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Team"},"license":"MIT","homepage":"https://github.com/pydantic/genai-prices/","keywords":["pydantic","genai","prices","llm","inference","api"],"repository":{"url":"git+https://github.com/pydantic/genai-prices.git","type":"git"},"description":"Calculate prices for calling LLM inference APIs","maintainers":[{"name":"viicos","email":"contact@vctrn.dev"},{"name":"samuelcolvin","email":"s@muelcolvin.com"},{"name":"pydantic-user","email":"engineering@pydantic.dev"},{"name":"dmontagu","email":"david@pydantic.dev"},{"name":"petyosi","email":"underlog@gmail.com"}],"readme":"# @pydantic/genai-prices\n\nJavaScript package and command-line tool for calculating LLM API prices.\n\n## Basic usage\n\n### `calcPrice`\n\nThe package exports a function for price calculation that, by default, uses the bundled price data.\n\n```ts\nimport { calcPrice } from '@pydantic/genai-prices'\n\nconst usage = { input_tokens: 1000, output_tokens: 100 }\n\nconst result = calcPrice(usage, 'gpt-3.5-turbo', { providerId: 'openai' })\n\nif (result) {\n  console.log(\n    `$${result.total_price} (input: $${result.input_price}, output: $${result.output_price})`,\n    result.provider.name,\n    result.model.name\n  )\n} else {\n  console.log('No price found for this model/provider combination')\n}\n```\n\n### Cached input tokens\n\n```ts\nimport { calcPrice } from '@pydantic/genai-prices'\n\nconst result = calcPrice(\n  {\n    input_tokens: 4740,\n    cache_read_tokens: 0,\n    cache_write_tokens: 4735,\n    output_tokens: 255,\n  },\n  'claude-sonnet-4-20250514',\n  { providerId: 'anthropic' }\n)\n\nif (!result) {\n  throw new Error('No price found for claude-sonnet-4-20250514')\n}\n\nconsole.log(result.total_price)\n```\n\n`input_tokens` is the total number of input tokens. It includes uncached tokens, cache-read tokens, and cache-write\ntokens. You also report `cache_read_tokens` and `cache_write_tokens` so that `calcPrice` can apply their separate rates.\n\nDo not pass only the uncached count as `input_tokens`. Cache tokens are partitions of the total, so their combined count\ncannot exceed `input_tokens`.\n\n### Fractional usage values\n\nEvery reportable usage unit accepts finite non-negative numbers, including fractions. This is useful for duration units\nsuch as seconds:\n\n```ts\nconst usage = { audio_seconds: 0.25 }\nJSON.stringify(usage)\n```\n\nUsage remains a plain JSON-serializable object. JavaScript uses ordinary `number` arithmetic, so compare fractional\nresults with an appropriate numerical tolerance. The package guards decomposition against insignificant floating-point\nresiduals, but it cannot recover decimal precision that was already lost before a value was supplied.\n\nFor Groq's Whisper models, report the transcription duration as `audio_seconds` or `input_audio_seconds`. These models\napply Groq's documented 10-second minimum. A missing or zero duration costs zero.\n\n### `updatePrices`\n\nYou can optionally use `updatePrices` to implement logic that can periodically update the data used by `calcPrice`.\nSee the `src/examples/browser` directory for an example that implements a local storage-backed auto-update and `src/examples/node-script.ts` for an example of a file-based asynchronous auto-update implementation.\n\n`calcPrice` is a synchronous function that uses the currently available data - either the bundled one, or the last data fetched from the `updatePrices` setup. To force `calcPrice` to await potential in-progress data updates that can happen in `enableAutoUpdate`, await the `waitForUpdate()` return value before calling `calcPrice`\n\n```ts\nimport { calcPrice, updatePrices } from '@pydantic/genai-prices'\n\nenableAutoUpdate(/** auto-update logic */)\n\n// ...\n\n// this guarantees that the latest data is used\nawait waitForUpdate()\nconst result = calcPrice(usage, 'gpt-5', { providerId: 'openai' })\n\nconsole.log(\n  `$${result.total_price} (input: $${result.input_price}, output: $${result.output_price})`,\n  result.provider.name,\n  result.model.name\n)\n```\n\n### Provider Matching\n\nThe library uses intelligent provider matching:\n\n1. **Explicit provider**: Use `providerId` parameter or `provider:model` format\n2. **Model-based matching**: Uses the provider's `model_match` logic (e.g., OpenAI matches models starting with \"gpt-\")\n3. **Fallback**: Tries to match based on model name patterns\n\n**Best practices:**\n\n- Always specify `providerId` if you know it (e.g., `openai`, `google`, etc.) for best results\n- Use `provider:model` format in CLI for explicit provider selection\n- The async API with `--auto-update` provides the most up-to-date pricing\n\n### Custom Pricing / New Models\n\nIf a model is not yet available in the bundled catalog, you can provide your own pricing definition. By passing a `provider` object in the options, the library will use your custom data instead of the internal catalog.\n\n```ts\nimport { calcPrice, Provider } from '@pydantic/genai-prices'\n\nconst customProvider: Provider = {\n  id: 'custom-provider',\n  name: 'Custom Provider',\n  api_pattern: '.*',\n  models: [\n    {\n      id: 'my-new-model',\n      match: { equals: 'my-new-model' },\n      prices: {\n        input_mtok: 2.5,\n        output_mtok: 10.0,\n      },\n    },\n  ],\n}\n\nconst usage = { input_tokens: 1000, output_tokens: 100 }\nconst result = calcPrice(usage, 'my-new-model', { provider: customProvider })\n```\n\n**Required fields:**\n\n- `Provider`: `id`, `name`, `api_pattern`, and `models` array\n- `ModelInfo`: `id`, `match` (determines how the model ID is matched), and `prices`\n\n**Pricing fields.** The full set is derived from\n[`prices/units.yml`](https://github.com/pydantic/genai-prices/blob/main/prices/units.yml); the common\nper-million-token ones are:\n\n- `input_mtok` / `output_mtok`: Text token pricing\n- `cache_read_mtok` / `cache_write_mtok`: Prompt caching pricing\n- `input_audio_mtok` / `output_audio_mtok`: Audio token pricing\n\nThe key suffix tells you the unit, and **not every key is per million**: `_mtok` is per 1M tokens,\n`_kcount` per 1,000 (e.g. `requests_kcount`, `web_searches_kcount`), `_mchars` per 1M characters,\n`_hours` per 3,600 seconds, `_gpixels` per 1e9 pixels, `_kpages` per 1,000 pages.\n\nPrices must also cover their ancestors: a model priced with `cache_write_1h_mtok` needs\n`cache_write_mtok` as well, or pricing throws at runtime.\n\n### Error Handling\n\nWhen a model or provider is not found, the library returns `null`. This makes it easier to handle cases where pricing information might not be available.\n\n```js\nimport { calcPrice } from '@pydantic/genai-prices'\n\nconst usage = { input_tokens: 1000, output_tokens: 100 }\n\n// Returns null if model/provider not found\nconst result = calcPrice(usage, 'non-existent-model')\nif (result === null) {\n  console.log('No pricing information available for this model')\n} else {\n  console.log(`Total Price: $${result.total_price} (input: $${result.input_price}, output: $${result.output_price})`)\n}\n\n// Async version also returns null\nconst asyncResult = await calcPrice(usage, 'non-existent-model', { awaitAutoUpdate: true, providerId: 'unknown-provider' })\nif (asyncResult === null) {\n  console.log('No pricing information available for this model/provider combination')\n} else {\n  console.log(`Total Price: $${asyncResult.total_price} (input: $${asyncResult.input_price}, output: $${asyncResult.output_price})`)\n}\n```\n\n## Troubleshooting\n\n### Common Issues\n\n- **No price found (returns null)**:\n  - Make sure you specify the correct `providerId` (e.g., `openai`, `google`, `anthropic`)\n  - Try using `provider:model` format in CLI\n  - Use `--auto-update` flag to fetch latest data\n  - Check that the model name is correct and supported by the provider\n\n## CLI\n\nThe easiest way to run the latest version of the package as a CLI tool is through npx:\n\n```bash\nnpx @pydantic/genai-prices@latest\n```\n\nFor example:\n\n```bash\nnpx @pydantic/genai-prices@latest calc gpt-4 --input-tokens 1000 --output-tokens 500\nnpx @pydantic/genai-prices@latest list\n```\n\nYou can also install it globally and then use the `genai-prices` command:\n\n```bash\nnpm i -g @pydantic/genai-prices\n```\n\n```bash\n# Basic usage\ngenai-prices gpt-5 --input-tokens 1000 --output-tokens 100\n\n# Specify provider explicitly\ngenai-prices openai:gpt-5 --input-tokens 1000 --output-tokens 100\n\n# List available providers and models\ngenai-prices list\ngenai-prices list openai\n```\n","readmeFilename":"README.md"}