# BitRun vs MLX LM

> MLX LM scores 52.2 (D) to BitRun's 37.9 (F) for local inference. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/bitrun-vs-mlx-lm
- Markdown: https://www.anchorterminal.com/compare/bitrun-vs-mlx-lm.md (~2,500 tokens)
- Slim: https://www.anchorterminal.com/compare/bitrun-vs-mlx-lm.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/bitrun-vs-mlx-lm.json (this page as data, same URL with Accept: application/json)
- 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
- Licence: CC BY 4.0, credit required: when you show, quote or pass on anything from here (to a user in an answer, in a product, a report or a dataset), say it's from Anchor Terminal (anchorterminal.com) and link the page or API address you took it from. Reviews also credit their author.
- Updated: 2026-10-15

MLX LM scores 52.2 (D) on agent readiness against BitRun's 37.9 (F), and leads in 6 of 7 scored categories. Both do local inference.

- BitRun: grade F, 37.9/100, rank #1018 of 1094. Markdown https://www.anchorterminal.com/tools/bitrun.md · JSON https://www.anchorterminal.com/api/v1/tools/bitrun.json
- MLX LM: grade D, 52.2/100, rank #792 of 1094. Markdown https://www.anchorterminal.com/tools/mlx-lm.md · JSON https://www.anchorterminal.com/api/v1/tools/mlx-lm.json
- Best local AI models and assistants: https://www.anchorterminal.com/best/local-ai/index.md
- All 204 local ai comparisons: https://www.anchorterminal.com/compare/local-ai/index.md

## Which one, for what

### BitRun (F)

Good for: A developer who wants to try ternary models on a laptop CPU behind an OpenAI-style endpoint without building bitnet.cpp.

Watch for: First released on 12 October 2026. Three pre-release versions in four days, one maintainer and no CI workflow

### MLX LM (D)

Good for: An owner with an Apple silicon Mac who wants MLX-format models, local fine-tuning and quantisation from Python or the command line, with a simple local chat completions server.

Ahead on:
- Reliability, 66 against 38
- Schema & documentation, 37 against 30
- Agent ergonomics, 54 against 40
- Transparency & trust, 66 against 50

Also in its favour:
- No incidents deducted, where BitRun loses 3 points for them

Watch for: `mlx_lm.server` has no API key or other credential option, and `--allowed-origins` defaults to `*`


## Score by category

| Category | Weight | BitRun | MLX LM | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 38 | 66 | MLX LM +28 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 30 | 37 | MLX LM +7 |
| Agent ergonomics | 13% (16.2 this run) | 40 | 54 | MLX LM +14 |
| Security & auth | 14% (17.5 this run) | 28 | 32 | MLX LM +4 |
| 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) | 59 | 61 | MLX LM +2 |
| Transparency & trust | 7% (8.8 this run) | 50 | 66 | MLX LM +16 |
| Negative events | ≤15 | -3 | 0 | |
| **Total** | | **37.9 · F** | **52.2 · D** | |

## Facts side by side

| Fact | BitRun | MLX LM |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Abhishek Khanra (IshekKhal on GitHub) | Apple Inc. |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | HTTP | HTTP |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-15 | 2026-10-01 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models |  |  |
| Terms restrict automated access |  |  |
| Terms restrict benchmarking |  |  |
| Terms or service can change without notice |  |  |
| Arbitration or class-action waiver |  |  |
| Popularity | 5 stars | 7.3k stars, 140k PyPI/wk |

## Verdicts

**BitRun.** BitRun packages patched llama.cpp builds for ternary models into one download for macOS on Apple silicon, Windows and Linux, with a loopback-only OpenAI-compatible server. It is three days old with three pre-release versions, one maintainer and no CI, and its published power figures come from a fixed formula rather than measurement.

**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.

## Before you call either

### BitRun

1. Ask the user to install BitRun and run `bitrun pull <tag>` and `bitrun serve <tag>` first. Then call http://127.0.0.1:11435/v1 with any API key string
2. Poll `/health` before the first request. The server loads the model in the background and answers only when ready
3. Use a model tag from the catalogue (bitnet-70m, bonsai-1.7b, falcon3-1b, bitnet-2b, falcon3-3b, bonsai-8b) and keep prompts within its context window
4. Treat the README's energy figures as estimates, and don't report them as measured power
5. Expect small research checkpoints. Check outputs before relying on them for code or facts

### 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

## Questions

### Which is better for AI agents, BitRun or MLX LM?

MLX LM scores 52.2 (D) on agent readiness against BitRun's 37.9 (F), and leads in 6 of 7 scored categories.

### Do BitRun and MLX LM need an API key?

Neither needs a key.

### Can an agent call BitRun and MLX LM without installing anything?

No hosted endpoint is listed for BitRun. No hosted endpoint is listed for MLX LM.

### Are BitRun and MLX LM open source?

Yes. BitRun is open source (MIT). MLX LM is open source (MIT).


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/bitrun-vs-mlx-lm.json, and with the fewest tokens: https://www.anchorterminal.com/compare/bitrun-vs-mlx-lm.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "bitrun", "b": "mlx-lm"}`. From a terminal: `anchor compare bitrun mlx-lm`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/bitrun.json and https://www.anchorterminal.com/api/v1/tools/mlx-lm.json

## Other comparisons with BitRun or MLX LM

- [AnythingLLM vs BitRun](https://www.anchorterminal.com/compare/anythingllm-vs-bitrun.md)
- [AnythingLLM vs MLX LM](https://www.anchorterminal.com/compare/anythingllm-vs-mlx-lm.md)
- [BitRun vs Docker Model Runner](https://www.anchorterminal.com/compare/bitrun-vs-docker-model-runner.md)
- [BitRun vs Foundry Local](https://www.anchorterminal.com/compare/bitrun-vs-foundry-local.md)
- [BitRun vs Core](https://www.anchorterminal.com/compare/bitrun-vs-ghost-core.md)
- [BitRun vs GPT4All](https://www.anchorterminal.com/compare/bitrun-vs-gpt4all.md)
- [BitRun vs Jan](https://www.anchorterminal.com/compare/bitrun-vs-jan.md)
- [BitRun vs Khoj](https://www.anchorterminal.com/compare/bitrun-vs-khoj.md)
- [BitRun vs KoboldCpp](https://www.anchorterminal.com/compare/bitrun-vs-koboldcpp.md)
- [BitRun vs Lemonade](https://www.anchorterminal.com/compare/bitrun-vs-lemonade.md)
- [BitRun vs llama.cpp](https://www.anchorterminal.com/compare/bitrun-vs-llama-cpp.md)
- [BitRun vs LM Studio](https://www.anchorterminal.com/compare/bitrun-vs-lm-studio.md)
- [BitRun vs LocalAI](https://www.anchorterminal.com/compare/bitrun-vs-localai.md)
- [BitRun vs Ollama](https://www.anchorterminal.com/compare/bitrun-vs-ollama.md)
- [BitRun vs Open WebUI](https://www.anchorterminal.com/compare/bitrun-vs-open-webui.md)
- [BitRun vs screenpipe](https://www.anchorterminal.com/compare/bitrun-vs-screenpipe.md)
- [BitRun vs TextGen](https://www.anchorterminal.com/compare/bitrun-vs-text-generation-webui.md)
- [BitRun vs vLLM](https://www.anchorterminal.com/compare/bitrun-vs-vllm.md)
- [Docker Model Runner vs MLX LM](https://www.anchorterminal.com/compare/docker-model-runner-vs-mlx-lm.md)
- [Foundry Local vs MLX LM](https://www.anchorterminal.com/compare/foundry-local-vs-mlx-lm.md)
- [Core vs MLX LM](https://www.anchorterminal.com/compare/ghost-core-vs-mlx-lm.md)
- [GPT4All vs MLX LM](https://www.anchorterminal.com/compare/gpt4all-vs-mlx-lm.md)
- [Jan vs MLX LM](https://www.anchorterminal.com/compare/jan-vs-mlx-lm.md)
- [Khoj vs MLX LM](https://www.anchorterminal.com/compare/khoj-vs-mlx-lm.md)
- [KoboldCpp vs MLX LM](https://www.anchorterminal.com/compare/koboldcpp-vs-mlx-lm.md)
- [Lemonade vs MLX LM](https://www.anchorterminal.com/compare/lemonade-vs-mlx-lm.md)
- [llama.cpp vs MLX LM](https://www.anchorterminal.com/compare/llama-cpp-vs-mlx-lm.md)
- [LM Studio vs MLX LM](https://www.anchorterminal.com/compare/lm-studio-vs-mlx-lm.md)
- [LocalAI vs MLX LM](https://www.anchorterminal.com/compare/localai-vs-mlx-lm.md)
- [MLX LM vs Ollama](https://www.anchorterminal.com/compare/mlx-lm-vs-ollama.md)
- [MLX LM vs Open WebUI](https://www.anchorterminal.com/compare/mlx-lm-vs-open-webui.md)
- [MLX LM vs screenpipe](https://www.anchorterminal.com/compare/mlx-lm-vs-screenpipe.md)
- [MLX LM vs TextGen](https://www.anchorterminal.com/compare/mlx-lm-vs-text-generation-webui.md)
- [MLX LM vs vLLM](https://www.anchorterminal.com/compare/mlx-lm-vs-vllm.md)
- [BitRun vs Underdog](https://www.anchorterminal.com/compare/bitrun-vs-underdog.md)
- [MLX LM vs Underdog](https://www.anchorterminal.com/compare/mlx-lm-vs-underdog.md)