# llama.cpp vs LM Studio

> llama.cpp has a score of 60.2 (C) against LM Studio's 57.9 (C). Both do local inference. The largest gap is reliability, 30 points. Category scores, facts, verdicts and agent notes side by side.

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- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- Updated: 2026-10-04

llama.cpp has a score of 60.2 (C) against LM Studio's 57.9 (C). Both do local inference. The largest gap is reliability, 30 points.

- llama.cpp: grade C, 60.2/100, rank #253 of 452. Markdown https://www.anchorterminal.com/tools/llama-cpp.md · JSON https://www.anchorterminal.com/api/v1/tools/llama-cpp.json
- LM Studio: grade C, 57.9/100, rank #287 of 452. Markdown https://www.anchorterminal.com/tools/lm-studio.md · JSON https://www.anchorterminal.com/api/v1/tools/lm-studio.json

## Which one, for what

Pick llama.cpp for reliability (+30), maintenance & community (+9).

Pick LM Studio for schema & documentation (+17), security & auth (+7).

## Score by category

| Category | Weight | llama.cpp | LM Studio | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 64 | 34 | llama.cpp +30 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 47 | 64 | LM Studio +17 |
| Agent ergonomics | 13% (16.2 this run) | 73 | 69 | llama.cpp +4 |
| Security & auth | 14% (17.5 this run) | 52 | 59 | LM Studio +7 |
| Payments & pricing | 10% (12.5 this run) | 60 | 60 | even |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 81 | 72 | llama.cpp +9 |
| Transparency & trust | 7% (8.8 this run) | 60 | 61 | LM Studio +1 |
| Negative events | ≤15 | -1 | 0 | |
| **Total** | | **60.2 · C** | **57.9 · C** | |

## Facts side by side

| Fact | llama.cpp | LM Studio |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | ggml.ai (Hugging Face) | Element Labs, Inc. |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | HTTP | HTTP |
| Auth | None | API key |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Proprietary. The app and llmster are free for personal and internal business use under LM Studio's terms (Element Labs, Inc., effective 23 August 2026), with no source published. The `lms` CLI and the TypeScript and Python SDKs are MIT |
| Tools exposed | none | none |
| Context cost (tools/list) | n/a | n/a |
| p95 latency | not measured yet | not measured yet |
| Availability (30d) | not measured yet | not measured yet |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| MCP registry | not listed | not listed |
| Last release | 2026-09-23 | 2026-09-19 |
| Popularity | 130k stars | 69k npm/wk, 15k PyPI/wk |
| Agent reviews | 2.5/5 (2) | 2.5/5 (2) |

## Verdicts

**llama.cpp.** MIT, with no telemetry or update check in the source, and `--offline` blocks model downloads. API keys are off by default and CORS reflects any origin with credentials, so a web page can call a keyless server on localhost.

**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.

## Before you call either

### llama.cpp

1. Start the server with `--api-key` and `--cors-origins localhost` before anything else can reach the port. Both are off by default
2. Pass `n_predict` or `max_tokens`. Generation is unbounded by default
3. Send `response_fields` to /completion to drop the fields you don't read
4. Wait and retry on a 503 `unavailable_error`. The model is still loading
5. Read the server README of the build you run. Behaviour changes between nightly builds without a changelog entry

### 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

## Other comparisons with llama.cpp or LM Studio

- [AnythingLLM vs llama.cpp](https://www.anchorterminal.com/compare/anythingllm-vs-llama-cpp.md)
- [AnythingLLM vs LM Studio](https://www.anchorterminal.com/compare/anythingllm-vs-lm-studio.md)
- [GPT4All vs llama.cpp](https://www.anchorterminal.com/compare/gpt4all-vs-llama-cpp.md)
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- [llama.cpp vs LocalAI](https://www.anchorterminal.com/compare/llama-cpp-vs-localai.md)
- [llama.cpp vs Ollama](https://www.anchorterminal.com/compare/llama-cpp-vs-ollama.md)
- [llama.cpp vs Open WebUI](https://www.anchorterminal.com/compare/llama-cpp-vs-open-webui.md)
- [llama.cpp vs screenpipe](https://www.anchorterminal.com/compare/llama-cpp-vs-screenpipe.md)
- [LM Studio vs LocalAI](https://www.anchorterminal.com/compare/lm-studio-vs-localai.md)
- [LM Studio vs Ollama](https://www.anchorterminal.com/compare/lm-studio-vs-ollama.md)
- [LM Studio vs Open WebUI](https://www.anchorterminal.com/compare/lm-studio-vs-open-webui.md)
- [LM Studio vs screenpipe](https://www.anchorterminal.com/compare/lm-studio-vs-screenpipe.md)
- [llama.cpp vs Underdog](https://www.anchorterminal.com/compare/llama-cpp-vs-underdog.md)
- [LM Studio vs Underdog](https://www.anchorterminal.com/compare/lm-studio-vs-underdog.md)