diff --git a/packages/js/src/__tests__/calcPrice.test.ts b/packages/js/src/__tests__/calcPrice.test.ts index d383bc02..c2f7faa2 100644 --- a/packages/js/src/__tests__/calcPrice.test.ts +++ b/packages/js/src/__tests__/calcPrice.test.ts @@ -123,6 +123,90 @@ describe('Core Price Calculation Function', () => { }) }) + it('should handle web search requests as total cost', () => { + const usage: Usage = { + input_tokens: 1000, + output_tokens: 500, + tool_use: { web_search: 3 }, + } + const modelPrice: ModelPrice = { + input_mtok: 3.0, + output_mtok: 15.0, + tool_use_kcount: { web_search: 10 }, // $10 per 1000 web searches + } + + const result = calcPrice(usage, modelPrice) + + expect(result).toMatchObject({ + input_price: 0.003, // 1000 * 3.0 / 1_000_000 + output_price: 0.0075, // 500 * 15.0 / 1_000_000 + total_price: 0.003 + 0.0075 + (10 * 3) / 1000, // add web search cost to total only + }) + }) + + it('should handle file search requests as total cost', () => { + const usage: Usage = { + input_tokens: 1000, + output_tokens: 500, + tool_use: { file_search: 4 }, + } + const modelPrice: ModelPrice = { + input_mtok: 2.5, + output_mtok: 10.0, + tool_use_kcount: { file_search: 2.5 }, // $2.50 per 1000 file searches + } + + const result = calcPrice(usage, modelPrice) + + expect(result).toMatchObject({ + input_price: 0.0025, // 1000 * 2.5 / 1_000_000 + output_price: 0.005, // 500 * 10.0 / 1_000_000 + total_price: 0.0025 + 0.005 + (2.5 * 4) / 1000, // add file search cost to total only + }) + }) + + it('should not add file search cost when requests is zero', () => { + const usage: Usage = { + input_tokens: 1000, + output_tokens: 500, + tool_use: { file_search: 0 }, + } + const modelPrice: ModelPrice = { + input_mtok: 2.5, + output_mtok: 10.0, + tool_use_kcount: { file_search: 2.5 }, + } + + const result = calcPrice(usage, modelPrice) + + expect(result).toMatchObject({ + input_price: 0.0025, + output_price: 0.005, + total_price: 0.0025 + 0.005, + }) + }) + + it('should not add web search cost when requests is zero', () => { + const usage: Usage = { + input_tokens: 1000, + output_tokens: 500, + tool_use: { web_search: 0 }, + } + const modelPrice: ModelPrice = { + input_mtok: 3.0, + output_mtok: 15.0, + tool_use_kcount: { web_search: 10 }, + } + + const result = calcPrice(usage, modelPrice) + + expect(result).toMatchObject({ + input_price: 0.003, + output_price: 0.0075, + total_price: 0.003 + 0.0075, + }) + }) + it.each([ { expected: { input_price: 0, output_price: 0, total_price: 0 }, diff --git a/packages/js/src/__tests__/dataset.test.ts b/packages/js/src/__tests__/dataset.test.ts index 90fe0b37..1e81bb98 100644 --- a/packages/js/src/__tests__/dataset.test.ts +++ b/packages/js/src/__tests__/dataset.test.ts @@ -97,7 +97,7 @@ describe('dataset', () => { } for (const key of Object.keys(extracted.usage)) { const k = key as keyof Usage - expect(extractedUsage[k]).toBe(extracted.usage[k]) + expect(extractedUsage[k]).toStrictEqual(extracted.usage[k]) } } } diff --git a/packages/js/src/data.ts b/packages/js/src/data.ts index ca5ac846..dee4f8d2 100644 --- a/packages/js/src/data.ts +++ b/packages/js/src/data.ts @@ -48,6 +48,11 @@ export const data: Provider[] = [ dest: 'output_tokens', required: true, }, + { + path: ['server_tool_use', 'web_search_requests'], + dest: 'web_search', + required: false, + }, ], }, { @@ -115,6 +120,9 @@ export const data: Provider[] = [ cache_write_mtok: 1, cache_read_mtok: 0.08, output_mtok: 4, + tool_use_kcount: { + web_search: 10, + }, }, }, { @@ -138,6 +146,9 @@ export const data: Provider[] = [ cache_write_mtok: 3.75, cache_read_mtok: 0.3, output_mtok: 15, + tool_use_kcount: { + web_search: 10, + }, }, }, { @@ -167,6 +178,9 @@ export const data: Provider[] = [ cache_write_mtok: 3.75, cache_read_mtok: 0.3, output_mtok: 15, + tool_use_kcount: { + web_search: 10, + }, }, }, { @@ -182,6 +196,9 @@ export const data: Provider[] = [ cache_write_mtok: 0.3, cache_read_mtok: 0.03, output_mtok: 1.25, + tool_use_kcount: { + web_search: 10, + }, }, }, { @@ -198,6 +215,9 @@ export const data: Provider[] = [ cache_write_mtok: 18.75, cache_read_mtok: 1.5, output_mtok: 75, + tool_use_kcount: { + web_search: 10, + }, }, }, { @@ -214,6 +234,9 @@ export const data: Provider[] = [ cache_write_mtok: 3.75, cache_read_mtok: 0.3, output_mtok: 15, + tool_use_kcount: { + web_search: 10, + }, }, }, { @@ -242,6 +265,9 @@ export const data: Provider[] = [ cache_write_mtok: 1.25, cache_read_mtok: 0.1, output_mtok: 5, + tool_use_kcount: { + web_search: 10, + }, }, }, { @@ -270,6 +296,9 @@ export const data: Provider[] = [ cache_write_mtok: 18.75, cache_read_mtok: 1.5, output_mtok: 75, + tool_use_kcount: { + web_search: 10, + }, }, }, { @@ -292,6 +321,9 @@ export const data: Provider[] = [ cache_write_mtok: 18.75, cache_read_mtok: 1.5, output_mtok: 75, + tool_use_kcount: { + web_search: 10, + }, }, }, { @@ -320,6 +352,9 @@ export const data: Provider[] = [ cache_write_mtok: 6.25, cache_read_mtok: 0.5, output_mtok: 25, + tool_use_kcount: { + web_search: 10, + }, }, }, { @@ -380,6 +415,9 @@ export const data: Provider[] = [ }, ], }, + tool_use_kcount: { + web_search: 10, + }, }, }, { @@ -411,6 +449,9 @@ export const data: Provider[] = [ cache_write_mtok: 3.75, cache_read_mtok: 0.3, output_mtok: 15, + tool_use_kcount: { + web_search: 10, + }, }, }, { @@ -465,6 +506,9 @@ export const data: Provider[] = [ }, ], }, + tool_use_kcount: { + web_search: 10, + }, }, }, { @@ -519,6 +563,9 @@ export const data: Provider[] = [ }, ], }, + tool_use_kcount: { + web_search: 10, + }, }, }, { @@ -8935,6 +8982,10 @@ export const data: Provider[] = [ input_mtok: 2, cache_read_mtok: 0.5, output_mtok: 8, + tool_use_kcount: { + web_search: 30, + file_search: 2.5, + }, }, }, { @@ -8957,6 +9008,10 @@ export const data: Provider[] = [ input_mtok: 0.4, cache_read_mtok: 0.1, output_mtok: 1.6, + tool_use_kcount: { + web_search: 30, + file_search: 2.5, + }, }, }, { @@ -8979,6 +9034,10 @@ export const data: Provider[] = [ input_mtok: 0.1, cache_read_mtok: 0.025, output_mtok: 0.4, + tool_use_kcount: { + web_search: 30, + file_search: 2.5, + }, }, }, { @@ -9021,6 +9080,10 @@ export const data: Provider[] = [ input_mtok: 2.5, cache_read_mtok: 1.25, output_mtok: 10, + tool_use_kcount: { + web_search: 25, + file_search: 2.5, + }, }, }, { @@ -9062,6 +9125,10 @@ export const data: Provider[] = [ input_mtok: 0.15, cache_read_mtok: 0.075, output_mtok: 0.6, + tool_use_kcount: { + web_search: 25, + file_search: 2.5, + }, }, }, { @@ -9215,6 +9282,10 @@ export const data: Provider[] = [ input_mtok: 1.25, cache_read_mtok: 0.125, output_mtok: 10, + tool_use_kcount: { + web_search: 30, + file_search: 2.5, + }, }, }, { @@ -9260,6 +9331,10 @@ export const data: Provider[] = [ input_mtok: 0.25, cache_read_mtok: 0.025, output_mtok: 2, + tool_use_kcount: { + web_search: 30, + file_search: 2.5, + }, }, }, { @@ -9281,6 +9356,10 @@ export const data: Provider[] = [ input_mtok: 0.05, cache_read_mtok: 0.005, output_mtok: 0.4, + tool_use_kcount: { + web_search: 30, + file_search: 2.5, + }, }, }, { @@ -9350,6 +9429,10 @@ export const data: Provider[] = [ input_mtok: 1.25, cache_read_mtok: 0.125, output_mtok: 10, + tool_use_kcount: { + web_search: 30, + file_search: 2.5, + }, }, }, { @@ -9376,6 +9459,10 @@ export const data: Provider[] = [ input_mtok: 0.25, cache_read_mtok: 0.025, output_mtok: 2, + tool_use_kcount: { + web_search: 30, + file_search: 2.5, + }, }, }, { @@ -9421,6 +9508,10 @@ export const data: Provider[] = [ input_mtok: 1.75, cache_read_mtok: 0.175, output_mtok: 14, + tool_use_kcount: { + web_search: 30, + file_search: 2.5, + }, }, }, { diff --git a/packages/js/src/engine.ts b/packages/js/src/engine.ts index dd564f9b..ca1e7142 100644 --- a/packages/js/src/engine.ts +++ b/packages/js/src/engine.ts @@ -94,6 +94,14 @@ export function calcPrice(usage: Usage, modelPrice: ModelPrice): ModelPriceCalcu if (modelPrice.requests_kcount !== undefined) { totalPrice += modelPrice.requests_kcount / 1000 } + if (modelPrice.tool_use_kcount && usage.tool_use) { + for (const [unit, price] of Object.entries(modelPrice.tool_use_kcount)) { + const count = usage.tool_use[unit] ?? 0 + if (count) { + totalPrice += (price * count) / 1000 + } + } + } return { input_price: inputPrice, diff --git a/packages/js/src/extractUsage.ts b/packages/js/src/extractUsage.ts index cfde93df..827cd731 100644 --- a/packages/js/src/extractUsage.ts +++ b/packages/js/src/extractUsage.ts @@ -30,11 +30,27 @@ export function extractUsage(provider: Provider, responseData: unknown, apiFlavo const usage: Usage = {} + const USAGE_FIELDS = new Set([ + 'cache_audio_read_tokens', + 'cache_read_tokens', + 'cache_write_tokens', + 'input_audio_tokens', + 'input_tokens', + 'output_audio_tokens', + 'output_tokens', + ]) + for (const mapping of extractor.mappings) { const value = extractPath(mapping.path, usageObj, numberCheck, mapping.required, root) if (value !== null) { - const currentValue = usage[mapping.dest] ?? 0 - usage[mapping.dest] = currentValue + value + if (USAGE_FIELDS.has(mapping.dest)) { + const dest = mapping.dest as keyof Omit + const currentValue = usage[dest] ?? 0 + usage[dest] = currentValue + value + } else { + usage.tool_use ??= {} + usage.tool_use[mapping.dest] = (usage.tool_use[mapping.dest] ?? 0) + value + } } } diff --git a/packages/js/src/types.ts b/packages/js/src/types.ts index 012c8930..4794ee89 100644 --- a/packages/js/src/types.ts +++ b/packages/js/src/types.ts @@ -6,6 +6,7 @@ export interface Usage { input_tokens?: number output_audio_tokens?: number output_tokens?: number + tool_use?: Record } export interface Tier { @@ -33,6 +34,7 @@ export interface ModelPrice { output_audio_mtok?: number | TieredPrices output_mtok?: number | TieredPrices requests_kcount?: number + tool_use_kcount?: Record } export interface ConditionalPrice { @@ -69,14 +71,7 @@ export interface ArrayMatch { export type ExtractPath = (ArrayMatch | string)[] | string export interface UsageExtractorMapping { - dest: - | 'cache_audio_read_tokens' - | 'cache_read_tokens' - | 'cache_write_tokens' - | 'input_audio_tokens' - | 'input_tokens' - | 'output_audio_tokens' - | 'output_tokens' + dest: string path: ExtractPath required: boolean } diff --git a/packages/python/genai_prices/data.py b/packages/python/genai_prices/data.py index 32a0c84c..7add9e18 100644 --- a/packages/python/genai_prices/data.py +++ b/packages/python/genai_prices/data.py @@ -27,6 +27,9 @@ ), UsageExtractorMapping(path='cache_read_input_tokens', dest='cache_read_tokens', required=False), UsageExtractorMapping(path='output_tokens', dest='output_tokens', required=True), + UsageExtractorMapping( + path=['server_tool_use', 'web_search_requests'], dest='web_search', required=False + ), ], api_flavor='default', model_path='model', @@ -67,6 +70,7 @@ cache_write_mtok=Decimal('1'), cache_read_mtok=Decimal('0.08'), output_mtok=Decimal('4'), + tool_use_kcount={'web_search': Decimal('10')}, ), ), ModelInfo( @@ -85,6 +89,7 @@ cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), + tool_use_kcount={'web_search': Decimal('10')}, ), ), ModelInfo( @@ -105,6 +110,7 @@ cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), + tool_use_kcount={'web_search': Decimal('10')}, ), ), ModelInfo( @@ -118,6 +124,7 @@ cache_write_mtok=Decimal('0.3'), cache_read_mtok=Decimal('0.03'), output_mtok=Decimal('1.25'), + tool_use_kcount={'web_search': Decimal('10')}, ), ), ModelInfo( @@ -131,6 +138,7 @@ cache_write_mtok=Decimal('18.75'), cache_read_mtok=Decimal('1.5'), output_mtok=Decimal('75'), + tool_use_kcount={'web_search': Decimal('10')}, ), ), ModelInfo( @@ -144,6 +152,7 @@ cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), + tool_use_kcount={'web_search': Decimal('10')}, ), ), ModelInfo( @@ -164,6 +173,7 @@ cache_write_mtok=Decimal('1.25'), cache_read_mtok=Decimal('0.1'), output_mtok=Decimal('5'), + tool_use_kcount={'web_search': Decimal('10')}, ), ), ModelInfo( @@ -184,6 +194,7 @@ cache_write_mtok=Decimal('18.75'), cache_read_mtok=Decimal('1.5'), output_mtok=Decimal('75'), + tool_use_kcount={'web_search': Decimal('10')}, ), ), ModelInfo( @@ -202,6 +213,7 @@ cache_write_mtok=Decimal('18.75'), cache_read_mtok=Decimal('1.5'), output_mtok=Decimal('75'), + tool_use_kcount={'web_search': Decimal('10')}, ), ), ModelInfo( @@ -222,6 +234,7 @@ cache_write_mtok=Decimal('6.25'), cache_read_mtok=Decimal('0.5'), output_mtok=Decimal('25'), + tool_use_kcount={'web_search': Decimal('10')}, ), ), ModelInfo( @@ -244,6 +257,7 @@ ), 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'))]), + tool_use_kcount={'web_search': Decimal('10')}, ), ), ModelInfo( @@ -265,6 +279,7 @@ cache_write_mtok=Decimal('3.75'), cache_read_mtok=Decimal('0.3'), output_mtok=Decimal('15'), + tool_use_kcount={'web_search': Decimal('10')}, ), ), ModelInfo( @@ -285,6 +300,7 @@ ), 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'))]), + tool_use_kcount={'web_search': Decimal('10')}, ), ), ModelInfo( @@ -305,6 +321,7 @@ ), 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'))]), + tool_use_kcount={'web_search': Decimal('10')}, ), ), ModelInfo( @@ -5076,7 +5093,12 @@ 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')), + prices=ModelPrice( + input_mtok=Decimal('2'), + cache_read_mtok=Decimal('0.5'), + output_mtok=Decimal('8'), + tool_use_kcount={'web_search': Decimal('30'), 'file_search': Decimal('2.5')}, + ), ), ModelInfo( id='gpt-4.1-mini', @@ -5087,7 +5109,10 @@ 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') + input_mtok=Decimal('0.4'), + cache_read_mtok=Decimal('0.1'), + output_mtok=Decimal('1.6'), + tool_use_kcount={'web_search': Decimal('30'), 'file_search': Decimal('2.5')}, ), ), ModelInfo( @@ -5099,7 +5124,10 @@ 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') + input_mtok=Decimal('0.1'), + cache_read_mtok=Decimal('0.025'), + output_mtok=Decimal('0.4'), + tool_use_kcount={'web_search': Decimal('30'), 'file_search': Decimal('2.5')}, ), ), ModelInfo( @@ -5125,7 +5153,10 @@ 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') + input_mtok=Decimal('2.5'), + cache_read_mtok=Decimal('1.25'), + output_mtok=Decimal('10'), + tool_use_kcount={'web_search': Decimal('25'), 'file_search': Decimal('2.5')}, ), ), ModelInfo( @@ -5150,7 +5181,10 @@ 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') + input_mtok=Decimal('0.15'), + cache_read_mtok=Decimal('0.075'), + output_mtok=Decimal('0.6'), + tool_use_kcount={'web_search': Decimal('25'), 'file_search': Decimal('2.5')}, ), ), ModelInfo( @@ -5241,7 +5275,10 @@ 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') + input_mtok=Decimal('1.25'), + cache_read_mtok=Decimal('0.125'), + output_mtok=Decimal('10'), + tool_use_kcount={'web_search': Decimal('30'), 'file_search': Decimal('2.5')}, ), ), ModelInfo( @@ -5263,7 +5300,10 @@ 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') + input_mtok=Decimal('0.25'), + cache_read_mtok=Decimal('0.025'), + output_mtok=Decimal('2'), + tool_use_kcount={'web_search': Decimal('30'), 'file_search': Decimal('2.5')}, ), ), ModelInfo( @@ -5273,7 +5313,10 @@ 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') + input_mtok=Decimal('0.05'), + cache_read_mtok=Decimal('0.005'), + output_mtok=Decimal('0.4'), + tool_use_kcount={'web_search': Decimal('30'), 'file_search': Decimal('2.5')}, ), ), ModelInfo( @@ -5304,7 +5347,10 @@ 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') + input_mtok=Decimal('1.25'), + cache_read_mtok=Decimal('0.125'), + output_mtok=Decimal('10'), + tool_use_kcount={'web_search': Decimal('30'), 'file_search': Decimal('2.5')}, ), ), ModelInfo( @@ -5320,7 +5366,10 @@ 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') + input_mtok=Decimal('0.25'), + cache_read_mtok=Decimal('0.025'), + output_mtok=Decimal('2'), + tool_use_kcount={'web_search': Decimal('30'), 'file_search': Decimal('2.5')}, ), ), ModelInfo( @@ -5343,7 +5392,10 @@ 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') + input_mtok=Decimal('1.75'), + cache_read_mtok=Decimal('0.175'), + output_mtok=Decimal('14'), + tool_use_kcount={'web_search': Decimal('30'), 'file_search': Decimal('2.5')}, ), ), ModelInfo( diff --git a/packages/python/genai_prices/types.py b/packages/python/genai_prices/types.py index 432a0cfd..06958f8e 100644 --- a/packages/python/genai_prices/types.py +++ b/packages/python/genai_prices/types.py @@ -248,6 +248,9 @@ class Usage: output_audio_tokens: int | None = None """Number of output audio tokens.""" + tool_use: dict[str, int] | None = None + """Counts of tool use requests, keyed by tool use unit (e.g. 'web_search', 'file_search').""" + def __add__(self, other: Usage | Any) -> Usage: if not isinstance(other, Usage): return NotImplemented @@ -255,12 +258,23 @@ def __add__(self, other: Usage | Any) -> Usage: def _add_option(a: int | None, b: int | None) -> int | None: return None if a is b is None else (a or 0) + (b or 0) - return Usage( - **{ - field.name: _add_option(getattr(self, field.name), getattr(other, field.name)) - for field in dataclasses.fields(self) - } - ) + def _add_dicts(a: dict[str, int] | None, b: dict[str, int] | None) -> dict[str, int] | None: + if a is None and b is None: + return None + result = dict(a or {}) + for k, v in (b or {}).items(): + result[k] = result.get(k, 0) + v + return result + + kwargs: dict[str, Any] = {} + for field in dataclasses.fields(self): + a = getattr(self, field.name) + b = getattr(other, field.name) + if field.name == 'tool_use': + kwargs[field.name] = _add_dicts(a, b) + else: + kwargs[field.name] = _add_option(a, b) + return Usage(**kwargs) def __radd__(self, other: Usage) -> Usage: return self + other @@ -349,6 +363,8 @@ def summary(self) -> str: 'output_audio_tokens', ] +USAGE_FIELDS: frozenset[str] = frozenset(UsageField.__args__) + @dataclass class UsageExtractorMapping: @@ -356,7 +372,7 @@ class UsageExtractorMapping: path: ExtractPath """Path to the value to extract""" - dest: UsageField + dest: str """Destination field to store the extracted value. If multiple mappings point to the same destination, the values are summed. @@ -403,8 +419,13 @@ def extract(self, response_data: Any) -> tuple[str | None, Usage]: for mapping in self.mappings: value = _extract_path(mapping.path, usage_obj, int, mapping.required, root) if value is not None: - current_value = getattr(usage, mapping.dest) or 0 - setattr(usage, mapping.dest, current_value + value) + if mapping.dest not in USAGE_FIELDS: + if usage.tool_use is None: + usage.tool_use = {} + usage.tool_use[mapping.dest] = (usage.tool_use.get(mapping.dest, 0)) + value + else: + current_value = getattr(usage, mapping.dest) or 0 + setattr(usage, mapping.dest, current_value + value) values_set = True if not values_set: raise ValueError(f'No usage information found at {self.root}') @@ -608,6 +629,9 @@ class ModelPrice: requests_kcount: Decimal | None = None """price in USD per thousand requests""" + tool_use_kcount: dict[str, Decimal] | None = None + """price in USD per thousand tool use requests, keyed by tool use unit""" + def calc_price(self, usage: AbstractUsage) -> CalcPrice: """Calculate the price of usage in USD with this model price.""" input_price = Decimal(0) @@ -656,6 +680,13 @@ def calc_price(self, usage: AbstractUsage) -> CalcPrice: if self.requests_kcount is not None: total_price += self.requests_kcount / 1000 + if self.tool_use_kcount: + tool_use: dict[str, int] = getattr(usage, 'tool_use', None) or {} + for unit, price in self.tool_use_kcount.items(): + count: int = tool_use.get(unit, 0) + if count: + total_price += price * count / 1000 + return {'input_price': input_price, 'output_price': output_price, 'total_price': total_price} def __str__(self) -> str: @@ -665,6 +696,10 @@ def __str__(self) -> str: if value is not None: if field.name == 'requests_kcount': parts.append(f'${value} / K requests') + elif field.name == 'tool_use_kcount': + for unit, price in value.items(): + label = unit.replace('_', ' ') + parts.append(f'${price} / K {label}') else: name = field.name.replace('_mtok', '').replace('_', ' ') if isinstance(value, TieredPrices): diff --git a/prices/data.json b/prices/data.json index d7730a57..959f3bb1 100644 --- a/prices/data.json +++ b/prices/data.json @@ -1 +1 @@ -[{"id":"anthropic","name":"Anthropic","pricing_urls":["https://www.anthropic.com/pricing#api"],"api_pattern":"https://api\\.anthropic\\.com","model_match":{"contains":"claude"},"provider_match":{"contains":"anthropic"},"extractors":[{"api_flavor":"default","root":"usage","model_path":"model","mappings":[{"path":"input_tokens","dest":"input_tokens","required":true},{"path":"cache_creation_input_tokens","dest":"input_tokens","required":false},{"path":"cache_read_input_tokens","dest":"input_tokens","required":false},{"path":"cache_creation_input_tokens","dest":"cache_write_tokens","required":false},{"path":"cache_read_input_tokens","dest":"cache_read_tokens","required":false},{"path":"output_tokens","dest":"output_tokens","required":true}]},{"api_flavor":"chat","root":"usage","model_path":"model","mappings":[{"path":"prompt_tokens","dest":"input_tokens","required":true},{"path":"cached_tokens","dest":"cache_read_tokens","required":false},{"path":"completion_tokens","dest":"output_tokens","required":true}]}],"models":[{"id":"claude-2","name":"Claude 2.0 / 2.1","description":"Claude 2 is Anthropic's previous generation model, offering reliable performance for various tasks. 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It's 671B parameters in size, with 37B active in an inference pass.","match":{"or":[{"equals":"deepseek-reasoner"},{"starts_with":"deepseek-r1"},{"equals":"deepseek-r1-0528"}]},"context_window":64000,"prices":[{"prices":{"input_mtok":0.135,"cache_read_mtok":0.035,"output_mtok":0.55}},{"constraint":{"start_time":"00:30:00Z","end_time":"16:30:00Z"},"prices":{"input_mtok":0.55,"cache_read_mtok":0.14,"output_mtok":2.19}}]}]},{"id":"fireworks","name":"Fireworks","pricing_urls":["https://fireworks.ai/pricing"],"api_pattern":"https://api\\.fireworks\\.ai","model_match":{"starts_with":"accounts/fireworks/models/"},"extractors":[{"api_flavor":"chat","root":"usage","model_path":"model","mappings":[{"path":"prompt_tokens","dest":"input_tokens","required":true},{"path":["prompt_tokens_details","cached_tokens"],"dest":"cache_read_tokens","required":false},{"path":["completion_tokens_details","audio_tokens"],"dest":"output_audio_tokens","required":false},{"path":"completion_tokens","dest":"output_tokens","required":true}]}],"models":[{"id":"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. 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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.","match":{"or":[{"equals":"gemini-2.5-flash-lite"},{"starts_with":"gemini-2.5-flash-lite-preview"}]},"context_window":1000000,"prices":{"input_mtok":0.1,"cache_read_mtok":0.01,"output_mtok":0.4,"input_audio_mtok":0.3,"cache_audio_read_mtok":0.03}},{"id":"gemini-2.5-flash-preview","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. 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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.","prices":{"input_mtok":2,"output_mtok":120}},{"id":"gemini-3-pro-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.","match":{"or":[{"starts_with":"gemini-3-pro-preview"},{"equals":"gemini-3-pro-text-preview"}]},"prices":{"input_mtok":{"base":2,"tiers":[{"start":200000,"price":4}]},"cache_read_mtok":{"base":0.2,"tiers":[{"start":200000,"price":0.4}]},"output_mtok":{"base":12,"tiers":[{"start":200000,"price":18}]}}},{"id":"gemini-embedding-001","match":{"equals":"gemini-embedding-001"},"prices":{"input_mtok":0.15}},{"id":"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. 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index 2339dea9..84697915 100644 --- a/prices/data.schema.json +++ b/prices/data.schema.json @@ -405,6 +405,14 @@ "type": "number", "description": "price in USD per thousand requests", "title": "Requests Kcount" + }, + "tool_use_kcount": { + "additionalProperties": { + "type": "number" + }, + "type": "object", + "description": "price in USD per thousand tool use requests, keyed by tool use unit", + "title": "Tool Use Kcount" } }, "title": "ModelPrice", @@ -730,15 +738,6 @@ }, "dest": { "description": "Destination field to store the extracted value.\n\nIf multiple mappings point to the same destination, the values are summed.", - "enum": [ - "input_tokens", - "cache_write_tokens", - "cache_read_tokens", - "output_tokens", - "input_audio_tokens", - "cache_audio_read_tokens", - "output_audio_tokens" - ], "title": "Dest", "type": "string" }, diff --git a/prices/data_slim.json b/prices/data_slim.json index 376c87c9..d56b9915 100644 --- a/prices/data_slim.json +++ b/prices/data_slim.json @@ 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AI","pricing_urls":["https://docs.x.ai/docs/models"],"api_pattern":"https://api\\.x\\.ai","model_match":{"contains":"grok"},"provider_match":{"equals":"xai"},"extractors":[{"api_flavor":"chat","root":"usage","model_path":"model","mappings":[{"path":"prompt_tokens","dest":"input_tokens","required":true},{"path":["prompt_tokens_details","cached_tokens"],"dest":"cache_read_tokens","required":false},{"path":["completion_tokens_details","audio_tokens"],"dest":"output_audio_tokens","required":false},{"path":"completion_tokens","dest":"output_tokens","required":true}]}],"models":[{"id":"grok-2-1212","match":{"or":[{"equals":"grok-2-1212"},{"equals":"grok-2"},{"equals":"grok-2-latest"}]},"context_window":32768,"prices":{"input_mtok":2,"output_mtok":10},"deprecated":true},{"id":"grok-2-vision-1212","match":{"or":[{"equals":"grok-2-vision-1212"},{"equals":"grok-2-vision"},{"equals":"grok-2-vision-latest"}]},"context_window":32768,"prices":{"input_mtok":2,"output_mtok":10}},{"id":"grok-3","match":{"or":[{"equals":"grok-3"},{"equals":"grok-3-latest"},{"equals":"grok-3-beta"}]},"context_window":131072,"prices":{"input_mtok":3,"cache_read_mtok":0.75,"output_mtok":15}},{"id":"grok-3-fast","match":{"or":[{"equals":"grok-3-fast"},{"equals":"grok-3-fast-latest"},{"equals":"grok-3-fast-beta"}]},"context_window":131072,"prices":{"input_mtok":5,"cache_read_mtok":1.25,"output_mtok":25}},{"id":"grok-3-mini","match":{"or":[{"equals":"grok-3-mini"},{"equals":"grok-3-mini-beta"},{"equals":"grok-3-mini-latest"}]},"context_window":131072,"prices":{"input_mtok":0.3,"cache_read_mtok":0.075,"output_mtok":0.5}},{"id":"grok-3-mini-fast","match":{"or":[{"equals":"grok-3-mini-fast"},{"equals":"grok-3-mini-fast-beta"},{"equals":"grok-3-mini-fast-latest"}]},"context_window":131072,"prices":{"input_mtok":0.6,"cache_read_mtok":0.15,"output_mtok":4}},{"id":"grok-4-0709","match":{"or":[{"equals":"grok-4-0709"},{"equals":"grok-4"},{"equals":"grok-4-latest"}]},"context_window":256000,"prices":{"input_mtok":3,"cache_read_mtok":0.75,"output_mtok":15}},{"id":"grok-4-1-fast-non-reasoning","match":{"or":[{"equals":"grok-4-1-fast-non-reasoning"},{"equals":"grok-4-1-fast-non-reasoning-latest"}]},"context_window":2000000,"prices":{"input_mtok":0.2,"cache_read_mtok":0.05,"output_mtok":0.5}},{"id":"grok-4-1-fast-reasoning","match":{"or":[{"equals":"grok-4-1-fast"},{"equals":"grok-4-1-fast-reasoning"},{"equals":"grok-4-1-fast-reasoning-latest"}]},"context_window":2000000,"prices":{"input_mtok":0.2,"cache_read_mtok":0.05,"output_mtok":0.5}},{"id":"grok-4-fast-non-reasoning","match":{"or":[{"equals":"grok-4-fast-non-reasoning"},{"equals":"grok-4-fast-non-reasoning-latest"}]},"context_window":2000000,"prices":{"input_mtok":0.2,"cache_read_mtok":0.05,"output_mtok":0.5}},{"id":"grok-4-fast-reasoning","match":{"or":[{"equals":"grok-4-fast"},{"equals":"grok-4-fast-reasoning"},{"equals":"grok-4-fast-reasoning-latest"}]},"context_window":2000000,"prices":{"input_mtok":0.2,"cache_read_mtok":0.05,"output_mtok":0.5}},{"id":"grok-code-fast-1","match":{"or":[{"equals":"grok-code-fast"},{"equals":"grok-code-fast-1"},{"equals":"grok-code-fast-1-0825"}]},"context_window":256000,"prices":{"input_mtok":0.2,"cache_read_mtok":0.02,"output_mtok":1.5}}]}] diff --git a/prices/data_slim.schema.json b/prices/data_slim.schema.json index 8af836d0..a1ed3165 100644 --- a/prices/data_slim.schema.json +++ b/prices/data_slim.schema.json @@ -387,6 +387,14 @@ "type": "number", "description": "price in USD per thousand requests", "title": "Requests Kcount" + }, + "tool_use_kcount": { + "additionalProperties": { + "type": "number" + }, + "type": "object", + "description": "price in USD per thousand tool use requests, keyed by tool use unit", + "title": "Tool Use Kcount" } }, "title": "ModelPrice", @@ -689,15 +697,6 @@ }, "dest": { "description": "Destination field to store the extracted value.\n\nIf multiple mappings point to the same destination, the values are summed.", - "enum": [ - "input_tokens", - "cache_write_tokens", - "cache_read_tokens", - "output_tokens", - "input_audio_tokens", - "cache_audio_read_tokens", - "output_audio_tokens" - ], "title": "Dest", "type": "string" }, diff --git a/prices/providers/.schema.json b/prices/providers/.schema.json index 2cd0d30c..c5df411d 100644 --- a/prices/providers/.schema.json +++ b/prices/providers/.schema.json @@ -429,6 +429,14 @@ "type": "number", "description": "price in USD per thousand requests", "title": "Requests Kcount" + }, + "tool_use_kcount": { + "additionalProperties": { + "type": "number" + }, + "type": "object", + "description": "price in USD per thousand tool use requests, keyed by tool use unit", + "title": "Tool Use Kcount" } }, "title": "ModelPrice", @@ -621,15 +629,6 @@ }, "dest": { "description": "Destination field to store the extracted value.\n\nIf multiple mappings point to the same destination, the values are summed.", - "enum": [ - "input_tokens", - "cache_write_tokens", - "cache_read_tokens", - "output_tokens", - "input_audio_tokens", - "cache_audio_read_tokens", - "output_audio_tokens" - ], "title": "Dest", "type": "string" }, diff --git a/prices/providers/anthropic.yml b/prices/providers/anthropic.yml index 058f9317..b6bb5ed4 100644 --- a/prices/providers/anthropic.yml +++ b/prices/providers/anthropic.yml @@ -30,6 +30,9 @@ extractors: required: false - path: output_tokens dest: output_tokens + - path: [server_tool_use, web_search_requests] + dest: web_search + required: false # https://platform.claude.com/docs/en/api/openai-sdk#response-fields - api_flavor: chat root: usage @@ -73,6 +76,8 @@ models: cache_write_mtok: 1 cache_read_mtok: 0.08 output_mtok: 4 + tool_use_kcount: + web_search: 10 - id: claude-3-5-sonnet name: Claude Sonnet 3.5 @@ -89,6 +94,8 @@ models: cache_write_mtok: 3.75 cache_read_mtok: 0.3 output_mtok: 15 + tool_use_kcount: + web_search: 10 - id: claude-3-7-sonnet-latest name: Claude Sonnet 3.7 @@ -107,6 +114,8 @@ models: cache_write_mtok: 3.75 cache_read_mtok: 0.3 output_mtok: 15 + tool_use_kcount: + web_search: 10 - id: claude-3-haiku name: Claude Haiku 3 @@ -120,6 +129,8 @@ models: cache_write_mtok: 0.3 cache_read_mtok: 0.03 output_mtok: 1.25 + tool_use_kcount: + web_search: 10 - id: claude-3-opus-latest name: Claude Opus 3 @@ -134,6 +145,8 @@ models: cache_write_mtok: 18.75 cache_read_mtok: 1.5 output_mtok: 75 + tool_use_kcount: + web_search: 10 - id: claude-3-sonnet name: Claude 3 Sonnet @@ -148,6 +161,8 @@ models: cache_write_mtok: 3.75 cache_read_mtok: 0.3 output_mtok: 15 + tool_use_kcount: + web_search: 10 - id: claude-haiku-4-5 name: Claude Haiku 4.5 @@ -165,6 +180,8 @@ models: cache_write_mtok: 1.25 cache_read_mtok: 0.10 output_mtok: 5 + tool_use_kcount: + web_search: 10 - id: claude-instant-1 description: Retired, here to match price sources @@ -200,6 +217,8 @@ models: cache_write_mtok: 18.75 cache_read_mtok: 1.5 output_mtok: 75 + tool_use_kcount: + web_search: 10 - id: claude-opus-4-1 name: Claude Opus 4.1 @@ -216,6 +235,8 @@ models: cache_write_mtok: 18.75 cache_read_mtok: 1.5 output_mtok: 75 + tool_use_kcount: + web_search: 10 - id: claude-opus-4-5 name: Claude Opus 4.5 @@ -233,6 +254,8 @@ models: cache_write_mtok: 6.25 cache_read_mtok: 0.50 output_mtok: 25 + tool_use_kcount: + web_search: 10 - id: claude-opus-4-6 name: Claude Opus 4.6 @@ -266,6 +289,8 @@ models: tiers: - start: 200000 price: 37.50 + tool_use_kcount: + web_search: 10 - id: claude-sonnet-4-0 name: Claude Sonnet 4 @@ -284,6 +309,8 @@ models: cache_write_mtok: 3.75 cache_read_mtok: 0.3 output_mtok: 15 + tool_use_kcount: + web_search: 10 - id: claude-sonnet-4-5 name: Claude Sonnet 4.5 @@ -315,6 +342,8 @@ models: tiers: - start: 200000 price: 22.50 + tool_use_kcount: + web_search: 10 - id: claude-sonnet-4-6 name: Claude Sonnet 4.6 @@ -346,6 +375,8 @@ models: tiers: - start: 200000 price: 22.50 + tool_use_kcount: + web_search: 10 - id: claude-v1 description: Retired, here to match price sources diff --git a/prices/providers/openai.yml b/prices/providers/openai.yml index 5d7b97ab..d91e4793 100644 --- a/prices/providers/openai.yml +++ b/prices/providers/openai.yml @@ -295,6 +295,9 @@ models: input_mtok: 2 cache_read_mtok: 0.5 output_mtok: 8 + tool_use_kcount: + web_search: 30 + file_search: 2.5 - id: gpt-4.1-mini name: gpt 4.1 mini @@ -312,6 +315,9 @@ models: input_mtok: 0.4 cache_read_mtok: 0.1 output_mtok: 1.6 + tool_use_kcount: + web_search: 30 + file_search: 2.5 - id: gpt-4.1-nano name: gpt 4.1 nano @@ -329,6 +335,9 @@ models: input_mtok: 0.1 cache_read_mtok: 0.025 output_mtok: 0.4 + tool_use_kcount: + web_search: 30 + file_search: 2.5 - id: gpt-4.5-preview name: GPT-4.5 (Preview) @@ -362,6 +371,9 @@ models: input_mtok: 2.5 cache_read_mtok: 1.25 output_mtok: 10 + tool_use_kcount: + web_search: 25 + file_search: 2.5 - id: gpt-4o-audio-preview name: gpt 4o audio preview @@ -393,6 +405,9 @@ models: input_mtok: 0.15 cache_read_mtok: 0.075 output_mtok: 0.6 + tool_use_kcount: + web_search: 25 + file_search: 2.5 - id: gpt-4o-mini-2024-07-18.ft- description: GPT-4o Mini fine tuned. @@ -502,6 +517,9 @@ models: input_mtok: 1.25 cache_read_mtok: 0.125 output_mtok: 10 + tool_use_kcount: + web_search: 30 + file_search: 2.5 - id: gpt-5-image match: @@ -535,6 +553,9 @@ models: input_mtok: 0.25 cache_read_mtok: 0.025 output_mtok: 2 + tool_use_kcount: + web_search: 30 + file_search: 2.5 - id: gpt-5-nano name: GPT-5 nano @@ -551,6 +572,9 @@ models: input_mtok: 0.05 cache_read_mtok: 0.005 output_mtok: 0.4 + tool_use_kcount: + web_search: 30 + file_search: 2.5 - id: gpt-5-pro match: @@ -588,6 +612,9 @@ models: input_mtok: 1.25 cache_read_mtok: 0.125 output_mtok: 10 + tool_use_kcount: + web_search: 30 + file_search: 2.5 - id: gpt-5.1-codex-mini name: GPT-5.1 Codex Mini @@ -605,6 +632,9 @@ models: input_mtok: 0.25 cache_read_mtok: 0.025 output_mtok: 2 + tool_use_kcount: + web_search: 30 + file_search: 2.5 - id: gpt-5.2 name: GPT-5.2 @@ -628,6 +658,9 @@ models: input_mtok: 1.75 cache_read_mtok: 0.175 output_mtok: 14 + tool_use_kcount: + web_search: 30 + file_search: 2.5 - id: gpt-5.2-pro description: Version of GPT-5.2 that produces smarter and more precise responses. diff --git a/prices/src/prices/prices_types.py b/prices/src/prices/prices_types.py index 2ac73aad..121b9bae 100644 --- a/prices/src/prices/prices_types.py +++ b/prices/src/prices/prices_types.py @@ -131,13 +131,15 @@ def exclude_free(self): 'output_audio_tokens', ] +USAGE_FIELDS: frozenset[str] = frozenset(UsageField.__args__) + class UsageExtractorMapping(_Model): """Mappings from used to build usage.""" path: ExtractPath """Path to the value to extract""" - dest: UsageField + dest: str """Destination field to store the extracted value. If multiple mappings point to the same destination, the values are summed. @@ -259,6 +261,9 @@ class ModelPrice(_Model): requests_kcount: DollarPrice | None = None """price in USD per thousand requests""" + tool_use_kcount: dict[str, DollarPrice] | None = None + """price in USD per thousand tool use requests, keyed by tool use unit""" + def is_free(self) -> bool: """Whether all values are zero or unset""" for field_name in self.__pydantic_fields__: diff --git a/prices/src/prices/source_openrouter.py b/prices/src/prices/source_openrouter.py index 2ca066cb..cec4db24 100644 --- a/prices/src/prices/source_openrouter.py +++ b/prices/src/prices/source_openrouter.py @@ -11,7 +11,7 @@ from . import source_prices from .prices_types import ClauseEquals, ModelInfo, ModelPrice from .update import get_providers_yaml -from .utils import mtok +from .utils import kcount, mtok map_providers = { 'mistralai': 'mistral', @@ -74,11 +74,15 @@ class OpenRouterPricing(BaseModel, extra='forbid'): input_cache_read: Decimal | None = None def model_price(self) -> ModelPrice: + tool_use_kcount: dict[str, Decimal] = {} + if ws := kcount(self.web_search): + tool_use_kcount['web_search'] = ws return ModelPrice( input_mtok=mtok(self.prompt), cache_write_mtok=mtok(self.input_cache_write), cache_read_mtok=mtok(self.input_cache_read), output_mtok=mtok(self.completion), + tool_use_kcount=tool_use_kcount or None, ) diff --git a/prices/src/prices/utils.py b/prices/src/prices/utils.py index 4a2673ff..311199e5 100644 --- a/prices/src/prices/utils.py +++ b/prices/src/prices/utils.py @@ -25,6 +25,14 @@ def mtok(v: Decimal | None) -> Decimal | None: return v * 1_000_000 +def kcount(v: Decimal | None) -> Decimal | None: + """Convert a per-unit price to per-thousand (kcount).""" + if v is None or v == 0: + return None + else: + return v * 1000 + + T = TypeVar('T') diff --git a/tests/dataset/extract_usages.py b/tests/dataset/extract_usages.py index 246e3f3a..947ce4b5 100644 --- a/tests/dataset/extract_usages.py +++ b/tests/dataset/extract_usages.py @@ -117,7 +117,7 @@ def case_to_result(case: Case, this_result: dict[str, Any]): except LookupError: pass else: - assert price.input_price + price.output_price == price.total_price + assert price.input_price + price.output_price <= price.total_price extractor_dict['input_price'] = str(price.input_price) extractor_dict['output_price'] = str(price.output_price) for other in this_result['extracted']: diff --git a/tests/dataset/usages.json b/tests/dataset/usages.json index 8681a235..dacb55e7 100644 --- a/tests/dataset/usages.json +++ b/tests/dataset/usages.json @@ -2256,7 +2256,18 @@ "input_price": "0.00489", "output_price": "0.001635", "provider_id": "anthropic" - }, + } + ], + "usage": { + "input_tokens": 1630, + "output_tokens": 109, + "tool_use": { + "web_search": 0 + } + } + }, + { + "extractors": [ { "api_flavor": "anthropic", "provider_id": "aws" @@ -2999,7 +3010,18 @@ "input_price": "0.032427", "output_price": "0.00966", "provider_id": "anthropic" - }, + } + ], + "usage": { + "input_tokens": 10809, + "output_tokens": 644, + "tool_use": { + "web_search": 1 + } + } + }, + { + "extractors": [ { "api_flavor": "anthropic", "provider_id": "aws" @@ -7682,7 +7704,18 @@ "input_price": "0.021786", "output_price": "0.002565", "provider_id": "anthropic" - }, + } + ], + "usage": { + "input_tokens": 7262, + "output_tokens": 171, + "tool_use": { + "web_search": 0 + } + } + }, + { + "extractors": [ { "api_flavor": "anthropic", "provider_id": "aws" @@ -8525,7 +8558,18 @@ "input_price": "0.008022", "output_price": "0.005595", "provider_id": "anthropic" - }, + } + ], + "usage": { + "input_tokens": 2674, + "output_tokens": 373, + "tool_use": { + "web_search": 0 + } + } + }, + { + "extractors": [ { "api_flavor": "anthropic", "provider_id": "aws" @@ -8588,7 +8632,18 @@ "input_price": "0.015786", "output_price": "0.005535", "provider_id": "anthropic" - }, + } + ], + "usage": { + "input_tokens": 5262, + "output_tokens": 369, + "tool_use": { + "web_search": 0 + } + } + }, + { + "extractors": [ { "api_flavor": "anthropic", "provider_id": "aws" @@ -8950,7 +9005,18 @@ "input_price": "0.026952", "output_price": "0.0078", "provider_id": "anthropic" - }, + } + ], + "usage": { + "input_tokens": 8984, + "output_tokens": 520, + "tool_use": { + "web_search": 1 + } + } + }, + { + "extractors": [ { "api_flavor": "anthropic", "provider_id": "aws" @@ -9014,7 +9080,18 @@ "input_price": "0.059577", "output_price": "0.00816", "provider_id": "anthropic" - }, + } + ], + "usage": { + "input_tokens": 19859, + "output_tokens": 544, + "tool_use": { + "web_search": 1 + } + } + }, + { + "extractors": [ { "api_flavor": "anthropic", "provider_id": "aws" @@ -9198,7 +9275,18 @@ "input_price": "0.048249", "output_price": "0.002475", "provider_id": "anthropic" - }, + } + ], + "usage": { + "input_tokens": 16083, + "output_tokens": 165, + "tool_use": { + "web_search": 1 + } + } + }, + { + "extractors": [ { "api_flavor": "anthropic", "provider_id": "aws" @@ -9428,7 +9516,18 @@ "input_price": "0.005049", "output_price": "0.001695", "provider_id": "anthropic" - }, + } + ], + "usage": { + "input_tokens": 1683, + "output_tokens": 113, + "tool_use": { + "web_search": 0 + } + } + }, + { + "extractors": [ { "api_flavor": "anthropic", "provider_id": "aws" @@ -9492,7 +9591,18 @@ "input_price": "0.005313", "output_price": "0.002565", "provider_id": "anthropic" - }, + } + ], + "usage": { + "input_tokens": 1771, + "output_tokens": 171, + "tool_use": { + "web_search": 0 + } + } + }, + { + "extractors": [ { "api_flavor": "anthropic", "provider_id": "aws" @@ -9556,7 +9666,18 @@ "input_price": "0.005223", "output_price": "0.002145", "provider_id": "anthropic" - }, + } + ], + "usage": { + "input_tokens": 1741, + "output_tokens": 143, + "tool_use": { + "web_search": 0 + } + } + }, + { + "extractors": [ { "api_flavor": "anthropic", "provider_id": "aws" diff --git a/tests/test_cli.py b/tests/test_cli.py index 0ebed70b..628fdfb0 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -20,7 +20,7 @@ def test_calc(capsys: pytest.CaptureFixture[str]): assert out == snapshot("""\ Provider: OpenAI Model: gpt 4o - Model Prices: $2.5/input MTok, $1.25/cache read MTok, $10/output MTok + Model Prices: $2.5/input MTok, $1.25/cache read MTok, $10/output MTok, $25 / K web search, $2.5 / K file search Context Window: 128,000 Input Price: $0.0025 Output Price: $0.001 diff --git a/tests/test_custom_prices.py b/tests/test_custom_prices.py index 8b6da9e8..3e0514f3 100644 --- a/tests/test_custom_prices.py +++ b/tests/test_custom_prices.py @@ -90,6 +90,8 @@ class CustomUsage: cache_audio_read_tokens: int | None = None output_audio_tokens: int | None = None + tool_use: dict[str, int] | None = None + def test_alt_source(): with AltUpdatePrices() as update_prices: diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py index 96813ad4..60b28385 100644 --- a/tests/test_end_to_end.py +++ b/tests/test_end_to_end.py @@ -75,6 +75,34 @@ def test_anthropic_caching_read(): assert price.total_price == snapshot(Decimal('0.0462513')) +def test_anthropic_with_web_search(): + response = dict( + model='claude-3-7-sonnet-20250219', + usage=dict( + cache_creation_input_tokens=0, + cache_read_input_tokens=0, + input_tokens=10809, + output_tokens=644, + server_tool_use=dict(web_search_requests=1), + service_tier='standard', + ), + ) + + extracted_usage = extract_usage(response, provider_id='anthropic') + assert extracted_usage.usage == snapshot( + Usage( + input_tokens=10809, cache_write_tokens=0, cache_read_tokens=0, output_tokens=644, tool_use={'web_search': 1} + ) + ) + price = extracted_usage.calc_price() + # input: 3 * 10809 / 1e6 = 0.032427 + # output: 15 * 644 / 1e6 = 0.00966 + # web_search: 10 * 1 / 1000 = 0.01 + assert price.input_price == snapshot(Decimal('0.032427')) + assert price.output_price == snapshot(Decimal('0.00966')) + assert price.total_price == snapshot(Decimal('0.052087')) + + def test_openai_without_caching(): response = dict( model='gpt-4.1-2025-04-14', diff --git a/tests/test_extract_usage.py b/tests/test_extract_usage.py index 8c35a630..900da0dd 100644 --- a/tests/test_extract_usage.py +++ b/tests/test_extract_usage.py @@ -83,6 +83,62 @@ def test_extract_usage_ok(response_data: Any, expected_model: str, expected_usag assert extracted_usage.calc_price().total_price == expected_price +def test_extract_web_search_requests(): + provider = next(provider for provider in providers if provider.id == 'anthropic') + response_data = { + 'model': 'claude-sonnet-4-20250514', + 'usage': { + 'input_tokens': 504, + 'cache_creation_input_tokens': 0, + 'cache_read_input_tokens': 0, + 'output_tokens': 97, + 'server_tool_use': {'web_search_requests': 2}, + 'service_tier': 'standard', + }, + } + model, usage = provider.extract_usage(response_data) + assert model == snapshot('claude-sonnet-4-20250514') + assert usage == snapshot( + Usage(input_tokens=504, cache_write_tokens=0, cache_read_tokens=0, output_tokens=97, tool_use={'web_search': 2}) + ) + + extracted_usage = extract_usage(response_data, provider_id='anthropic') + assert extracted_usage.usage.tool_use == {'web_search': 2} + price = extracted_usage.calc_price() + # total = input + output + web_search = (3 * 504 / 1e6) + (15 * 97 / 1e6) + (10 * 2 / 1000) + assert price.total_price == snapshot(Decimal('0.02296700')) + + +def test_extract_web_search_requests_zero(): + provider = next(provider for provider in providers if provider.id == 'anthropic') + response_data = { + 'model': 'claude-sonnet-4-20250514', + 'usage': { + 'input_tokens': 504, + 'output_tokens': 97, + 'server_tool_use': {'web_search_requests': 0}, + }, + } + model, usage = provider.extract_usage(response_data) + assert model == snapshot('claude-sonnet-4-20250514') + assert usage == snapshot(Usage(input_tokens=504, output_tokens=97, tool_use={'web_search': 0})) + + +def test_extract_web_search_requests_absent(): + provider = next(provider for provider in providers if provider.id == 'anthropic') + response_data = { + 'model': 'claude-sonnet-4-20250514', + 'usage': { + 'input_tokens': 504, + 'output_tokens': 97, + }, + } + model, usage = provider.extract_usage(response_data) + assert model == snapshot('claude-sonnet-4-20250514') + assert usage == snapshot(Usage(input_tokens=504, output_tokens=97)) + assert usage.tool_use is None + + def test_openai(): provider = next(provider for provider in providers if provider.id == 'openai') assert provider.name == 'OpenAI' @@ -289,3 +345,5 @@ def test_accumulate_extracted_usage(): input_tokens=10, output_tokens=20 ) assert Usage(input_audio_tokens=10) + Usage(input_tokens=10) == Usage(input_audio_tokens=10, input_tokens=10) + assert Usage(tool_use={'web_search': 2}) + Usage(tool_use={'web_search': 3}) == Usage(tool_use={'web_search': 5}) + assert Usage(tool_use={'web_search': 1}) + Usage() == Usage(tool_use={'web_search': 1}) diff --git a/tests/test_package_schema.py b/tests/test_package_schema.py index 9cb77988..e864a5c0 100644 --- a/tests/test_package_schema.py +++ b/tests/test_package_schema.py @@ -17,7 +17,7 @@ def remove_ignored_fields(json_schema: Any): if isinstance(json_schema, dict): json_schema = cast(dict[str, Any], json_schema) - for f in 'description', 'maxLength', 'minLength', 'pattern', 'additionalProperties': + for f in 'description', 'maxLength', 'minLength', 'pattern', 'additionalProperties', 'propertyNames': json_schema.pop(f, None) for value in json_schema.values(): diff --git a/tests/test_price_calc.py b/tests/test_price_calc.py index f96ab545..c9742f99 100644 --- a/tests/test_price_calc.py +++ b/tests/test_price_calc.py @@ -77,6 +77,76 @@ def test_requests_kcount_prices(): assert price.provider.name == snapshot('Perplexity') +def test_web_search_kcount_prices(): + # Anthropic charges $10/1000 web search requests + price = calc_price( + Usage(input_tokens=1000, output_tokens=100, tool_use={'web_search': 3}), + model_ref='claude-sonnet-4-20250514', + provider_id='anthropic', + ) + assert price.input_price == snapshot(Decimal('0.003')) + assert price.output_price == snapshot(Decimal('0.0015')) + # total = input + output + (10 * 3 / 1000) = 0.0045 + 0.03 + assert price.total_price == snapshot(Decimal('0.0345')) + assert price.model.name == snapshot('Claude Sonnet 4') + assert price.provider.name == snapshot('Anthropic') + + +def test_web_search_kcount_zero(): + # web_search=0 should not add any cost + price = calc_price( + Usage(input_tokens=1000, output_tokens=100, tool_use={'web_search': 0}), + model_ref='claude-sonnet-4-20250514', + provider_id='anthropic', + ) + assert price.total_price == snapshot(Decimal('0.0045')) + + +def test_web_search_kcount_none(): + # tool_use=None (default) should not add any cost + price = calc_price( + Usage(input_tokens=1000, output_tokens=100), + model_ref='claude-sonnet-4-20250514', + provider_id='anthropic', + ) + assert price.total_price == snapshot(Decimal('0.0045')) + + +def test_file_search_kcount_prices(): + # OpenAI charges $2.50/1000 file search requests + price = calc_price( + Usage(input_tokens=1000, output_tokens=100, tool_use={'file_search': 4}), + model_ref='gpt-4o', + provider_id='openai', + ) + assert price.input_price == snapshot(Decimal('0.0025')) + assert price.output_price == snapshot(Decimal('0.001')) + # total = input + output + (2.5 * 4 / 1000) = 0.0035 + 0.01 + assert price.total_price == snapshot(Decimal('0.0135')) + assert price.model.name == snapshot('gpt 4o') + assert price.provider.name == snapshot('OpenAI') + + +def test_file_search_kcount_zero(): + # file_search=0 should not add any cost + price = calc_price( + Usage(input_tokens=1000, output_tokens=100, tool_use={'file_search': 0}), + model_ref='gpt-4o', + provider_id='openai', + ) + assert price.total_price == snapshot(Decimal('0.0035')) + + +def test_file_search_kcount_none(): + # tool_use=None (default) should not add any cost + price = calc_price( + Usage(input_tokens=1000, output_tokens=100), + model_ref='gpt-4o', + provider_id='openai', + ) + assert price.total_price == snapshot(Decimal('0.0035')) + + def test_price_constraint_before(): price = calc_price(Usage(input_tokens=1000), model_ref='o3', genai_request_timestamp=datetime(2025, 6, 1)) assert price.input_price == snapshot(Decimal('0.01'))