decimal('0.88')), ), ModelInfo( id='meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8', match=ClauseEquals(equals='meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'), prices=ModelPrice(input_mtok=Decimal('0.27'), output_mtok=Decimal('0.85')), ), ModelInfo( id='meta-llama/Llama-4-Scout-17B-16E-Instruct', match=ClauseEquals(equals='meta-llama/Llama-4-Scout-17B-16E-Instruct'), prices=ModelPrice(input_mtok=Decimal('0.18'), output_mtok=Decimal('0.59')), ), ModelInfo( id='meta-llama/Meta-Llama-3-70B-Instruct-Lite', match=ClauseEquals(equals='meta-llama/Meta-Llama-3-70B-Instruct-Lite'), prices=ModelPrice(input_mtok=Decimal('0.54'), output_mtok=Decimal('0.54')), ), ModelInfo( id='meta-llama/Meta-Llama-3-70B-Instruct-Turbo', match=ClauseEquals(equals='meta-llama/Meta-Llama-3-70B-Instruct-Turbo'), prices=ModelPrice(input_mtok=Decimal('0.88'), output_mtok=Decimal('0.88')), ), ModelInfo( id='meta-llama/Meta-Llama-3-8B-Instruct-Lite', match=ClauseEquals(equals='meta-llama/Meta-Llama-3-8B-Instruct-Lite'), prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ), ModelInfo( id='meta-llama/Meta-Llama-3-8B-Instruct-Turbo', match=ClauseEquals(equals='meta-llama/Meta-Llama-3-8B-Instruct-Turbo'), prices=ModelPrice(input_mtok=Decimal('0.18'), output_mtok=Decimal('0.18')), ), ModelInfo( id='meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo', match=ClauseEquals(equals='meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo'), prices=ModelPrice(input_mtok=Decimal('3.5'), output_mtok=Decimal('3.5')), ), ModelInfo( id='meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo', match=ClauseEquals(equals='meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo'), prices=ModelPrice(input_mtok=Decimal('0.88'), output_mtok=Decimal('0.88')), ), ModelInfo( id='meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo', match=ClauseEquals(equals='meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo'), prices=ModelPrice(input_mtok=Decimal('0.18'), output_mtok=Decimal('0.18')), ), ModelInfo( id='meta-llama/Meta-Llama-3.3-70B-Instruct-Turbo', match=ClauseEquals(equals='meta-llama/Meta-Llama-3.3-70B-Instruct-Turbo'), prices=ModelPrice(input_mtok=Decimal('0.88'), output_mtok=Decimal('0.88')), ), ModelInfo( id='microsoft/WizardLM-2-8x22B', match=ClauseEquals(equals='microsoft/WizardLM-2-8x22B'), prices=ModelPrice(input_mtok=Decimal('1.2'), output_mtok=Decimal('1.2')), ), ModelInfo( id='microsoft/phi-2', match=ClauseEquals(equals='microsoft/phi-2'), prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ), ModelInfo( id='mistralai/Mistral-7B-Instruct-v0.1', match=ClauseEquals(equals='mistralai/Mistral-7B-Instruct-v0.1'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='mistralai/Mistral-7B-Instruct-v0.2', match=ClauseEquals(equals='mistralai/Mistral-7B-Instruct-v0.2'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='mistralai/Mistral-7B-v0.1', match=ClauseEquals(equals='mistralai/Mistral-7B-v0.1'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='mistralai/Mixtral-8x22B-Instruct-v0.1', match=ClauseEquals(equals='mistralai/Mixtral-8x22B-Instruct-v0.1'), prices=ModelPrice(input_mtok=Decimal('2.4'), output_mtok=Decimal('2.4')), ), ModelInfo( id='mistralai/Mixtral-8x7B-Instruct-v0.1', match=ClauseEquals(equals='mistralai/Mixtral-8x7B-Instruct-v0.1'), prices=ModelPrice(input_mtok=Decimal('0.9'), output_mtok=Decimal('0.9')), ), ModelInfo( id='mistralai/Mixtral-8x7B-v0.1', match=ClauseEquals(equals='mistralai/Mixtral-8x7B-v0.1'), prices=ModelPrice(input_mtok=Decimal('0.9'), output_mtok=Decimal('0.9')), ), ModelInfo( id='openchat/openchat-3.5-1210', match=ClauseEquals(equals='openchat/openchat-3.5-1210'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='snorkelai/Snorkel-Mistral-PairRM-DPO', match=ClauseEquals(equals='snorkelai/Snorkel-Mistral-PairRM-DPO'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='teknium/OpenHermes-2-Mistral-7B', match=ClauseEquals(equals='teknium/OpenHermes-2-Mistral-7B'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='teknium/OpenHermes-2p5-Mistral-7B', match=ClauseEquals(equals='teknium/OpenHermes-2p5-Mistral-7B'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='togethercomputer/GPT-JT-Moderation-6B', match=ClauseEquals(equals='togethercomputer/GPT-JT-Moderation-6B'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='togethercomputer/Llama-2-7B-32K-Instruct', match=ClauseEquals(equals='togethercomputer/Llama-2-7B-32K-Instruct'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='togethercomputer/RedPajama-INCITE-7B-Base', match=ClauseEquals(equals='togethercomputer/RedPajama-INCITE-7B-Base'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='togethercomputer/RedPajama-INCITE-7B-Chat', match=ClauseEquals(equals='togethercomputer/RedPajama-INCITE-7B-Chat'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='togethercomputer/RedPajama-INCITE-7B-Instruct', match=ClauseEquals(equals='togethercomputer/RedPajama-INCITE-7B-Instruct'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='togethercomputer/RedPajama-INCITE-Base-3B-v1', match=ClauseEquals(equals='togethercomputer/RedPajama-INCITE-Base-3B-v1'), prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ), ModelInfo( id='togethercomputer/RedPajama-INCITE-Chat-3B-v1', match=ClauseEquals(equals='togethercomputer/RedPajama-INCITE-Chat-3B-v1'), prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ), ModelInfo( id='togethercomputer/RedPajama-INCITE-Instruct-3B-v1', match=ClauseEquals(equals='togethercomputer/RedPajama-INCITE-Instruct-3B-v1'), prices=ModelPrice(input_mtok=Decimal('0.1'), output_mtok=Decimal('0.1')), ), ModelInfo( id='togethercomputer/StripedHyena-Hessian-7B', match=ClauseEquals(equals='togethercomputer/StripedHyena-Hessian-7B'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='togethercomputer/StripedHyena-Nous-7B', match=ClauseEquals(equals='togethercomputer/StripedHyena-Nous-7B'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='togethercomputer/alpaca-7b', match=ClauseEquals(equals='togethercomputer/alpaca-7b'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ModelInfo( id='upstage/SOLAR-10.7B-Instruct-v1.0', match=ClauseEquals(equals='upstage/SOLAR-10.7B-Instruct-v1.0'), prices=ModelPrice(input_mtok=Decimal('0.3'), output_mtok=Decimal('0.3')), ), ModelInfo( id='zero-one-ai/Yi-34B', match=ClauseEquals(equals='zero-one-ai/Yi-34B'), prices=ModelPrice(input_mtok=Decimal('0.8'), output_mtok=Decimal('0.8')), ), ModelInfo( id='zero-one-ai/Yi-6B', match=ClauseEquals(equals='zero-one-ai/Yi-6B'), prices=ModelPrice(input_mtok=Decimal('0.2'), output_mtok=Decimal('0.2')), ), ], ), Provider( id='typesafe', name='TypeSafe', api_pattern='https://api\\.typesafe\\.ai', pricing_urls=['https://docs.typesafe.ai/models'], price_comments='TypeSafe bills Jev per input token only; output tokens are free. `jev-latest` and `jev-preview` are aliases that move with releases and currently point at `jev-1.13.0`; a versioned id is billed the same.', model_match=ClauseStartsWith(starts_with='jev-'), provider_match=ClauseContains(contains='typesafe'), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=True), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ) ], models=[ ModelInfo( id='jev-1.13.0', match=ClauseOr( or_=[ ClauseEquals(equals='jev-1.13.0'), ClauseEquals(equals='jev-latest'), ClauseEquals(equals='jev-preview'), ] ), name='Jev 1.13.0', prices=ModelPrice(input_mtok=Decimal('0.042')), ) ], ), Provider( id='voyageai', name='Voyage AI', api_pattern='https://api\\.voyageai\\.com', pricing_urls=['https://docs.voyageai.com/docs/pricing'], price_comments='Voyage AI bills per input token only; embedding models produce vectors rather than completion tokens, so there is no output price. The Batch API offers a 33% discount. This file covers text embedding models only; rerankers and multimodal embedding models are not included.', model_match=ClauseStartsWith(starts_with='voyage-'), provider_match=ClauseContains(contains='voyage'), models=[ ModelInfo( id='voyage-01', match=ClauseEquals(equals='voyage-01'), name='Voyage 01', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.1')), ), ModelInfo( id='voyage-02', match=ClauseEquals(equals='voyage-02'), name='Voyage 02', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.1')), ), ModelInfo( id='voyage-2', match=ClauseEquals(equals='voyage-2'), name='Voyage 2', prices=ModelPrice(input_mtok=Decimal('0.1')), ), ModelInfo( id='voyage-3', match=ClauseEquals(equals='voyage-3'), name='Voyage 3', description='General-purpose text embedding model optimized for retrieval quality and cost.', prices=ModelPrice(input_mtok=Decimal('0.06')), ), ModelInfo( id='voyage-3-large', match=ClauseEquals(equals='voyage-3-large'), name='Voyage 3 Large', prices=ModelPrice(input_mtok=Decimal('0.18')), ), ModelInfo( id='voyage-3-lite', match=ClauseEquals(equals='voyage-3-lite'), name='Voyage 3 Lite', prices=ModelPrice(input_mtok=Decimal('0.02')), ), ModelInfo( id='voyage-3.5', match=ClauseEquals(equals='voyage-3.5'), name='Voyage 3.5', description='General-purpose text embedding model optimized for retrieval quality and cost.', prices=ModelPrice(input_mtok=Decimal('0.06')), ), ModelInfo( id='voyage-3.5-lite', match=ClauseEquals(equals='voyage-3.5-lite'), name='Voyage 3.5 Lite', description='Latency- and cost-optimized variant of voyage-3.5.', prices=ModelPrice(input_mtok=Decimal('0.02')), ), ModelInfo( id='voyage-4', match=ClauseEquals(equals='voyage-4'), name='Voyage 4', description='General-purpose text embedding model balancing retrieval quality and cost.', prices=ModelPrice(input_mtok=Decimal('0.06')), ), ModelInfo( id='voyage-4-large', match=ClauseEquals(equals='voyage-4-large'), name='Voyage 4 Large', description='Highest-quality general-purpose text embedding model in the Voyage 4 family.', prices=ModelPrice(input_mtok=Decimal('0.12')), ), ModelInfo( id='voyage-4-lite', match=ClauseEquals(equals='voyage-4-lite'), name='Voyage 4 Lite', description='Latency- and cost-optimized text embedding model in the Voyage 4 family.', prices=ModelPrice(input_mtok=Decimal('0.02')), ), ModelInfo( id='voyage-code-2', match=ClauseEquals(equals='voyage-code-2'), name='Voyage Code 2', description='Embedding model optimized for code retrieval.', prices=ModelPrice(input_mtok=Decimal('0.12')), ), ModelInfo( id='voyage-code-3', match=ClauseEquals(equals='voyage-code-3'), name='Voyage Code 3', description='Embedding model optimized for code retrieval.', prices=ModelPrice(input_mtok=Decimal('0.18')), ), ModelInfo( id='voyage-context-3', match=ClauseEquals(equals='voyage-context-3'), name='Voyage Context 3', description='Contextualized chunk embedding model that encodes chunks together with full-document context.', prices=ModelPrice(input_mtok=Decimal('0.18')), ), ModelInfo( id='voyage-finance-2', match=ClauseEquals(equals='voyage-finance-2'), name='Voyage Finance 2', description='Embedding model optimized for finance-domain retrieval.', prices=ModelPrice(input_mtok=Decimal('0.12')), ), ModelInfo( id='voyage-large-2', match=ClauseEquals(equals='voyage-large-2'), name='Voyage Large 2', prices=ModelPrice(input_mtok=Decimal('0.12')), ), ModelInfo( id='voyage-large-2-instruct', match=ClauseEquals(equals='voyage-large-2-instruct'), name='Voyage Large 2 Instruct', prices=ModelPrice(input_mtok=Decimal('0.12')), ), ModelInfo( id='voyage-law-2', match=ClauseEquals(equals='voyage-law-2'), name='Voyage Law 2', description='Embedding model optimized for legal-domain retrieval.', prices=ModelPrice(input_mtok=Decimal('0.12')), ), ModelInfo( id='voyage-lite-01', match=ClauseEquals(equals='voyage-lite-01'), name='Voyage Lite 01', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.1')), ), ModelInfo( id='voyage-lite-01-instruct', match=ClauseEquals(equals='voyage-lite-01-instruct'), name='Voyage Lite 01 Instruct', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.1')), ), ModelInfo( id='voyage-lite-02-instruct', match=ClauseEquals(equals='voyage-lite-02-instruct'), name='Voyage Lite 02 Instruct', deprecated=True, prices=ModelPrice(input_mtok=Decimal('0.1')), ), ModelInfo( id='voyage-multilingual-2', match=ClauseEquals(equals='voyage-multilingual-2'), name='Voyage Multilingual 2', prices=ModelPrice(input_mtok=Decimal('0.12')), ), ], ), Provider( id='x-ai', name='X AI', api_pattern='https://api\\.x\\.ai', pricing_urls=['https://docs.x.ai/docs/models'], model_match=ClauseContains(contains='grok'), provider_match=ClauseEquals(equals='xai'), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping(path='cached_prompt_text_tokens', dest='cache_read_tokens', required=False), UsageExtractorMapping(path='reasoning_tokens', dest='output_tokens', required=False), UsageExtractorMapping(path='reasoning_tokens', dest='output_reasoning_tokens', required=False), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='default', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ), UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='input_tokens', dest='input_tokens', required=False), UsageExtractorMapping( path=['input_token_details', 'text_tokens'], dest='input_text_tokens', required=False ), UsageExtractorMapping( path=['input_token_details', 'audio_tokens'], dest='input_audio_tokens', required=False ), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=False), UsageExtractorMapping( path=['output_token_details', 'text_tokens'], dest='output_text_tokens', required=False ), UsageExtractorMapping( path=['output_token_details', 'audio_tokens'], dest='output_audio_tokens', required=False ), UsageExtractorMapping(path='billable_audio_seconds', dest='audio_seconds', required=True), UsageExtractorMapping(path='input_text_messages', dest='input_text_messages', required=False), ], api_flavor='realtime', model_path='model', ), ], models=[ ModelInfo( id='grok-2-1212', match=ClauseOr( or_=[ ClauseEquals(equals='grok-2-1212'), ClauseEquals(equals='grok-2'), ClauseEquals(equals='grok-2-latest'), ] ), name='Grok 2 1212', description='(deprecated) Grok 2 1212 introduces significant enhancements to accuracy, instruction adherence, and multilingual support, making it a powerful and flexible choice for developers seeking a highly steerable, intelligent model.', context_window=32768, deprecated=True, prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('10')), ), ModelInfo( id='grok-2-vision-1212', match=ClauseOr( or_=[ ClauseEquals(equals='grok-2-vision-1212'), ClauseEquals(equals='grok-2-vision'), ClauseEquals(equals='grok-2-vision-latest'), ] ), name='Grok 2 Vision 1212', description='Our multimodal model that processes documents, diagrams, charts, screenshots, and photographs.', context_window=32768, prices=ModelPrice(input_mtok=Decimal('2'), output_mtok=Decimal('10')), ), ModelInfo( id='grok-3', match=ClauseOr( or_=[ ClauseEquals(equals='grok-3'), ClauseEquals(equals='grok-3-latest'), ClauseEquals(equals='grok-3-beta'), ] ), name='Grok 3', description='Flagship model that excels at enterprise use cases like data extraction, coding, and text summarization. Possesses deep domain knowledge in finance, healthcare, law, and science.', context_window=131072, prices=ModelPrice(input_mtok=Decimal('3'), cache_read_mtok=Decimal('0.75'), output_mtok=Decimal('15')), ), ModelInfo( id='grok-3-fast', match=ClauseOr( or_=[ ClauseEquals(equals='grok-3-fast'), ClauseEquals(equals='grok-3-fast-latest'), ClauseEquals(equals='grok-3-fast-beta'), ] ), name='Grok 3 Fast', description='Excels at enterprise use cases like data extraction, coding, and text summarization. Possesses deep domain knowledge in finance, healthcare, law, and science.', context_window=131072, prices=ModelPrice(input_mtok=Decimal('5'), cache_read_mtok=Decimal('1.25'), output_mtok=Decimal('25')), ), ModelInfo( id='grok-3-mini', match=ClauseOr( or_=[ ClauseEquals(equals='grok-3-mini'), ClauseEquals(equals='grok-3-mini-beta'), ClauseEquals(equals='grok-3-mini-latest'), ] ), name='Grok 3 Mini', description='A lightweight model that thinks before responding. Fast, smart, and great for logic-based tasks that do not require deep domain knowledge. The raw thinking traces are accessible.', context_window=131072, prices=ModelPrice( input_mtok=Decimal('0.3'), cache_read_mtok=Decimal('0.075'), output_mtok=Decimal('0.5') ), ), ModelInfo( id='grok-3-mini-fast', match=ClauseOr( or_=[ ClauseEquals(equals='grok-3-mini-fast'), ClauseEquals(equals='grok-3-mini-fast-beta'), ClauseEquals(equals='grok-3-mini-fast-latest'), ] ), name='Grok 3 Mini Fast', description='A lightweight model that thinks before responding. Fast, smart, and great for logic-based tasks that do not require deep domain knowledge. The raw thinking traces are accessible.', context_window=131072, prices=ModelPrice(input_mtok=Decimal('0.6'), cache_read_mtok=Decimal('0.15'), output_mtok=Decimal('4')), ), ModelInfo( id='grok-4-0709', match=ClauseOr( or_=[ ClauseEquals(equals='grok-4-0709'), ClauseEquals(equals='grok-4'), ClauseEquals(equals='grok-4-latest'), ] ), name='Grok 4', description='A flagship model, offering unparalleled performance in natural language, math and reasoning - the perfect jack of all trades.', context_window=256000, prices=ModelPrice(input_mtok=Decimal('3'), cache_read_mtok=Decimal('0.75'), output_mtok=Decimal('15')), ), ModelInfo( id='grok-4-1-fast-non-reasoning', match=ClauseOr( or_=[ ClauseEquals(equals='grok-4-1-fast-non-reasoning'), ClauseEquals(equals='grok-4-1-fast-non-reasoning-latest'), ] ), name='Grok 4.1 Fast Non-Reasoning', description='A frontier multimodal model optimized specifically for high-performance agentic tool calling.', context_window=2000000, prices=ModelPrice( input_mtok=Decimal('0.2'), cache_read_mtok=Decimal('0.05'), output_mtok=Decimal('0.5') ), ), ModelInfo( id='grok-4-1-fast-reasoning', match=ClauseOr( or_=[ ClauseEquals(equals='grok-4-1-fast'), ClauseEquals(equals='grok-4-1-fast-reasoning'), ClauseEquals(equals='grok-4-1-fast-reasoning-latest'), ] ), name='Grok 4.1 Fast Reasoning', description='A frontier multimodal model optimized specifically for high-performance agentic tool calling.', context_window=2000000, prices=ModelPrice( input_mtok=Decimal('0.2'), cache_read_mtok=Decimal('0.05'), output_mtok=Decimal('0.5') ), ), ModelInfo( id='grok-4-fast-non-reasoning', match=ClauseOr( or_=[ ClauseEquals(equals='grok-4-fast-non-reasoning'), ClauseEquals(equals='grok-4-fast-non-reasoning-latest'), ] ), name='Grok 4 Fast Non-Reasoning', description='A frontier multimodal model optimized specifically for high-performance agentic tool calling.', context_window=2000000, prices=ModelPrice( input_mtok=Decimal('0.2'), cache_read_mtok=Decimal('0.05'), output_mtok=Decimal('0.5') ), ), ModelInfo( id='grok-4-fast-reasoning', match=ClauseOr( or_=[ ClauseEquals(equals='grok-4-fast'), ClauseEquals(equals='grok-4-fast-reasoning'), ClauseEquals(equals='grok-4-fast-reasoning-latest'), ] ), name='Grok 4 Fast Reasoning', description='A frontier multimodal model optimized specifically for high-performance agentic tool calling.', context_window=2000000, prices=ModelPrice( input_mtok=Decimal('0.2'), cache_read_mtok=Decimal('0.05'), output_mtok=Decimal('0.5') ), ), ModelInfo( id='grok-4.20', match=ClauseEquals(equals='grok-4.20'), name='Grok 4.20', description='Grok 4.20 is a reasoning model from xAI with industry-leading speed and agentic tool calling capabilities. It combines low hallucination rates with strict prompt adherence.', context_window=1000000, price_comments='Prompts at or above 200k tokens are billed at the higher rate for every token in the request, not just the tokens past the threshold. xAI\'s boundary is inclusive -- its table columns read "< 200k prompt tokens" and ">= 200k prompt tokens" -- while a tier here fires on `tokens > start`, so the start is 199999 and a 200000-token prompt lands on the higher rate. Ref: https://docs.x.ai/docs/models', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('1.25'), tiers=[Tier(start=199999, price=Decimal('2.5'))]), cache_read_mtok=TieredPrices(base=Decimal('0.2'), tiers=[Tier(start=199999, price=Decimal('0.4'))]), output_mtok=TieredPrices(base=Decimal('2.5'), tiers=[Tier(start=199999, price=Decimal('5'))]), ), ), ModelInfo( id='grok-4.20-multi-agent', match=ClauseEquals(equals='grok-4.20-multi-agent'), name='Grok 4.20 Multi-Agent', description="Grok 4.20 Multi-Agent is a variant of xAI's Grok 4.20 designed for collaborative, agent-based workflows. Multiple agents operate in parallel to conduct deep research, coordinate tool use, and synthesize information.", context_window=1000000, prices=ModelPrice(input_mtok=Decimal('2'), cache_read_mtok=Decimal('0.2'), output_mtok=Decimal('6')), ), ModelInfo( id='grok-4.3', match=ClauseOr( or_=[ ClauseEquals(equals='grok-4.3'), ClauseRegex(regex='^grok-4\\.3-\\d{8}$'), ClauseEquals(equals='x-ai/grok-4.3'), ClauseRegex(regex='^x-ai/grok-4\\.3-\\d{8}$'), ClauseEquals(equals='grok-4.3-latest'), ] ), name='Grok 4.3', description='Most advanced flagship model, leading the industry in non-hallucination rate, agentic tool calling, and instruction following capabilities. Supports text and image inputs with text outputs, function calling, structured outputs, and reasoning.', context_window=1000000, price_comments='Prompts at or above 200k tokens are billed at the higher rate for every token in the request, not just the tokens past the threshold. xAI\'s boundary is inclusive -- its table columns read "< 200k prompt tokens" and ">= 200k prompt tokens" -- while a tier here fires on `tokens > start`, so the start is 199999 and a 200000-token prompt lands on the higher rate. Ref: https://docs.x.ai/docs/models', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('1.25'), tiers=[Tier(start=199999, price=Decimal('2.5'))]), cache_read_mtok=TieredPrices(base=Decimal('0.2'), tiers=[Tier(start=199999, price=Decimal('0.4'))]), output_mtok=TieredPrices(base=Decimal('2.5'), tiers=[Tier(start=199999, price=Decimal('5'))]), ), ), ModelInfo( id='grok-4.5', match=ClauseOr( or_=[ ClauseEquals(equals='grok-4.5'), ClauseRegex(regex='^grok-4\\.5-\\d{8}$'), ClauseEquals(equals='x-ai/grok-4.5'), ClauseRegex(regex='^x-ai/grok-4\\.5-\\d{8}$'), ClauseEquals(equals='grok-4.5-latest'), ClauseEquals(equals='grok-latest'), ] ), name='Grok 4.5', description="xAI's most intelligent and fastest flagship model, well-suited for general-purpose use including coding and chat. Supports text and image inputs with text outputs, function calling, structured outputs, and reasoning.", context_window=500000, price_comments='Prompts at or above 200k tokens are billed at the higher rate for every token in the request, not just the tokens past the threshold. xAI\'s boundary is inclusive -- its table columns read "< 200k prompt tokens" and ">= 200k prompt tokens" -- while a tier here fires on `tokens > start`, so the start is 199999 and a 200000-token prompt lands on the higher rate. The base cache-read price is 0.30, not 0.50 - 0.50 is Grok 4.6\'s. Ref: https://docs.x.ai/docs/models/grok-4.5', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('2'), tiers=[Tier(start=199999, price=Decimal('4'))]), cache_read_mtok=TieredPrices(base=Decimal('0.3'), tiers=[Tier(start=199999, price=Decimal('0.6'))]), output_mtok=TieredPrices(base=Decimal('6'), tiers=[Tier(start=199999, price=Decimal('12'))]), ), ), ModelInfo( id='grok-4.6', match=ClauseOr( or_=[ ClauseEquals(equals='grok-4.6'), ClauseRegex(regex='^grok-4\\.6-\\d{8}$'), ClauseEquals(equals='x-ai/grok-4.6'), ClauseRegex(regex='^x-ai/grok-4\\.6-\\d{8}$'), ClauseEquals(equals='grok-4.6-latest'), ] ), name='Grok 4.6', description="xAI's latest flagship model, released 2026-08-12. Supports text and image inputs with text outputs, function calling, structured outputs, and reasoning, over a 500k-token context window.", context_window=500000, price_comments='Prompts at or above 200k tokens are billed at the higher rate for every token in the request, not just the tokens past the threshold. xAI\'s boundary is inclusive -- its table columns read "< 200k prompt tokens" and ">= 200k prompt tokens" -- while a tier here fires on `tokens > start`, so the start is 199999 and a 200000-token prompt lands on the higher rate. Ref: https://docs.x.ai/docs/models/grok-4.6', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('2'), tiers=[Tier(start=199999, price=Decimal('4'))]), cache_read_mtok=TieredPrices(base=Decimal('0.5'), tiers=[Tier(start=199999, price=Decimal('1'))]), output_mtok=TieredPrices(base=Decimal('6'), tiers=[Tier(start=199999, price=Decimal('12'))]), ), ), ModelInfo( id='grok-build-0.1', match=ClauseEquals(equals='grok-build-0.1'), name='Grok Build 0.1', description="Grok Build 0.1 is xAI's fast coding model trained specifically for agentic software engineering workflows. It supports text and image inputs with text output, and is optimized for interactive coding.", context_window=256000, price_comments='Prompts at or above 200k tokens are billed at the higher rate for every token in the request, not just the tokens past the threshold. xAI\'s boundary is inclusive -- its table columns read "< 200k prompt tokens" and ">= 200k prompt tokens" -- while a tier here fires on `tokens > start`, so the start is 199999 and a 200000-token prompt lands on the higher rate. Ref: https://docs.x.ai/docs/models', prices=ModelPrice( input_mtok=TieredPrices(base=Decimal('1'), tiers=[Tier(start=199999, price=Decimal('2'))]), cache_read_mtok=TieredPrices(base=Decimal('0.2'), tiers=[Tier(start=199999, price=Decimal('0.4'))]), output_mtok=TieredPrices(base=Decimal('2'), tiers=[Tier(start=199999, price=Decimal('4'))]), ), ), ModelInfo( id='grok-code-fast-1', match=ClauseOr( or_=[ ClauseEquals(equals='grok-code-fast'), ClauseEquals(equals='grok-code-fast-1'), ClauseEquals(equals='grok-code-fast-1-0825'), ] ), name='Grok Code Fast 1', description='A speedy and economical reasoning model that excels at agentic coding.', context_window=256000, prices=ModelPrice( input_mtok=Decimal('0.2'), cache_read_mtok=Decimal('0.02'), output_mtok=Decimal('1.5') ), ), ModelInfo( id='grok-voice-latest', match=ClauseEquals(equals='grok-voice-latest'), name='Grok Voice', price_comments='See https://docs.x.ai/developers/release-notes and https://docs.x.ai/developers/pricing#voice-pricing.', prices=[ ConditionalPrice( prices=ModelPrice(audio_hours=Decimal('3'), input_text_messages_kcount=Decimal('4')) ), ConditionalPrice( constraint=StartDateConstraint(start_date=datetime.date(2026, 8, 5)), prices=ModelPrice(audio_hours=Decimal('4.8'), input_text_messages_kcount=Decimal('4')), ), ], ), ModelInfo( id='grok-voice-think-fast-1.0', match=ClauseEquals(equals='grok-voice-think-fast-1.0'), name='Grok Voice Think Fast 1.0', price_comments='See https://docs.x.ai/developers/pricing#voice-pricing.', deprecated=True, prices=ModelPrice(audio_hours=Decimal('3'), input_text_messages_kcount=Decimal('4')), ), ModelInfo( id='grok-voice-think-fast-2.0', match=ClauseEquals(equals='grok-voice-think-fast-2.0'), name='Grok Voice Think Fast 2.0', price_comments='See https://docs.x.ai/developers/pricing#voice-pricing.', prices=ModelPrice(audio_hours=Decimal('4.8'), input_text_messages_kcount=Decimal('4')), ), ], ), Provider( id='zai', name='Z.AI', api_pattern='https://api\\.z\\.ai', pricing_urls=['https://docs.z.ai/guides/overview/pricing'], price_comments='USD prices from the Z.AI pricing page. The API pattern covers both the standard and Coding Plan endpoints, with Coding Plan usage valued at the published API rates.', extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ) ], models=[ ModelInfo( id='GLM-5.2', match=ClauseOr(or_=[ClauseEquals(equals='GLM-5.2'), ClauseEquals(equals='glm-5.2')]), name='GLM-5.2', description='Z.AI flagship model with a 1,000,000 token context window, context caching, structured output, and function calling.', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('1.4'), cache_read_mtok=Decimal('0.26'), output_mtok=Decimal('4.4') ), ), ModelInfo( id='GLM-5.3', match=ClauseOr(or_=[ClauseEquals(equals='GLM-5.3'), ClauseEquals(equals='glm-5.3')]), name='GLM-5.3', description='Z.AI flagship model with a 1,000,000 token context window, context caching, structured output, and function calling. Reasoning is always enabled, with low, high and max effort levels.', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('1.4'), cache_read_mtok=Decimal('0.26'), output_mtok=Decimal('4.4') ), ), ModelInfo( id='GLM-5.3-Flash', match=ClauseOr(or_=[ClauseEquals(equals='GLM-5.3-Flash'), ClauseEquals(equals='glm-5.3-flash')]), name='GLM-5.3-Flash', description='Z.AI native multimodal model with a 1,000,000 token context window, context caching, function calling, and always-on reasoning.', context_window=1000000, price_comments='Standard API rates reflect the 50% launch promotion ending at 24:00 on September 9, 2026 (UTC+8): list prices are $0.15 input, $0.03 cached input, and $0.50 output per million tokens.', prices=ModelPrice( input_mtok=Decimal('0.075'), cache_read_mtok=Decimal('0.015'), output_mtok=Decimal('0.25') ), ), ], ), Provider( id='zhipuai', name='Zhipu AI', api_pattern='https://open\\.bigmodel\\.cn', pricing_urls=['https://open.bigmodel.cn/pricing', 'https://docs.bigmodel.cn/cn/guide/start/model-overview'], price_comments='Prices sourced from Zhipu AI open platform pricing (CNY, open.bigmodel.cn/pricing), converted to USD at 1 USD = 7.25 CNY (May/June 2026). Zhipu AI does not publish USD prices; CNY is the only billing currency. Flagship models (GLM-4.5-Air, GLM-4.7, GLM-5 series) have tiered pricing by input/output length; prices shown are for the cheapest tier ([0, 32k) input / [0, 0.2k) output where applicable). GLM-4 standard inference models (GLM-4-Air, GLM-4-Plus, etc.) bill input and output tokens at the same per-token rate per their pricing page. Cache write is temporarily free for flagship models (limited-time promotion, not included).', model_match=ClauseOr(or_=[ClauseStartsWith(starts_with='GLM-'), ClauseStartsWith(starts_with='glm-')]), extractors=[ UsageExtractor( root='usage', mappings=[ UsageExtractorMapping(path='prompt_tokens', dest='input_tokens', required=True), UsageExtractorMapping( path=['prompt_tokens_details', 'cached_tokens'], dest='cache_read_tokens', required=False ), UsageExtractorMapping( path=['completion_tokens_details', 'reasoning_tokens'], dest='output_reasoning_tokens', required=False, ), UsageExtractorMapping(path='completion_tokens', dest='output_tokens', required=True), ], api_flavor='chat', model_path='model', ) ], models=[ ModelInfo( id='GLM-4-Air', match=ClauseOr(or_=[ClauseEquals(equals='GLM-4-Air'), ClauseEquals(equals='glm-4-air')]), name='GLM-4-Air', description='High-performance GLM-4 model with context caching. 128,000 token context window. Input and output billed at the same per-token rate.', context_window=128000, prices=ModelPrice( input_mtok=Decimal('0.069'), cache_read_mtok=Decimal('0.034'), output_mtok=Decimal('0.069') ), ), ModelInfo( id='GLM-4-AirX', match=ClauseOr(or_=[ClauseEquals(equals='GLM-4-AirX'), ClauseEquals(equals='glm-4-airx')]), name='GLM-4-AirX', description='Fastest GLM-4 model. 8,000 token context window. Does not support context caching. Input and output billed at the same per-token rate.', context_window=8000, prices=ModelPrice(input_mtok=Decimal('1.379'), output_mtok=Decimal('1.379')), ), ModelInfo( id='GLM-4-Assistant', match=ClauseOr(or_=[ClauseEquals(equals='GLM-4-Assistant'), ClauseEquals(equals='glm-4-assistant')]), name='GLM-4-Assistant', description='GLM-4 agent/assistant model. 128,000 token context window. Does not support context caching. Input and output billed at the same per-token rate.', context_window=128000, prices=ModelPrice(input_mtok=Decimal('0.69'), output_mtok=Decimal('0.69')), ), ModelInfo( id='GLM-4-FlashX-250414', match=ClauseOr( or_=[ClauseEquals(equals='GLM-4-FlashX-250414'), ClauseEquals(equals='glm-4-flashx-250414')] ), name='GLM-4-FlashX-250414', description='Fast and cheap GLM-4 model with context caching. 128,000 token context window. Input and output billed at the same per-token rate.', context_window=128000, prices=ModelPrice( input_mtok=Decimal('0.014'), cache_read_mtok=Decimal('0.007'), output_mtok=Decimal('0.014') ), ), ModelInfo( id='GLM-4-Long', match=ClauseOr(or_=[ClauseEquals(equals='GLM-4-Long'), ClauseEquals(equals='glm-4-long')]), name='GLM-4-Long', description='GLM-4 model optimized for long inputs with context caching. 1,000,000 token context window. Input and output billed at the same per-token rate.', context_window=1000000, prices=ModelPrice( input_mtok=Decimal('0.138'), cache_read_mtok=Decimal('0.069'), output_mtok=Decimal('0.138') ), ), ModelInfo( id='GLM-4-Plus', match=ClauseOr(or_=[ClauseEquals(equals='GLM-4-Plus'), ClauseEquals(equals='glm-4-plus')]), name='GLM-4-Plus', description='Flagship GLM-4 model with context caching. 128,000 token context window. Input and output billed at the same per-token rate.', context_window=128000, prices=ModelPrice( input_mtok=Decimal('0.69'), cache_read_mtok=Decimal('0.345'), output_mtok=Decimal('0.69') ), ), ModelInfo( id='GLM-4.5-Air', match=ClauseOr(or_=[ClauseEquals(equals='GLM-4.5-Air'), ClauseEquals(equals='glm-4.5-air')]), name='GLM-4.5-Air', description="Zhipu AI's GLM-4.5-Air flagship model with context caching. 128,000 token context window. Tiered pricing; prices shown for [0, 32k) input / [0, 0.2k) output tier.", context_window=128000, prices=ModelPrice( input_mtok=Decimal('0.11'), cache_read_mtok=Decimal('0.022'), output_mtok=Decimal('0.276') ), ), ModelInfo( id='GLM-4.7', match=ClauseOr(or_=[ClauseEquals(equals='GLM-4.7'), ClauseEquals(equals='glm-4.7')]), name='GLM-4.7', description="Zhipu AI's GLM-4.7 flagship model with context caching. 200,000 token context window. Tiered pricing; prices shown for [0, 32k) input / [0, 0.2k) output tier.", context_window=200000, prices=ModelPrice( input_mtok=Decimal('0.276'), cache_read_mtok=Decimal('0.055'), output_mtok=Decimal('1.103') ), ), ModelInfo( id='GLM-4.7-FlashX', match=ClauseOr(or_=[ClauseEquals(equals='GLM-4.7-FlashX'), ClauseEquals(equals='glm-4.7-flashx')]), name='GLM-4.7-FlashX', description='Fast and affordable GLM-4.7 model with context caching. 200,000 token context window.', context_window=200000, prices=ModelPrice( input_mtok=Decimal('0.069'), cache_read_mtok=Decimal('0.014'), output_mtok=Decimal('0.414') ), ), ModelInfo( id='GLM-5', match=ClauseOr(or_=[ClauseEquals(equals='GLM-5'), ClauseEquals(equals='glm-5')]), name='GLM-5', description='Zhipu AI GLM-5 model with context caching. 200,000 token context window. Tiered pricing; prices shown for [0, 32k) input tier.', context_window=200000, prices=ModelPrice( input_mtok=Decimal('0.552'), cache_read_mtok=Decimal('0.138'), output_mtok=Decimal('2.483') ), ), ModelInfo( id='GLM-5-Turbo', match=ClauseOr(or_=[ClauseEquals(equals='GLM-5-Turbo'), ClauseEquals(equals='glm-5-turbo')]), name='GLM-5-Turbo', description='Zhipu AI GLM-5 Turbo model with context caching. 200,000 token context window. Tiered pricing; prices shown for [0, 32k) input tier.', context_window=200000, prices=ModelPrice( input_mtok=Decimal('0.69'), cache_read_mtok=Decimal('0.166'), output_mtok=Decimal('3.034') ), ), ModelInfo( id='GLM-5.1', match=ClauseOr( or_=[ ClauseEquals(equals='GLM-5.1'), ClauseEquals(equals='glm-5.1'), ClauseEquals(equals='GLM-5.1-20260406'), ClauseEquals(equals='glm-5.1-20260406'), ] ), name='GLM-5.1', description='Zhipu AI flagship model supporting long-horizon tasks, structured output, function calling, and context caching. 200,000 token context window. Tiered pricing; prices shown for [0, 32k) input tier.', context_window=200000, prices=ModelPrice( input_mtok=Decimal('0.828'), cache_read_mtok=Decimal('0.179'), output_mtok=Decimal('3.31') ), ), ModelInfo( id='GLM-5.2', match=ClauseOr(or_=[ClauseEquals(equals='GLM-5.2'), ClauseEquals(equals='glm-5.2')]), name='GLM-5.2', description="Zhipu AI's latest flagship model supporting 1,000,000 token context, long-horizon coding tasks, structured output, function calling, and context caching.", context_window=1000000, prices=ModelPrice( input_mtok=Decimal('1.103'), cache_read_mtok=Decimal('0.276'), output_mtok=Decimal('3.862') ), ), ModelInfo( id='GLM-5.3', match=ClauseOr(or_=[ClauseEquals(equals='GLM-5.3'), ClauseEquals(equals='glm-5.3')]), name='GLM-5.3', description='Zhipu AI flagship model supporting 1,000,000 token context, long-horizon coding tasks, structured output, function calling, and context caching.', context_window=1000000, price_comments='Standard API rates are 8 CNY input, 2 CNY cached input, and 28 CNY output per million tokens, converted to USD at 1 USD = 7.25 CNY.', prices=ModelPrice( input_mtok=Decimal('1.103'), cache_read_mtok=Decimal('0.276'), output_mtok=Decimal('3.862') ), ), ModelInfo( id='GLM-5.3-Flash', match=ClauseOr(or_=[ClauseEquals(equals='GLM-5.3-Flash'), ClauseEquals(equals='glm-5.3-flash')]), name='GLM-5.3-Flash', description="Zhipu AI's native multimodal GLM-5.3 model with a 1,000,000 token context window, function calling, always-on reasoning, and context caching.", context_window=1000000, price_comments='The standard API launch promotion is 0.4 CNY input, 0.115 CNY cached input, and 1.4 CNY output per million tokens, converted to USD at 1 USD = 7.25 CNY. The pricing page marks this as a two-week 50% discount; list prices are 0.8 CNY input, 0.23 CNY cached input, and 2.8 CNY output per million tokens.', prices=ModelPrice( input_mtok=Decimal('0.055'), cache_read_mtok=Decimal('0.016'), output_mtok=Decimal('0.193') ), ), ], ), ]