The open-source registry of LLM models, inference providers, and provider-model mappings. Community-maintained, schema-validated, and machine-readable.
Current stats: 138 models · 52 providers · 209 mappings
OpenModels is a structured, version-controlled registry that serves as the single source of truth for LLM model metadata, inference provider details, and provider-model mappings (including pricing and rate limits). All data is stored as human-readable YAML files and validated against JSON Schemas on every pull request.
| Vendor | Models |
|---|---|
| OpenAI | GPT-5.6 Terra, GPT-5.6 Sol, GPT-5.6 Luna, GPT-5.5, GPT-5.4 Mini, GPT-OSS 120B, GPT-OSS 20B, GPT-5.4, GPT-5.5 Pro, GPT-5, GPT-4 |
| Anthropic | Claude Opus 5, Claude Sonnet 5, Claude Mythos 5, Claude Fable 5, Claude Opus 4.8, Claude Opus 4.7, Claude Sonnet 4.6, Claude Opus 4.6, Claude Sonnet 4.5, Claude Haiku 4.5, Claude 3 Opus |
| Gemini 3.7 Flash, Gemini 3.6 Flash, Gemini 3.5 Flash-Lite, Gemini 3.5 Flash Cyber, Gemini 3.5 Pro, DiffusionGemma, Gemma 4 12B, Gemini 3 Flash, Gemini 3.5 Flash, Gemini 3.1 Flash-Lite, Gemma 4 (E2B/E4B/26B/31B), Gemini 3.1 Pro, Gemini 2.5 Pro, Gemini 2.5 Flash, Gemma 3 (1B/4B/12B/27B) | |
| xAI | Grok 4.6, Grok 4.5, Grok 4.3, Grok 4.20, Grok 4.1 Fast, Grok 4 |
| DeepSeek | DeepSeek V4 Pro, DeepSeek V4 Flash, DeepSeek V4, DeepSeek R1, DeepSeek V3 |
| Meta | Muse Spark 1.1, Muse Spark, Llama 4 Scout, Llama 4 Maverick, Llama 3.3 70B, Llama 3.2 (3B/11B/90B), Llama 3.1 8B |
| Mistral | Mistral Small 4, Mistral Medium 3.5, Mistral Large 3, Devstral 2, Mistral Small 3.1, Codestral |
| Alibaba | Qwen3.8-Flash-Next, Qwen 3.8 Max, Qwen 3.7 Plus, Qwen 3.7 Max, Qwen 3.6 (27B, 35B-A3B, Plus), Qwen 3.6, Qwen3 Coder, Qwen3 235B, Qwen3 32B, QwQ-32B |
| Moonshot | Kimi K3, Kimi K2.7 Code, Kimi K2.6 |
| Cohere | Command A+, Command A, Command R7B |
| Cohere For AI | Tiny Aya, Aya Expanse 32B |
| Zhipu | GLM-5.3-Flash, GLM-5.3, GLM-5.2, GLM-5.1, GLM-4.7 |
| MiniMax | M3, M2.7 |
| Sarvam AI | Sarvam-M, Sarvam-105B, Sarvam-30B, Sarvam-1 |
| Sakana AI | Sakana Fugu Ultra, Sakana Fugu |
| Motif Technologies | Motif 3 Beta |
| Thinking Machines Lab | Inkling |
| OpenBMB | MiniCPM-V 4.6 |
| NVIDIA | Nemotron 3 Ultra, Nemotron 3 Super 120B, Nemotron Nano 9B |
| Microsoft | Phi-4 Mini, Phi-4 |
| Yandex | YandexGPT 5 Lite |
| Sber | GigaChat 3.1 Ultra, GigaChat 3.1 Lightning |
| ISSAI | KazLLM 1.0 70B |
| Astana Hub | AlemLLM |
| IBM | Granite 4.1 30B, Granite 4.1 8B |
| Xiaomi | MiMo-V2.5-Pro |
| MTS AI | Cotype Nano |
| Tencent | Hy3 Preview |
| Poolside | Laguna M.1 |
| AI21 Labs | Jamba Large 1.7 |
| TII | Falcon-H1, Falcon 3 10B |
| 01.AI | Yi-Lightning |
| Writer | Palmyra X5 |
| Databricks | DBRX |
| Snowflake | Arctic |
| Stability AI | StableLM 2 12B |
| Uzbek LLM Lab | Alloma 8B Instruct |
| InclusionAI | Ring-2.6-1T |
| Upstage | Solar Pro 3 |
| LLM360/MBZUAI | K2 Think |
| OpenAI (Audio) | Whisper |
| VNGRS | Kumru 7B |
| Trendyol | Trendyol LLM 8B T1 |
| WiroAI | WiroAI Turkish LLM 9B |
01.AI · AI21 Labs · Alibaba Model Studio · Amazon Bedrock · Anthropic · Anyscale · Azure AI · Baseten · Cerebras · Cloudflare Workers AI · Cohere · Deep Infra · DeepSeek · Featherless · Fireworks · Google AI Studio · Google (Vertex AI) · Groq · Hugging Face Inference · Hyperbolic · IBM watsonx.ai · InclusionAI · Inference.net · Lambda · Meta · MiniMax · Mistral · Modal · Moonshot · Nebius · NLP Cloud · Novita · NVIDIA NIM · OpenAI · OpenRouter · Perplexity · Reka AI · Replicate · Sakana AI · SambaNova · Sarvam AI · Sber · Scaleway · SiliconFlow · Snowflake Cortex AI · Tinker · Together AI · Upstage · xAI · Xiaomi MiMo · Yandex Cloud · Zhipu AI
openmodels/
├── models/ # Canonical model definitions (YAML)
├── providers/ # Inference provider definitions (YAML)
├── mappings/ # Provider-model mappings with pricing (YAML)
│ ├── anthropic/
│ ├── openai/
│ ├── together-ai/
│ └── ...
├── schemas/ # JSON Schema definitions for validation
│ ├── model.schema.json
│ ├── provider.schema.json
│ └── mapping.schema.json
├── validate_registry.py # Validation script
└── requirements.txt # Python dependencies
We welcome contributions from the community. You can add new models, providers, or mappings by opening a pull request.
- Fork this repository
- Create a new branch for your changes
- Add or update YAML files (see templates below)
- Run validation locally to check your changes
- Open a pull request
Create a new file at models/{model-id}.yaml:
id: my-new-model # Unique ID (kebab-case, lowercase)name: My New Model # Display namedescription: A brief description of the model and its capabilities.capabilities: # What the model can do
- chat
- completion
- function-calling
- code-generationmodalities: # Input/output modalities
- text
- codecontext_window: 128000# Max context window in tokenslicensing: apache-2.0 # License (e.g., proprietary, apache-2.0, mit)created_at: "2025-01-01T00:00:00.000Z"updated_at: "2025-01-01T00:00:00.000Z"Required fields: id, name, description, capabilities, modalities, context_window, licensing, created_at, updated_at
Create a new file at providers/{provider-id}.yaml:
id: my-provider # Unique ID (kebab-case, lowercase)name: My Provider # Display namedescription: Description of the inference provider.api_base_url: https://api.myprovider.com/v1auth_type: api-key # One of: bearer, api-key, oauth2regions: # Deployment regions
- us-east-1
- eu-west-1compatibility: openai # API compatibility (openai, anthropic, custom)created_at: "2025-01-01T00:00:00.000Z"updated_at: "2025-01-01T00:00:00.000Z"Required fields: id, name, description, api_base_url, auth_type, regions, compatibility, created_at, updated_at
Create a new file at mappings/{provider-id}/{model-id}.yaml:
model_id: deepseek-v3 # Must reference an existing modelprovider_id: together-ai # Must reference an existing providerprovider_model_name: deepseek-ai/DeepSeek-V3 # Provider's internal model namepricing:
input_per_million: 0.90# Price per 1M input tokens (required)output_per_million: 0.90# Price per 1M output tokens (required)currency: USD # ISO 4217 currency code (required)rate_limits:
requests_per_minute: 600tokens_per_minute: 1000000context_window_override: null # Override model's default (or null)available_regions: # Subset of provider's regions
- us-east-1created_at: "2025-01-01T00:00:00.000Z"updated_at: "2025-01-01T00:00:00.000Z"Required fields: model_id, provider_id, provider_model_name, pricing, rate_limits, available_regions, created_at, updated_at
The pricing structure supports optional fields for specialized pricing dimensions:
| Field | Description |
|---|---|
input_per_million | Price per 1M input tokens (required) |
output_per_million | Price per 1M output tokens (required) |
currency | ISO 4217 currency code (required) |
cache_write_per_million | Price per 1M tokens written to cache |
cache_read_per_million | Price per 1M tokens read from cache |
image_per_unit | Price per image unit (vision models) |
audio_per_second | Price per second of audio |
reasoning_tokens_per_million | Price per 1M reasoning tokens |
search_grounding_per_query | Price per search grounding query |
See mappings/.example-extended-pricing.yaml for a complete example.
- Use kebab-case (lowercase letters, numbers, and hyphens only)
- Must be unique across all files of the same type
- Examples:
deepseek-v3,together-ai,gpt-5-4-mini
All pull requests are automatically validated by GitHub Actions. The validation checks:
- YAML Syntax — All YAML files must be parseable
- Schema Validation — Files must conform to their JSON Schema (
schemas/) - Duplicate IDs — Model and provider IDs must be unique
- Referential Integrity — Mappings must reference existing models and providers
# Install dependencies
pip install -r requirements.txt
# Run the validation script
python validate_registry.pyExample output on success:
✅ Validation Passed
Validated 135 files successfully.
Example output on failure:
❌ Validation Failed
Summary: 2 of 135 files failed validation.
Schema Validation Errors:
models/invalid-model.yaml
- context_window: -1000 is less than the minimum of 1
JSON Schema definitions in schemas/ enforce the structure of all YAML files:
model.schema.json— Model definition schemaprovider.schema.json— Provider definition schemamapping.schema.json— Mapping definition schema
The validation workflow triggers on pull requests that modify:
models/**,providers/**,mappings/**schemas/**validate_registry.py,requirements.txt
The workflow will:
- Install Python dependencies
- Run the validation script
- Post validation results as a PR comment
- Block the PR if validation fails
On merge to main, the ingestion workflow automatically loads updated data into the platform database.
The OpenModels platform provides a REST API and web interface for querying registry data, comparing providers, and viewing telemetry (health, latency, uptime).
- API Documentation: Available at
/api/docs(Swagger UI) and/api/docs/openapi.json(OpenAPI spec) - Web Interface: Browse models, compare providers, and view real-time telemetry
- Registry Repository:github.com/openmodelsrun/openmodels
- Documentation Site:github.com/openmodelsrun/docs
This registry is open source. See the repository for license details.