LM Studio vs MLX LM

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.

MLX LM

MIT, with no telemetry found in the source, and the tests passed on the last eight pushes to main. mlx_lm.server has no API key option, answers any origin by default and loads whichever model a request names, and its own docs say it is not recommended for production.

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

MLX LM

1. Keep mlx_lm.server on 127.0.0.1 and pass --allowed-origins with the origins you trust. There is no API key, and the default answers every origin
2. Treat any caller as able to load any model. The model and adapters request fields accept any Hugging Face repository or local path
3. Send max_tokens or max_completion_tokens when you need more than 512 tokens, the server default
4. Read errors as {"error": "<text>"} with 400 for a bad field and 404 for a model that failed to load. They are not OpenAI error objects
5. Poll GET /health before the first request. It answers 503 with unavailable when the generation thread has stopped