LM Studio vs Ollama

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.

Ollama

An OpenAPI 3.1 file for the 15 native operations and llms.txt with 68 links to Markdown pages. No credential on the local API, and any caller that reaches it can pull, push, create and delete models.

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

Ollama

1. Send "stream": false for one JSON body. The native routes stream NDJSON by default
2. Set OLLAMA_CONTEXT_LENGTH=64000 or options.num_ctx before agent work. The default is 4k below 24 GiB of VRAM
3. Back off on a 503. It means the queue (512 by default) is full
4. Put an authenticating proxy in front before binding past 127.0.0.1. The server checks no credential
5. Expect model names with a cloud tag to run on Ollama's servers. They need ollama signin and fail with OLLAMA_NO_CLOUD=1