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Chess Puzzles: AI model results
Chess Puzzles is a Reasoning benchmark run by Epoch AI. 81 models in our data have a published result.
It is part of the SI-IQ index (loading k = 1.08, chance level 5%).
- Evaluator
- Epoch AI
- Benchmark home page
- epoch.ai/benchmarks/chess-puzzles
Results
| 1 | GPT-6 Astra OpenAI | 74.0% | 150146–153 | 8 |
| 2 | GPT-6.1 Sol OpenAI | 68.0% | 148144–152 | 8 |
| 3 | Claude Opus 5.5 Anthropic | 67.0% | 144141–148 | 8 |
| 4 | Gemini 3.7 Flash Google DeepMind | 66.0% | 131128–134 | 8 |
| 5 | GPT-5.5 OpenAI | 63.0% | 138135–141 | 9 |
| 6 | GPT-5.5 Pro OpenAI | 62.0% | 139135–142 | 5 too little data |
| 7 | GPT-5.2 Pro OpenAI | 60.0% | 132128–135 | 3 too little data |
| 8 | Claude Sonnet 5.5 Anthropic | 59.0% | 140137–143 | 8 |
| 9 | GPT-5.4 Pro OpenAI | 59.0% | 135131–138 | 5 too little data |
| 10 | GPT-5.6 Sol OpenAI | 59.0% | 140137–143 | 8 |
| 11 | Gemini 4 Argon Google DeepMind | 59.0% | 140137–143 | 8 |
| 12 | Gemini 3.8 Flash Google DeepMind | 58.0% | 130127–133 | 8 |
| 13 | GPT-6 Sol OpenAI | 56.0% | 140137–144 | 8 |
| 14 | Claude Haiku 5.5 Anthropic | 55.0% | 129126–132 | 8 |
| 15 | Claude Opus 4.8 Anthropic | 52.0% | 132129–135 | 8 |
| 16 | Gemini 3.1 Pro Google DeepMind | 51.0% | 127124–130 | 9 |
| 17 | Claude Fable 5.1 Anthropic | 48.0% | 140136–143 | 8 |
| 18 | DeepSeek V4.1 Flashopen DeepSeek | 48.0% | 128125–131 | 8 |
| 19 | GPT-5 Pro OpenAI | 48.0% | 124120–128 | 3 too little data |
| 20 | GPT-5.2 OpenAI | 48.0% | 125122–128 | 10 |
| 21 | GPT-5.4 OpenAI | 48.0% | 131128–133 | 9 |
| 22 | Claude Fable 5 Anthropic | 47.0% | 138134–141 | 8 |
| 23 | GPT-5.6 Terra OpenAI | 47.0% | 134131–137 | 8 |
| 24 | DeepSeek V4 Pro 0813open DeepSeek | 46.0% | 128125–131 | 8 |
| 25 | Qwen 3.8 Max Alibaba | 46.0% | 130127–133 | 7 too little data |
| 26 | Claude Sonnet 5 Anthropic | 45.0% | 126123–129 | 8 |
| 27 | GPT-5.4 Nano OpenAI | 45.0% | 118115–121 | 8 |
| 28 | Gemini 3.5 Flash Google DeepMind | 45.0% | 126123–129 | 9 |
| 29 | Claude Opus 5 Anthropic | 43.0% | 136133–140 | 8 |
| 30 | GPT-5.6 Luna OpenAI | 43.0% | 130127–133 | 8 |
| 31 | GPT-5.1 OpenAI | 42.0% | 120115–125 | 7 |
| 32 | Gemini 3.6 Flash Google DeepMind | 42.0% | 125122–128 | 8 |
| 33 | Claude Opus 4.7 Anthropic | 41.0% | 127124–130 | 9 |
| 34 | Grok 4.7 xAI | 41.0% | 123120–127 | 7 too little data |
| 35 | Gemini 3 Flash Google DeepMind | 39.0% | 122119–125 | 9 |
| 36 | GLM-5.3open Z.ai (Zhipu AI) | 37.0% | 125122–128 | 8 |
| 37 | Kimi K3open Moonshot | 37.0% | 127124–130 | 8 |
| 38 | Muse Spark 1.3 Meta AI | 37.0% | 128125–131 | 5 too little data |
| 39 | Qwen3.8 Max (0902) Alibaba | 37.0% | 127123–130 | 6 too little data |
| 40 | o3 OpenAI | 37.0% | 116113–119 | 10 |
| 41 | GPT-5 OpenAI | 36.0% | 121118–124 | 11 |
| 42 | GPT-6 Luna OpenAI | 36.0% | 130127–133 | 8 |
| 43 | Grok 4.5 xAI | 35.0% | 124121–127 | 6 too little data |
| 44 | Muse Spark 1.1 Meta AI | 34.0% | 125116–133 | 2 too little data |
| 45 | Grok 4.6 xAI | 33.0% | 126123–129 | 8 |
| 46 | Mistral Large 4 Mistral AI | 33.0% | 122119–125 | 7 too little data |
| 47 | DeepSeek V4 Flash 0731open DeepSeek | 32.0% | 124121–127 | 8 |
| 48 | Muse Spark 1.2 Meta AI | 27.0% | 121112–129 | 2 too little data |
| 49 | Qwen3.7-Max Alibaba | 26.0% | 125122–128 | 9 |
| 50 | GPT-5 mini OpenAI | 24.0% | 115112–118 | 11 |
| 51 | Claude Sonnet 4.6 Anthropic | 23.0% | 113108–118 | 7 |
| 52 | GPT-5.4 Mini OpenAI | 23.0% | 117113–120 | 8 |
| 53 | GLM-5.2open Z.ai (Zhipu AI) | 22.0% | 121118–124 | 9 |
| 54 | GPT-5 nano OpenAI | 22.0% | 105102–109 | 10 |
| 55 | o4-mini OpenAI | 22.0% | 112109–115 | 10 |
| 56 | Claude Opus 4.6 Anthropic | 21.0% | 123120–126 | 9 |
| 57 | GLM-4.7open Z.ai (Zhipu AI) | 21.0% | 112107–118 | 5 |
| 58 | GLM-5.3-Flashopen Z.ai (Zhipu AI) | 21.0% | 120117–124 | 7 |
| 59 | Gemini 3.5 Flash-Lite Google DeepMind | 21.0% | 109106–113 | 7 |
| 60 | Inklingopen Thinking Machines | 21.0% | 114110–117 | 7 |
| 61 | GLM-5.1open Z.ai (Zhipu AI) | 20.0% | 116112–120 | 7 |
| 62 | Kimi K2.6open Moonshot | 20.0% | 121118–124 | 9 |
| 63 | DeepSeek-V4-Proopen DeepSeek | 19.0% | 117113–120 | 9 |
| 64 | Grok 4.3 Beta xAI | 18.0% | 117114–121 | 6 too little data |
| 65 | o3-mini OpenAI | 15.0% | 105101–108 | 9 |
| 66 | Claude Opus 4.5 Anthropic | 14.0% | 115111–118 | 10 |
| 67 | Kimi K2.7 Codeopen Moonshot | 14.0% | 118115–122 | 7 |
| 68 | Qwen 3.5 Flash (hosted 35B-A3B) Alibaba | 14.0% | 107103–111 | 6 too little data |
| 69 | Qwen 3.6 Plus Alibaba | 14.0% | 113109–117 | 7 too little data |
| 70 | MiniMax-M3open MiniMax | 13.0% | 108102–114 | 5 |
| 71 | Qwen 3.6 Flash Alibaba | 12.0% | 107103–111 | 6 too little data |
| 72 | Claude Sonnet 4.5 Anthropic | 10.0% | 110107–113 | 11 |
| 73 | o1 OpenAI | 10.0% | 10399–106 | 6 too little data |
| 74 | Claude Haiku 4.5 Anthropic | 8.0% | 10499–108 | 7 |
| 75 | Qwen 3.5 Plus (hosted 397B-A17B) Alibaba | 8.0% | 109103–115 | 5 too little data |
| 76 | GPT-4o (Aug 2024) OpenAI | 6.0% | 7368–77 | 8 |
| 77 | Claude 3 Opus Anthropic | 3.0% | 6560–70 | 5 too little data |
| 78 | GPT-4.1 OpenAI | 3.0% | 9187–94 | 10 |
| 79 | GPT-4o mini OpenAI | 3.0% | 6864–73 | 9 |
| 80 | GPT-4.1 mini OpenAI | 1.0% | 9188–95 | 10 |
| 81 | GPT-4.1 nano OpenAI | 0.0% | 7672–80 | 8 |
Data sources: Aider polyglot leaderboard data (Apache-2.0); Arena leaderboard dataset on Hugging Face (CC BY 4.0); Berkeley Function Calling Leaderboard, Gorilla project (Apache-2.0); Epoch AI, ‘Capabilities & benchmarking’ (CC BY 4.0); API list prices via LiteLLM's price table (MIT); each price links to the developer's pricing page; Source: OpenRouter (openrouter.ai), licensed under CC BY 4.0: reuse and republish with attribution to OpenRouter..