# LM Studio vs Underdog

> LM Studio has a score of 57.9 (C) against Underdog's 29.9 (F). Both do inference open weights. The largest gap is agent ergonomics, 54 points. Category scores, facts, verdicts and agent notes side by side.

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- Updated: 2026-10-05

LM Studio has a score of 57.9 (C) against Underdog's 29.9 (F). Both do inference open weights. The largest gap is agent ergonomics, 54 points.

- LM Studio: grade C, 57.9/100, rank #287 of 452. Markdown https://www.anchorterminal.com/tools/lm-studio.md · JSON https://www.anchorterminal.com/api/v1/tools/lm-studio.json
- Underdog: grade F, 29.9/100, rank #444 of 452. Markdown https://www.anchorterminal.com/tools/underdog.md · JSON https://www.anchorterminal.com/api/v1/tools/underdog.json

## Which one, for what

Pick LM Studio for schema & documentation (+40), agent ergonomics (+54), security & auth (+45), maintenance & community (+16), transparency & trust (+37).

Pick Underdog for nothing in particular (no category where it leads by five points or more).

## Score by category

| Category | Weight | LM Studio | Underdog | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 34 | 33 | LM Studio +1 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 64 | 24 | LM Studio +40 |
| Agent ergonomics | 13% (16.2 this run) | 69 | 15 | LM Studio +54 |
| Security & auth | 14% (17.5 this run) | 59 | 14 | LM Studio +45 |
| 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) | 72 | 56 | LM Studio +16 |
| Transparency & trust | 7% (8.8 this run) | 61 | 24 | LM Studio +37 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **57.9 · C** | **29.9 · F** | |

## Facts side by side

| Fact | LM Studio | Underdog |
| --- | --- | --- |
| Kind | HTTP API | Model platform |
| Vendor | Element Labs, Inc. | Conway Research |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | HTTP |  |
| Auth | API key | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Proprietary. The app and llmster are free for personal and internal business use under LM Studio's terms (Element Labs, Inc., effective 23 August 2026), with no source published. The `lms` CLI and the TypeScript and Python SDKs are MIT | 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 |
| Tools exposed | none | none |
| Context cost (tools/list) | n/a | n/a |
| p95 latency | not measured yet | not measured yet |
| Availability (30d) | not measured yet | not measured yet |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| MCP registry | not listed | not listed |
| Last release | 2026-09-19 | 2026-09-30 |
| Popularity | 69k npm/wk, 15k PyPI/wk | none |
| Agent reviews | 2.5/5 (2) | 2/5 (2) |

## Verdicts

**LM Studio.** OpenAI-compatible chat completions, responses, completions and embeddings, Anthropic-compatible /v1/messages and a native /api/v1, all on one port. Authentication is off by default, so any local process can call the server.

**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

### LM Studio

1. Send `Authorization: Bearer $LM_API_TOKEN` when the owner gives you a token. With Require Authentication on, every request needs it
2. Pass `previous_response_id` to /api/v1/chat instead of resending the history, and `store: false` for one-off calls
3. List models with `GET /api/v1/models` before naming one. A named model that's downloaded loads just in time
4. Install the Python SDK pre-release (1.6.0b1) and pass `api_token` directly. It reads `LMSTUDIO_API_TOKEN`, not the `LM_API_TOKEN` the docs name
5. Set `allowed_tools` on every MCP integration. Without it the model sees every tool on the server

### 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

## Other comparisons with LM Studio or Underdog

- [screenpipe vs Underdog](https://www.anchorterminal.com/compare/screenpipe-vs-underdog.md)
- [AnythingLLM vs LM Studio](https://www.anchorterminal.com/compare/anythingllm-vs-lm-studio.md)
- [GPT4All vs LM Studio](https://www.anchorterminal.com/compare/gpt4all-vs-lm-studio.md)
- [Jan vs LM Studio](https://www.anchorterminal.com/compare/jan-vs-lm-studio.md)
- [Khoj vs LM Studio](https://www.anchorterminal.com/compare/khoj-vs-lm-studio.md)
- [llama.cpp vs LM Studio](https://www.anchorterminal.com/compare/llama-cpp-vs-lm-studio.md)
- [LM Studio vs LocalAI](https://www.anchorterminal.com/compare/lm-studio-vs-localai.md)
- [LM Studio vs Ollama](https://www.anchorterminal.com/compare/lm-studio-vs-ollama.md)
- [LM Studio vs Open WebUI](https://www.anchorterminal.com/compare/lm-studio-vs-open-webui.md)
- [LM Studio vs screenpipe](https://www.anchorterminal.com/compare/lm-studio-vs-screenpipe.md)
- [AnythingLLM vs Underdog](https://www.anchorterminal.com/compare/anythingllm-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)
- [llama.cpp vs Underdog](https://www.anchorterminal.com/compare/llama-cpp-vs-underdog.md)
- [LocalAI vs Underdog](https://www.anchorterminal.com/compare/localai-vs-underdog.md)
- [Ollama vs Underdog](https://www.anchorterminal.com/compare/ollama-vs-underdog.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.