AI 모델 스펙·가격·기능 통합 오픈소스 DB 공개

Models.dev is a comprehensive open-source database of AI model specifications, pricing, and capabilities. There's no single database with information about all the available AI models. We started Models.dev as a community-contributed project to address this. We also use it internally in opencode . API You can access this data through an API. curl https://models.dev/api.json Use the Model ID field to do a lookup on any model; it's the identifier used by AI SDK . Logos Provider logos are available as SVG files: curl https://models.dev/logos/{provider}.svg Replace {provider} with the Provider ID (e.g., anthropic , openai , google ). If we don't have a provider's logo, a default logo is served instead. Contributing The data is stored in the repo as TOML files; organized by provider and model. The logo is stored as an SVG. This is used to generate this page and power the API. We need your help keeping the data up to date. Adding a New Model To add a new model, start by checking if the provider already exists in the providers/ directory. If not, then: 1. Create a Provider If the provider isn't already in providers/ : Create a new folder in providers/ with the provider's ID. For example, providers/newprovider/ . Add a provider.toml with the provider details: name = " Provider Name " npm = " @ai-sdk/provider " # AI SDK Package name env = [ " PROVIDER_API_KEY " ] # Environment Variable keys used for auth doc = " https://example.com/docs/models " # Link to provider's documentation If the provider doesn’t publish an npm package but exposes an OpenAI-compatible endpoint, set the npm field accordingly and include the base URL: npm = " @ai-sdk/openai-compatible " # Use OpenAI-compatible SDK api = " https://api.example.com/v1 " # Required with openai-compatible 2. Add a Logo (optional) To add a logo for the provider: Add a logo.svg file to the provider's directory (e.g., providers/newprovider/logo.svg ) Use SVG format with no fixed size or colors - use currentColor for fills/strokes Example SVG structure: < svg xmlns = " http://www.w3.org/2000/svg " viewBox = " 0 0 24 24 " fill = " currentColor " > <!-- Logo paths here --> </ svg > 3. Add a Model Definition Create a new TOML file in the provider's models/ directory where the filename is the model ID. If the model ID contains / , use subfolders. For example, for the model ID openai/gpt-5 , create a folder openai/ and place a file named gpt-5.toml inside it. name = " Model Display Name " attachment = true # or false - supports file attachments reasoning = false # or true - supports reasoning / chain-of-thought tool_call = true # or false - supports tool calling structured_output = true # or false - supports a dedicated structured output feature temperature = true # or false - supports temperature control knowledge = " 2024-04 " # Knowledge-cutoff date release_date = " 2025-02-19 " # First public release date last_updated = " 2025-02-19 " # Most recent update date open_weights = true # or false - model’s trained weights are publicly available [ cost ] input = 3.00 # Cost per million input tokens (USD) output = 15.00 # Cost per million output tokens (USD) reasoning = 15.00 # Cost per million reasoning tokens (USD) cache_read = 0.30 # Cost per million cached read tokens (USD) cache_write = 3.75 # Cost per million cached write tokens (USD) input_audio = 1.00 # Cost per million audio input tokens (USD) output_audio = 10.00 # Cost per million audio output tokens (USD) [ limit ] context = 400_000 # Maximum context window (tokens) input = 272_000 # Maximum input tokens output = 8_192 # Maximum output tokens [ modalities ] input = [ " text " , " image " ] # Supported input modalities output = [ " text " ] # Supported output modalities [ interleaved ] field = " reasoning_content " # Name of the interleaved field "reasoning_content" or "reasoning_details" 3a. Reuse an Existing Model with extends For wrapper providers that mirror a model from another provider, prefer reusing the canonical model definition instead of duplicating the whole file. Use extends only for non-first-party wrappers and mirrors. Do not use it inside the actual lab provider directories that act as the canonical source for a model family, for example providers/anthropic/ , providers/openai/ , providers/google/ , providers/xai/ , providers/minimax/ , or providers/moonshot/ . [ extends ] from = " anthropic/claude-opus-4-6 " omit = [ " experimental.modes.fast " ] [ provider ] npm = " @ai-sdk/anthropic " Rules: from must point to another model using <provider>/<model-id> . omit is optional and removes fields after the inherited model and local overrides are merged. You can override any top-level model field locally. If you override a nested table like [cost] , [limit] , or [modalities] , include the full values needed for that table. id still comes from the filename; do not add it to the TOML. Use extends when the wrapper model is materially the same as the source model and only differs by a small set of overrides or omitted fields. 4. Submit a Pull Request Fork this repo Create a new branch with your changes Add your provider and/or model files Open a PR with a clear description Validation There's a GitHub Action that will automatically validate your submission against our schema to ensure: All required fields are present Data types are correct Values are within acceptable ranges TOML syntax is valid When converting existing wrapper models to extends , compare generated output before and after the change: bun run compare:migrations This prints a diff for each changed model TOML so you can confirm the generated JSON only changed where you intended. Schema Reference Models must conform to the following schema, as defined in packages/core/src/schema.ts . Provider Schema: name : String - Display name of the provider npm : String - AI SDK Package name env : String[] - Environment variable keys used for auth doc : String - Link to the provider's documentation api (optional) : String - OpenAI-compatible API endpoint. Required only when using @ai-sdk/openai-compatible as the npm package Model Schema: name : String — Display name of the model attachment : Boolean — Supports file attachments reasoning : Boolean — Supports reasoning / chain-of-thought tool_call : Boolean - Supports tool calling structured_output (optional) : Boolean — Supports structured output feature temperature (optional) : Boolean — Supports temperature control knowledge (optional) : String — Knowledge-cutoff date in YYYY-MM or YYYY-MM-DD format release_date : String — First public release date in YYYY-MM or YYYY-MM-DD last_updated : String — Most recent update date in YYYY-MM or YYYY-MM-DD open_weights : Boolean - Indicate the model's trained weights are publicly available interleaved (optional) : Boolean or Object — Supports interleaved reasoning. Use true for general support or an object with field to specify the format interleaved.field : String — Name of the interleaved field ( "reasoning_content" or "reasoning_details" ) cost.input : Number — Cost per million input tokens (USD) cost.output : Number — Cost per million output tokens (USD) cost.reasoning (optional) : Number — Cost per million reasoning tokens (USD) cost.cache_read (optional) : Number — Cost per million cached read tokens (USD) cost.cache_write (optional) : Number — Cost per million cached write tokens (USD) cost.input_audio (optional) : Number — Cost per million audio input tokens, if billed separately (USD) cost.output_audio (optional) : Number — Cost per million audio output tokens, if billed separately (USD) limit.context : Number — Maximum context window (tokens) limit.input : Number — Maximum input tokens limit.output : Number — Maximum output tokens modalities.input : Array of strings — Supported input modalities (e.g., ["text", "image", "audio", "video", "pdf"]) modalities.output : Array of strings — Supported output modalities (e.g., ["text"]) status (optional) : String — Supported status: alpha - Indicate the model is in