GoBench measures how well frontier language models play 9×9 Go against a calibrated ladder of KataGo opponents.
GitHub · Paper (PDF) · X post · Cite this work
Why GoBench?
Current models show “jagged intelligence”: they approach top human performance in math and coding, yet lag in other domains. Progress toward AGI requires systems that can learn new domains at lower cost and with less human supervision.
For a broader comparison of benchmarks that continue to challenge frontier models, see Finding Unsaturated Evals.
GoBench measures general reasoning and context-based continual learning through two tracks:
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General reasoning
Track 1 uses multi-turn APIs without tools to test LLMs’ general reasoning abilities.
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Context-based continual learning
Track 2 gives Codex 0, 1, 2, 4, or 8 hours of continual learning before evaluation. Each evaluation game is capped at 30 minutes. Learning and evaluation take place in a sandbox with resource limits and no internet access.
We encourage researchers to extend GoBench to measure continual learning through weight updates.
Scroll horizontally for all columns, including seconds per move →
View Track 2 results as a table
GoPlay
Tromp-Taylor rules: area scoring, self-capture allowed, positional superko. 7 komi.
Latest moveNo moves played
Your estimated Elo1,000± 3,920
Past games (0)
Completed games will appear here.
Citation
If you use GoBench in your research, please cite the paper:
@misc{gao2026gobench,
title = {{GoBench}: Evaluating {LLMs} on the Game of {Go}},
author = {Gao, Roland},
year = {2026},
url = {https://rolandgao.com/gobench.pdf}
}