I build SpaceApp, a self-hosted workspace for running AI coding CLIs side by side. On 18 October I let six models play chess against each other there, each through its own CLI. Nobody won by checkmate. The way they lost is the interesting part.
| # | Model | Points | W / D / L | Elo | Avg per move | Forfeits |
|---|---|---|---|---|---|---|
| 1 | Opus 5.5 (xhigh) | 3.5 | 3 / 1 / 0 | 1535 | 13 s | 0 |
| 2 | Gemini 4 Argon (High) | 3.5 | 3 / 1 / 0 | 1535 | 23 s | 0 |
| 3 | GPT-6-Astra (xhigh) | 2 | 2 / 0 / 2 | 1500 | 37 s | 2 |
| 4 | Fable 5.1 (xhigh) | 1 | 1 / 0 / 3 | 1478 | 26 s | 3 |
| 5 | Gemini 3.8 Flash (High) | 1 | 1 / 0 / 3 | 1477 | 40 s | 3 |
| 6 | GPT-6.1-Sol (xhigh) | 1 | 1 / 0 / 3 | 1475 | 34 s | 3 |
Twelve games are a small sample, and a tournament decided by forfeits says very little about chess strength. What it does show is how reliably a model answers through its CLI when every answer has a deadline: no timeouts, no provider errors, no illegal moves, game after game. That is the property I care about most when agents run unattended for hours, and it is not the same ranking you get from a single-prompt leaderboard.
SpaceApp runs on Linux, macOS and Windows 11 with Node.js 20.11 or newer:
npx --yes run-spaceapp@latest install
It is source-available under the Business Source License 1.1. Code, docs and known limitations: https://github.com/oll4com/spaceapp
If you have run something similar, I would like to know which models held up for you, and which rules you would change for a fairer second run.