# models.dev **Repository Path**: geodon/models.dev ## Basic Information - **Project Name**: models.dev - **Description**: Models.dev is a comprehensive open-source database of AI model specifications, pricing, and capabilities. - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: dev - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-06-30 - **Last Updated**: 2026-06-30 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

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--- [Models.dev](https://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](https://opencode.ai). ## API You can access this data through an API. ```bash 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](https://ai-sdk.dev/). Provider-agnostic model metadata is available separately: ```bash curl https://models.dev/models.json ``` Use this for facts about the model itself, independent of where it is served. If you need both provider endpoints and model-only metadata in one response: ```bash curl https://models.dev/catalog.json ``` ### Logos Provider logos are available as SVG files: ```bash 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 Model Metadata Model-only facts live in `models/`, using the same path-style IDs as provider models. For example, `models/openai/gpt-5.toml` defines metadata for the underlying GPT-5 model, while `providers/openai/models/gpt-5.toml` defines OpenAI-specific serving details such as pricing. Use model metadata for provider-agnostic facts: - `name`, `family`, `release_date`, `last_updated`, `knowledge` - `attachment`, `reasoning`, `tool_call`, `structured_output`, `temperature` - `[limit]` defaults like context, input, and output token limits - `[modalities]` defaults - `open_weights`, `license`, `links`, `weights`, and `benchmarks` Example: ```toml name = "GPT-5" family = "gpt" release_date = "2025-08-07" last_updated = "2025-08-07" attachment = true reasoning = true temperature = false tool_call = true structured_output = true open_weights = false [limit] context = 400_000 input = 272_000 output = 128_000 [modalities] input = ["text", "image"] output = ["text"] [[benchmarks]] name = "Benchmark Name" score = 72.5 metric = "accuracy" source = "https://example.com/results" [[weights]] label = "Model weights" url = "https://huggingface.co/example/model" format = "safetensors" ``` Provider TOMLs can inherit these facts with `base_model` and then keep only provider-specific fields or overrides: ```toml base_model = "openai/gpt-5" [cost] input = 1.25 output = 10.00 cache_read = 0.125 [limit] context = 200_000 # optional provider override output = 32_000 ``` Provider fields win over model metadata during generation. Use this when the underlying model is the same but a provider serves it with different context limits, modalities, features, or pricing. ### Adding a New Provider 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/`: 1. Create a new folder in `providers/` with the provider's ID. For example, `providers/newprovider/`. 2. Add a `provider.toml` with the provider details: ```toml 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: ```toml 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: 1. Add a `logo.svg` file to the provider's directory (e.g., `providers/newprovider/logo.svg`) 2. Use SVG format with no fixed size or colors - use `currentColor` for fills/strokes Example SVG structure: ```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. ```toml 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 Model Metadata with `base_model` For wrapper providers that mirror an existing model, prefer referencing the model-only metadata instead of duplicating provider-agnostic fields. Use `base_model` when the provider serves the same underlying model and only provider-specific fields differ. ```toml base_model = "anthropic/claude-opus-4-6" [cost] input = 5.00 output = 25.00 ``` Rules: - `base_model` must point to a TOML file in `models/` using `/`. - 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. - `base_model_omit` is optional and removes inherited model metadata fields after local overrides are merged. Use dot-path strings, for example `base_model_omit = ["limit.input"]`. - `id` still comes from the filename; do not add it to the TOML. Use `base_model` when the wrapper model is materially the same as the source model and only differs by provider-specific pricing, limits, modalities, provider request shape, or lifecycle flags. Sync and generator scripts should preserve existing `base_model` / `base_model_omit` fields when updating provider TOMLs. Do not use legacy `[extends]` tables. #### 4. Submit a Pull Request 1. Fork this repo 2. Create a new branch with your changes 3. Add your provider and/or model files 4. 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 moving existing provider fields into model metadata, compare generated output before and after the change: ```bash 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 alpha testing - `beta` - Indicate the model is in beta testing - `deprecated` - Indicate the model is no longer served by the provider's public API ### Examples See existing providers in the `providers/` directory for reference: - `providers/anthropic/` - Anthropic Claude models - `providers/openai/` - OpenAI GPT models - `providers/google/` - Google Gemini models ### Working on frontend Make sure you have [Bun](https://bun.sh/) installed. ```bash $ bun install $ cd packages/web $ bun run dev ``` And it'll open the frontend at http://localhost:3000 ### Manual testing with opencode You can manually check provider changes with opencode by: ```bash $ bun install $ cd packages/web $ bun run build $ OPENCODE_MODELS_PATH="dist/_api.json" opencode ``` ### Questions? Open an issue if you need help or have questions about contributing. --- Models.dev is created by the maintainers of [SST](https://sst.dev). **Join our community** [Discord](https://sst.dev/discord) | [YouTube](https://www.youtube.com/c/sst-dev) | [X.com](https://x.com/SST_dev)