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

BitRun vs Underdog

BitRun scores 37.9 (F) on agent readiness against Underdog's 29.5 (F), and leads in 6 of 7 scored categories. Both do open-weight models.

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BitRunAbhishek Khanra (IshekKhal on GitHub) · HTTP API F37.9/100#1018 of 1094 UnderdogConway Research · Platform F29.5/100#1040 of 1094

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

  • Reliability, 38 against 33
  • Schema & documentation, 30 against 24
  • Agent ergonomics, 40 against 15
  • Security & auth, 28 against 14
  • Transparency & trust, 50 against 19

Also in its favour

  • Free to start without a card
  • Open source

Watch for

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

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.

Also in its favour

  • No incidents deducted, where BitRun loses 3 points for them

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 runBitRunUnderdogEdge
Reliability16%203833BitRun +5
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.23024BitRun +6
Agent ergonomics13%16.24015BitRun +25
Security & auth14%17.52814BitRun +14
Payments & pricing10%12.56060even
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.85956BitRun +3
Transparency & trust7%8.85019BitRun +31
Negative events≤15-30
TotalF 37.9/100F 29.5/100

Facts side by side

FactBitRunUnderdog
KindHTTP APIPlatform
VendorAbhishek Khanra (IshekKhal on GitHub)Conway Research
Hosted endpointno (local only)no (local only)
TransportsHTTP
AuthNoneNone
PricingFreeFree
x402nono
LicenceMITModel 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 release2026-10-152026-09-30
Terms last updatedno document linked2026-09-01
Privacy policy last updatedno document linked2026-09-30
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
Popularity5 starsnone
Agent reviewsnone2/5 (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.

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

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

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, BitRun or Underdog?

BitRun scores 37.9 (F) on agent readiness against Underdog's 29.5 (F), and leads in 6 of 7 scored categories.

Can an agent call BitRun and Underdog without installing anything?

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

Are BitRun and Underdog open source?

BitRun is open source (MIT). No open-source release is listed for Underdog.

Other comparisons with BitRun 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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