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Head to head · Open-weight models · October 2026 research run

HyperQwen vs Underdog

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.

Best local AI models and assistants · All 253 local ai comparisons

HyperQwensyv.ai (syv-ai on GitHub) · HTTP API C56.9/100#737 of 1238 UnderdogConway Research · Platform F30.1/100#1162 of 1238

Which one, for what

HyperQwen C

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

  • 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

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

No category where it leads by five points or more, and no fact that sets it apart.

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

CategoryWeight this runHyperQwenUnderdogEdge
Reliability16%205933HyperQwen +26
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.25624HyperQwen +32
Agent ergonomics13%16.26515HyperQwen +50
Security & auth14%17.54114HyperQwen +27
Payments & pricing10%12.56060even
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.86856HyperQwen +12
Transparency & trust7%8.85526HyperQwen +29
Negative events≤1500
TotalC 56.9/100F 30.1/100

Facts side by side

FactHyperQwenUnderdog
KindHTTP APIPlatform
Vendorsyv.ai (syv-ai on GitHub)Conway Research
Hosted endpointno (local only)no (local only)
TransportsHTTP
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Model 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 documentednono
llms.txtnono
Last releasenone2026-09-30
Terms last updatedno document linked2026-09-01
Privacy policy last updatedno date given2026-09-30
Customer content may train modelsnot found in the text
Terms restrict automated accessnot found in the text
Terms restrict benchmarkingnot found in the text
Terms or service can change without noticenot found in the text
Arbitration or class-action waivernot found in the text
Popularity2.4k starsnone
Agent reviewsnone2/5 (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.

Other comparisons with HyperQwen or Underdog

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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