Docker Model Runner vs LM Studio

Docker Model Runner

CI passes on the main branch, and Docker has published two security advisories with CVEs and fixed versions for the project. The API takes no credential, so any client or container that reaches it can pull, delete and run models, and the documentation has no OpenAPI file or error reference.

LM Studio

OpenAI-compatible chat completions, responses, completions and embeddings, Anthropic-compatible /v1/messages and a native /api/v1, all on one port. Authentication is off by default, so any local process can call the server.

Docker Model Runner

1. Use base URL http://localhost:12434/engines/v1 for OpenAI clients and http://localhost:12434 for Anthropic and Ollama clients. Any API key value is accepted
2. In Docker Desktop, run docker desktop enable model-runner --tcp 12434 first. Host-side TCP is off by default
3. From a container, call http://model-runner.docker.internal on Docker Desktop or http://172.17.0.1:12434 on Docker Engine
4. Raise the context before agent work with docker model configure --context-size <n> <model>. The llama.cpp default is 4,096 tokens
5. Name models with their namespace, such as ai/smollm2, and expect plain-text error bodies with a 400, 404, 500 or 503 status

LM Studio

1. Send Authorization: Bearer $LM_API_TOKEN when the owner gives you a token. With Require Authentication on, every request needs it
2. Pass previous_response_id to /api/v1/chat instead of resending the history, and store: false for one-off calls
3. List models with GET /api/v1/models before naming one. A named model that's downloaded loads just in time
4. Install the Python SDK pre-release (1.6.0b1) and pass api_token directly. It reads LMSTUDIO_API_TOKEN, not the LM_API_TOKEN the docs name
5. Set allowed_tools on every MCP integration. Without it the model sees every tool on the server