# HyperQwen vs Underdog

> HyperQwen scores 56.9 (C) to Underdog's 30.1 (F) for open-weight models. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/hyperqwen-vs-underdog
- Markdown: https://www.anchorterminal.com/compare/hyperqwen-vs-underdog.md (~2,900 tokens)
- Slim: https://www.anchorterminal.com/compare/hyperqwen-vs-underdog.min.md (~730 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/hyperqwen-vs-underdog.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
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-30

HyperQwen scores 56.9 (C) on agent readiness against Underdog's 30.1 (F), and leads in 6 of 7 scored categories. Both do open-weight models.

- HyperQwen: grade C, 56.9/100, rank #738 of 1239. Markdown https://www.anchorterminal.com/tools/hyperqwen.md · JSON https://www.anchorterminal.com/api/v1/tools/hyperqwen.json
- Underdog: grade F, 30.1/100, rank #1163 of 1239. Markdown https://www.anchorterminal.com/tools/underdog.md · JSON https://www.anchorterminal.com/api/v1/tools/underdog.json
- Best local AI models and assistants: https://www.anchorterminal.com/best/local-ai/index.md
- All 253 local ai comparisons: https://www.anchorterminal.com/compare/local-ai/index.md

## Which one, for what

### HyperQwen (C)

Good 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:
- Reliability, 59 against 33
- Schema & documentation, 56 against 24
- Agent ergonomics, 65 against 15
- Security & auth, 41 against 14
- Maintenance & community, 68 against 56
- Transparency & trust, 55 against 26

Also in its favour:
- Free to start without a card
- Open source

Watch for: Serves Qwen3.8-27B and checkpoints derived from it only. Other models are on the roadmap

### Underdog (F)

Good for: An owner who wants a Mac assistant over their own mail and calendar that, per Conway, keeps everything on the machine.

Watch for: No API, MCP server, CLI or SDK of Conway's for agents, and the `husky serve` command on the husky-flash card comes from a repository that isn't public


## Score by category

| Category | Weight | HyperQwen | Underdog | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 59 | 33 | HyperQwen +26 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 56 | 24 | HyperQwen +32 |
| Agent ergonomics | 13% (16.2 this run) | 65 | 15 | HyperQwen +50 |
| Security & auth | 14% (17.5 this run) | 41 | 14 | HyperQwen +27 |
| 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) | 68 | 56 | HyperQwen +12 |
| Transparency & trust | 7% (8.8 this run) | 55 | 26 | HyperQwen +29 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **56.9 · C** | **30.1 · F** | |

## Facts side by side

| Fact | HyperQwen | Underdog |
| --- | --- | --- |
| Kind | HTTP API | Platform |
| Vendor | syv.ai (syv-ai on GitHub) | Conway Research |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | HTTP |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Model weights Apache-2.0 on Hugging Face (Underdog 27B 1.0 and its ternary build, Woof 4B and 2B 1.1, Bark 0.8B 1.0); woof-1.0-4B carries the Apache-2.0 tag without a licence file; husky-flash is marked other and its card says Woof's licence applies; the app's source isn't published and its licence is on underdog.ai, unchecked |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | none | 2026-09-30 |
| Terms last updated | no document linked | 2026-09-01 |
| Privacy policy last updated | no date given | 2026-09-30 |
| Customer content may train models |  | not found in the text |
| Terms restrict automated access |  | not found in the text |
| Terms restrict benchmarking |  | not found in the text |
| Terms or service can change without notice |  | not found in the text |
| Arbitration or class-action waiver |  | not found in the text |
| Popularity | 2.4k stars | none |
| 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.

**Underdog.** Apache-2.0 weights on Hugging Face for Underdog 27B 1.0, Woof 4B and 2B 1.1 and Bark 0.8B 1.0, with no gate. No API, MCP server, CLI or SDK of Conway's for agents, and the `husky serve` command on the husky-flash card comes from a repository that isn't public.

## Before you call either

### HyperQwen

1. Call http://127.0.0.1:18020/v1 with model `qwen3.8-27b` and the `VLLM_API_KEY` value, or any string if no key was set
2. Poll `/health` before the first request. A first start downloads and prepares the model and compiles kernels, which can take over 30 minutes
3. Declare the context window your setup uses (65,536 by default, 131,072 for `CTX=long`, 245,760 for `CTX=huge`) so the client doesn't truncate
4. Ask the owner to set `VLLM_API_KEY` before setting `BIND=0.0.0.0`, and to front the port with a proxy that allowlists routes
5. Treat the README's tokens-per-second figures as the project's measurements on one RTX 3090 at 250 W

### Underdog

1. Don't look for an agent interface to Underdog. We found no API, MCP server, CLI or SDK, and underdog.ai, where one would be documented, refuses our reader
2. Don't count on `husky serve`. The husky-flash card names it, but the Greyhound repository it comes from isn't public and no port or protocol is documented
3. Run `splash serve --model ConwayResearch/Underdog-27B-1.0 --default-reasoning-effort medium` with Inco AI's Splash 1.1.0 or later for an OpenAI-compatible endpoint on `127.0.0.1:8000`, and pass `--api-key`, since Splash starts without authentication
4. Pin a Hugging Face revision. Woof 4B went from 1.0 to 1.1 in eight days with no note of what changed
5. Check Woof 4B and 2B 1.1 files against the SHA-256 values in release-provenance.json before loading them

## Questions

### Which is better for AI agents, HyperQwen or Underdog?

HyperQwen scores 56.9 (C) on agent readiness against Underdog's 30.1 (F), and leads in 6 of 7 scored categories.

### Can an agent call HyperQwen and Underdog without installing anything?

No hosted endpoint is listed for HyperQwen. No hosted endpoint is listed for Underdog.

### Are HyperQwen and Underdog open source?

HyperQwen is open source (Apache-2.0). No open-source release is listed for Underdog.


## For agents

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

## Other comparisons with HyperQwen or Underdog

- [AnythingLLM vs HyperQwen](https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen.md)
- [Berth vs HyperQwen](https://www.anchorterminal.com/compare/berth-vs-hyperqwen.md)
- [BitRun vs HyperQwen](https://www.anchorterminal.com/compare/bitrun-vs-hyperqwen.md)
- [Docker Model Runner vs HyperQwen](https://www.anchorterminal.com/compare/docker-model-runner-vs-hyperqwen.md)
- [Foundry Local vs HyperQwen](https://www.anchorterminal.com/compare/foundry-local-vs-hyperqwen.md)
- [Core vs HyperQwen](https://www.anchorterminal.com/compare/ghost-core-vs-hyperqwen.md)
- [GPT4All vs HyperQwen](https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen.md)
- [HyperQwen vs Jan](https://www.anchorterminal.com/compare/hyperqwen-vs-jan.md)
- [HyperQwen vs Khoj](https://www.anchorterminal.com/compare/hyperqwen-vs-khoj.md)
- [HyperQwen vs KoboldCpp](https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp.md)
- [HyperQwen vs Lemonade](https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade.md)
- [HyperQwen vs llama.cpp](https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp.md)
- [HyperQwen vs LM Studio](https://www.anchorterminal.com/compare/hyperqwen-vs-lm-studio.md)
- [HyperQwen vs LocalAI](https://www.anchorterminal.com/compare/hyperqwen-vs-localai.md)
- [HyperQwen vs MLX LM](https://www.anchorterminal.com/compare/hyperqwen-vs-mlx-lm.md)
- [HyperQwen vs Ollama](https://www.anchorterminal.com/compare/hyperqwen-vs-ollama.md)
- [HyperQwen vs Open WebUI](https://www.anchorterminal.com/compare/hyperqwen-vs-open-webui.md)
- [HyperQwen vs screenpipe](https://www.anchorterminal.com/compare/hyperqwen-vs-screenpipe.md)
- [HyperQwen vs TextGen](https://www.anchorterminal.com/compare/hyperqwen-vs-text-generation-webui.md)
- [HyperQwen vs vLLM](https://www.anchorterminal.com/compare/hyperqwen-vs-vllm.md)
- [screenpipe vs Underdog](https://www.anchorterminal.com/compare/screenpipe-vs-underdog.md)
- [AnythingLLM vs Underdog](https://www.anchorterminal.com/compare/anythingllm-vs-underdog.md)
- [BitRun vs Underdog](https://www.anchorterminal.com/compare/bitrun-vs-underdog.md)
- [Docker Model Runner vs Underdog](https://www.anchorterminal.com/compare/docker-model-runner-vs-underdog.md)
- [Foundry Local vs Underdog](https://www.anchorterminal.com/compare/foundry-local-vs-underdog.md)
- [Core vs Underdog](https://www.anchorterminal.com/compare/ghost-core-vs-underdog.md)
- [GPT4All vs Underdog](https://www.anchorterminal.com/compare/gpt4all-vs-underdog.md)
- [Jan vs Underdog](https://www.anchorterminal.com/compare/jan-vs-underdog.md)
- [KoboldCpp vs Underdog](https://www.anchorterminal.com/compare/koboldcpp-vs-underdog.md)
- [Lemonade vs Underdog](https://www.anchorterminal.com/compare/lemonade-vs-underdog.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)
- [LocalAI vs Underdog](https://www.anchorterminal.com/compare/localai-vs-underdog.md)
- [MLX LM vs Underdog](https://www.anchorterminal.com/compare/mlx-lm-vs-underdog.md)
- [Nemotron 3.5 Super VL vs Underdog](https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-underdog.md)
- [Ollama vs Underdog](https://www.anchorterminal.com/compare/ollama-vs-underdog.md)
- [TextGen vs Underdog](https://www.anchorterminal.com/compare/text-generation-webui-vs-underdog.md)
- [Underdog vs vLLM](https://www.anchorterminal.com/compare/underdog-vs-vllm.md)

## Disclosure

- Underdog competes with LocalGhost, which Anchor Terminal's founder builds. It's graded by the same published checklist as every listing, neither stricter nor looser. Two research agents graded it independently, and a third reconciled them item by item, checking the evidence itself wherever they disagreed instead of keeping either award by default. underdog.ai refuses our reader, so what we couldn't read there is marked unchecked, not missing, and the text of its pricing page was supplied to us by Anchor Terminal's founder.

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