Each LLM is given the same 1000 chess puzzles to solve. See puzzles.csv. Benchmarked on Mar 25, 2024.
| Model | Solved | Solved % | Illegal Moves | Illegal Moves % | Adjusted Elo |
|---|---|---|---|---|---|
| gpt-4-turbo-preview | 229 | 22.9% | 163 | 16.3% | 1144 |
| gpt-4 | 195 | 19.5% | 183 | 18.3% | 1047 |
| claude-3-opus-20240229 | 72 | 7.2% | 464 | 46.4% | 521 |
| claude-3-haiku-20240307 | 38 | 3.8% | 590 | 59.0% | 363 |
| claude-3-sonnet-20240229 | 23 | 2.3% | 663 | 66.3% | 286 |
| gpt-3.5-turbo | 23 | 2.3% | 683 | 68.3% | 269 |
| claude-instant-1.2 | 10 | 1.0% | 707 | 66.3% | 245 |
| mistral-large-latest | 4 | 0.4% | 813 | 81.3% | 149 |
| mixtral-8x7b | 9 | 0.9% | 832 | 83.2% | 136 |
| gemini-1.5-pro-latest* | FAIL | - | - | - | - |
Published by the CEO of Kagi!
I didn’t figure. I’m sure they could be taught to be much better, but normal computing can already play chess more or less perfectly. There really isn’t much any room left to be gained.