LM StudioLM Studio

REST API v0

The REST API includes enhanced stats such as Token / Second and Time To First Token (TTFT), as well as rich information about models such as loaded vs unloaded, max context, quantization, and more.

Heads Up

LM Studio now has a v1 REST API! We recommend using the v1 API for new projects!

Requires LM Studio 0.3.6 or newer.

LM Studio now has its own REST API, in addition to OpenAI-compatible endpoints (learn more) and Anthropic-compatible endpoints (learn more).

The REST API includes enhanced stats such as Token / Second and Time To First Token (TTFT), as well as rich information about models such as loaded vs unloaded, max context, quantization, and more.

Supported API Endpoints

Start the REST API server

To start the server, run the following command:

lms server start

Pro Tip

You can run LM Studio as a service and get the server to auto-start on boot without launching the GUI. Learn about Headless Mode.

Endpoints

GET /api/v0/models

List all loaded and downloaded models

Example request

curl -H "Authorization: Bearer $LM_API_TOKEN" http://localhost:1234/api/v0/models

Response format

{
  "object": "list",
  "data": [
    {
      "id": "qwen2-vl-7b-instruct",
      "object": "model",
      "type": "vlm",
      "publisher": "mlx-community",
      "arch": "qwen2_vl",
      "compatibility_type": "mlx",
      "quantization": "4bit",
      "state": "not-loaded",
      "max_context_length": 32768
    },
    {
      "id": "meta-llama-3.1-8b-instruct",
      "object": "model",
      "type": "llm",
      "publisher": "lmstudio-community",
      "arch": "llama",
      "compatibility_type": "gguf",
      "quantization": "Q4_K_M",
      "state": "not-loaded",
      "max_context_length": 131072
    },
    {
      "id": "text-embedding-nomic-embed-text-v1.5",
      "object": "model",
      "type": "embeddings",
      "publisher": "nomic-ai",
      "arch": "nomic-bert",
      "compatibility_type": "gguf",
      "quantization": "Q4_0",
      "state": "not-loaded",
      "max_context_length": 2048
    }
  ]
}

GET /api/v0/models/{model}

Get info about one specific model

Example request

curl -H "Authorization: Bearer $LM_API_TOKEN" http://localhost:1234/api/v0/models/qwen2-vl-7b-instruct

Response format

{
  "id": "qwen2-vl-7b-instruct",
  "object": "model",
  "type": "vlm",
  "publisher": "mlx-community",
  "arch": "qwen2_vl",
  "compatibility_type": "mlx",
  "quantization": "4bit",
  "state": "not-loaded",
  "max_context_length": 32768
}

POST /api/v0/chat/completions

Chat Completions API. You provide a messages array and receive the next assistant response in the chat.

Example request

curl http://localhost:1234/api/v0/chat/completions \
  -H "Authorization: Bearer $LM_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "granite-3.0-2b-instruct",
    "messages": [
      { "role": "system", "content": "Always answer in rhymes." },
      { "role": "user", "content": "Introduce yourself." }
    ],
    "temperature": 0.7,
    "max_tokens": -1,
    "stream": false
  }'

Response format

{
  "id": "chatcmpl-i3gkjwthhw96whukek9tz",
  "object": "chat.completion",
  "created": 1731990317,
  "model": "granite-3.0-2b-instruct",
  "choices": [
    {
      "index": 0,
      "logprobs": null,
      "finish_reason": "stop",
      "message": {
        "role": "assistant",
        "content": "Greetings, I'm a helpful AI, here to assist,\nIn providing answers, with no distress.\nI'll keep it short and sweet, in rhyme you'll find,\nA friendly companion, all day long you'll bind."
      }
    }
  ],
  "usage": {
    "prompt_tokens": 24,
    "completion_tokens": 53,
    "total_tokens": 77
  },
  "stats": {
    "tokens_per_second": 51.43709529007664,
    "time_to_first_token": 0.111,
    "generation_time": 0.954,
    "stop_reason": "eosFound"
  },
  "model_info": {
    "arch": "granite",
    "quant": "Q4_K_M",
    "format": "gguf",
    "context_length": 4096
  },
  "runtime": {
    "name": "llama.cpp-mac-arm64-apple-metal-advsimd",
    "version": "1.3.0",
    "supported_formats": ["gguf"]
  }
}

POST /api/v0/completions

Text Completions API. You provide a prompt and receive a completion.

Example request

curl http://localhost:1234/api/v0/completions \
  -H "Authorization: Bearer $LM_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "granite-3.0-2b-instruct",
    "prompt": "the meaning of life is",
    "temperature": 0.7,
    "max_tokens": 10,
    "stream": false,
    "stop": "\n"
  }'

Response format

{
  "id": "cmpl-p9rtxv6fky2v9k8jrd8cc",
  "object": "text_completion",
  "created": 1731990488,
  "model": "granite-3.0-2b-instruct",
  "choices": [
    {
      "index": 0,
      "text": " to find your purpose, and once you have",
      "logprobs": null,
      "finish_reason": "length"
    }
  ],
  "usage": {
    "prompt_tokens": 5,
    "completion_tokens": 9,
    "total_tokens": 14
  },
  "stats": {
    "tokens_per_second": 57.69230769230769,
    "time_to_first_token": 0.299,
    "generation_time": 0.156,
    "stop_reason": "maxPredictedTokensReached"
  },
  "model_info": {
    "arch": "granite",
    "quant": "Q4_K_M",
    "format": "gguf",
    "context_length": 4096
  },
  "runtime": {
    "name": "llama.cpp-mac-arm64-apple-metal-advsimd",
    "version": "1.3.0",
    "supported_formats": ["gguf"]
  }
}

POST /api/v0/embeddings

Text Embeddings API. You provide a text and a representation of the text as an embedding vector is returned.

Example request

curl http://localhost:1234/api/v0/embeddings \
  -H "Authorization: Bearer $LM_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-nomic-embed-text-v1.5",
    "input": "Some text to embed"
  }

Example response

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [
        -0.016731496900320053,
        0.028460891917347908,
        -0.1407836228609085,
        ... (truncated for brevity) ...,
        0.02505224384367466,
        -0.0037634256295859814,
        -0.04341062530875206
      ],
      "index": 0
    }
  ],
  "model": "text-embedding-nomic-embed-text-v1.5@q4_k_m",
  "usage": {
    "prompt_tokens": 0,
    "total_tokens": 0
  }
}

Please report bugs by opening an issue on Github.

Get download statusGET

Get the status of model downloads

OpenAI Compatibility Endpoints

Send requests to Responses, Chat Completions (text and images), Completions, and Embeddings endpoints.

On this page

Requires LM Studio 0.3.6 or newer.Supported API EndpointsStart the REST API serverEndpointsGET /api/v0/modelsGET /api/v0/models/{model}POST /api/v0/chat/completionsPOST /api/v0/completionsPOST /api/v0/embeddings