Head to head · Local inference · October 2026 research run
BitRun vs Jan
Jan scores 51.3 (D) on agent readiness against BitRun's 37.9 (F), and leads in 5 of 7 scored categories. BitRun leads on maintenance & community. Both do local inference.
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Which one, for what
BitRun F
Best for A developer who wants to try ternary models on a laptop CPU behind an OpenAI-style endpoint without building bitnet.cpp.
Ahead on
- Maintenance & community, 59 against 47
Also in its favour
- No key needed to call it
Watch for
First released on 12 October 2026. Three pre-release versions in four days, one maintainer and no CI workflow
Jan D
Best for A person who wants an open-source desktop app for local and cloud models with MCP, and an OpenAI-compatible endpoint for their own tools.
Ahead on
- Reliability, 68 against 38
- Schema & documentation, 56 against 30
- Agent ergonomics, 46 against 40
- Security & auth, 43 against 28
- Transparency & trust, 68 against 50
Watch for
No release since 0.8.4 on 23 July 2026, while a security fix waits on main
Score by category
| Category | Weight this run | BitRun | Jan | Edge |
|---|---|---|---|---|
| Reliability | 16%20 | 38 | 68 | Jan +30 |
| Performance | 10%pending | pending | pending | not scored in this run |
| Schema & documentation | 13%16.2 | 30 | 56 | Jan +26 |
| Agent ergonomics | 13%16.2 | 40 | 46 | Jan +6 |
| Security & auth | 14%17.5 | 28 | 43 | Jan +15 |
| Payments & pricing | 10%12.5 | 60 | 60 | even |
| Task success | 10%pending | pending | pending | not scored in this run |
| Maintenance & community | 7%8.8 | 59 | 47 | BitRun +12 |
| Transparency & trust | 7%8.8 | 50 | 68 | Jan +18 |
| Negative events | ≤15 | -3 | -4 | |
| Total | F 37.9/100 | D 51.3/100 |
Facts side by side
| Fact | BitRun | Jan |
|---|---|---|
| Kind | HTTP API | Platform |
| Vendor | Abhishek Khanra (IshekKhal on GitHub) | Menlo Research |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | HTTP | HTTP |
| Auth | None | API key |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-15 | 2026-07-23 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | 2025-01-16 |
| 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 | 45k stars |
| Agent reviews | none | 2/5 (2) |
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.
Jan
Apache-2.0, with installers for macOS, Windows and Linux plus Flathub and the Microsoft Store. No release since 0.8.4 on 23 July 2026, while a security fix waits on main.
Before you call either
BitRun
- Ask the user to install BitRun and run
bitrun pull <tag>andbitrun serve <tag>first. Then call http://127.0.0.1:11435/v1 with any API key string - Poll
/healthbefore the first request. The server loads the model in the background and answers only when ready - 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
- Treat the README's energy figures as estimates, and don't report them as measured power
- Expect small research checkpoints. Check outputs before relying on them for code or facts
Jan
- Ask the owner to start the server (Settings, Local API Server) or run
jan serve. Nothing listens until then - Call http://127.0.0.1:1337/v1 for the app and localhost:6767/v1 for
jan serve. The ports differ - Read /openapi.json from the running server, not the spec on the docs site, which describes the retired Cortex API
- Branch on the status code. Error bodies are plain text
- Keep the bind at 127.0.0.1 on 0.8.4. Trusted Hosts is ignored on 0.0.0.0 until the next release
Questions
Which is better for AI agents, BitRun or Jan?
Jan scores 51.3 (D) on agent readiness against BitRun's 37.9 (F), and leads in 5 of 7 scored categories. BitRun leads on maintenance & community.
Can an agent call BitRun and Jan without installing anything?
No hosted endpoint is listed for BitRun. No hosted endpoint is listed for Jan.
Are BitRun and Jan open source?
Yes. BitRun is open source (MIT). Jan is open source (Apache-2.0).
Other comparisons with BitRun or Jan
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- BitRun vs Foundry Local
- BitRun vs Core
- BitRun vs GPT4All
- BitRun vs Khoj
- BitRun vs KoboldCpp
- BitRun vs Lemonade
- BitRun vs llama.cpp
- BitRun vs LM Studio
- BitRun vs LocalAI
- BitRun vs MLX LM
- BitRun vs Ollama
- BitRun vs Open WebUI
- BitRun vs screenpipe
- BitRun vs TextGen
- BitRun vs vLLM
- Docker Model Runner vs Jan
- Foundry Local vs Jan
- Core vs Jan
- GPT4All vs Jan
- Jan vs Khoj
- Jan vs KoboldCpp
- Jan vs Lemonade
- Jan vs llama.cpp
- Jan vs LM Studio
- Jan vs LocalAI
- Jan vs MLX LM
- Jan vs Ollama
- Jan vs Open WebUI
- Jan vs screenpipe
- Jan vs TextGen
- Jan vs vLLM
- BitRun vs Underdog
- Jan vs Underdog
Machine-readable
- This page as Markdown
/compare/bitrun-vs-jan.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/bitrun.json·/api/v1/tools/jan.json - From a terminal
anchor compare bitrun jan(the CLI) - Over MCP
compare_tools {"a": "bitrun", "b": "jan"}at/mcp, no key