Head to head · Local inference · October 2026 research run
HyperQwen vs Jan
HyperQwen scores 56.9 (C) on agent readiness against Jan's 51.4 (D), and leads in 2 of 7 scored categories. Jan leads on reliability and transparency & trust. Both do local inference.
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Which one, for what
Best for An operator with one 24 GB NVIDIA card who wants Qwen3.8-27B behind an OpenAI-compatible endpoint for a coding or tool-calling agent, with long context.
Ahead on
- Agent ergonomics, 65 against 46
- Maintenance & community, 68 against 47
Also in its favour
- No key needed to call it
- No incidents deducted, where Jan loses 4 points for them
Watch for
Serves Qwen3.8-27B and checkpoints derived from it only. Other models are on the roadmap
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 59
- Transparency & trust, 70 against 55
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 | HyperQwen | Jan | Edge |
|---|---|---|---|---|
| Reliability | 16%20 | 59 | 68 | Jan +9 |
| Performance | 10%pending | pending | pending | not scored in this run |
| Schema & documentation | 13%16.2 | 56 | 56 | even |
| Agent ergonomics | 13%16.2 | 65 | 46 | HyperQwen +19 |
| Security & auth | 14%17.5 | 41 | 43 | Jan +2 |
| Payments & pricing | 10%12.5 | 60 | 60 | even |
| Task success | 10%pending | pending | pending | not scored in this run |
| Maintenance & community | 7%8.8 | 68 | 47 | HyperQwen +21 |
| Transparency & trust | 7%8.8 | 55 | 70 | Jan +15 |
| Negative events | ≤15 | 0 | -4 | |
| Total | C 56.9/100 | D 51.4/100 |
Facts side by side
| Fact | HyperQwen | Jan |
|---|---|---|
| Kind | HTTP API | Platform |
| Vendor | syv.ai (syv-ai 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 | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | none | 2026-07-23 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no date given | 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 | 2.4k stars | 45k stars |
| Agent reviews | none | 2/5 (2) |
Verdicts
HyperQwen
HyperQwen packages a patched vLLM 0.32.0 and requantised Qwen3.8-27B weights into one container with tool calling preset and a loopback bind when no key is set. It runs one model family, its speed figures come from one RTX 3090, and it has no versioned releases, changelog or security policy.
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
HyperQwen
- Call http://127.0.0.1:18020/v1 with model
qwen3.8-27band theVLLM_API_KEYvalue, or any string if no key was set - Poll
/healthbefore the first request. A first start downloads and prepares the model and compiles kernels, which can take over 30 minutes - Declare the context window your setup uses (65,536 by default, 131,072 for
CTX=long, 245,760 forCTX=huge) so the client doesn't truncate - Ask the owner to set
VLLM_API_KEYbefore settingBIND=0.0.0.0, and to front the port with a proxy that allowlists routes - Treat the README's tokens-per-second figures as the project's measurements on one RTX 3090 at 250 W
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, HyperQwen or Jan?
HyperQwen scores 56.9 (C) on agent readiness against Jan's 51.4 (D), and leads in 2 of 7 scored categories. Jan leads on reliability and transparency & trust.
Can an agent call HyperQwen and Jan without installing anything?
No hosted endpoint is listed for HyperQwen. No hosted endpoint is listed for Jan.
Are HyperQwen and Jan open source?
Yes. HyperQwen is open source (Apache-2.0). Jan is open source (Apache-2.0).
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- HyperQwen vs Khoj
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- HyperQwen vs llama.cpp
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- HyperQwen vs LocalAI
- HyperQwen vs MLX LM
- HyperQwen vs Ollama
- HyperQwen vs Open WebUI
- HyperQwen vs screenpipe
- HyperQwen vs TextGen
- HyperQwen vs vLLM
- 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
- HyperQwen vs Underdog
- Jan vs Underdog
Machine-readable
- This page as Markdown
/compare/hyperqwen-vs-jan.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/hyperqwen.json·/api/v1/tools/jan.json - From a terminal
anchor compare hyperqwen jan(the CLI) - Over MCP
compare_tools {"a": "hyperqwen", "b": "jan"}at/mcp, no key