Reasoning benchmark # Chess Puzzles leaderboard > Chess Puzzles results for 129 AI models, led by GPT-6 Astra at 72%. What the benchmark measures, who runs it, and a source for every score. - Canonical page: https://noometry.com/benchmarks/chess-puzzles - Last updated: 2026-10-10 - Title: Chess Puzzles Leaderboard (October 2026): Scores by Model As of October 2026, GPT-6 Astra has the highest published Chess Puzzles score on Noometry at 72%, out of 129 models with results. Last verified October 10, 2026 ## About Chess Puzzles Chess tactics puzzles solved from a text position, without an engine. - **Category:** [Reasoning](https://noometry.com/best/reasoning) - **Introduced:** 2025 - **Format:** Move prediction - **Unit:** Percent (random guessing ≈ 5%) - **Official site:** [epoch.ai](https://epoch.ai/benchmarks) ## Top 15 models Top models on Chess Puzzles 1. GPT-6 Astra 72% 2. GPT-5.5 Pro 64% 3. GPT-5.6 Sol 64% 4. Gemini 3.8 Flash 61% 5. GPT-6.1 Sol 61% 6. GPT-5.4 Pro 58.6% 7. Gemini 3.1 Pro Preview 55% 8. GPT-5.5 54% 9. GPT-5.6 Terra 54% 10. Gemini 3.5 Flash 50% 11. GPT-5.2 49% 12. Claude Fable 5.1 47% 13. DeepSeek V4 Pro 47% 14. Gemini 3.7 Flash 47% 15. GPT-5.4 44% 16. 304050607080 Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise) ## All results Chess Puzzles results by model | # | Model | Provider | Score | Setting | Source | Date | | --- | --- | --- | --- | --- | --- | --- | | 1 | [GPT-6 Astra](https://noometry.com/models/gpt-6-astra) | [OpenAI](https://noometry.com/providers/openai) | 72% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 | | 2 | [GPT-5.5 Pro](https://noometry.com/models/gpt-5-5-pro) | [OpenAI](https://noometry.com/providers/openai) | 64% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-05-01 | | 3 | [GPT-5.6 Sol](https://noometry.com/models/gpt-5-6-sol) | [OpenAI](https://noometry.com/providers/openai) | 64% | promax | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-10 | | 4 | [Gemini 3.8 Flash](https://noometry.com/models/gemini-3-8-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 61% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-02 | | 5 | [GPT-6.1 Sol](https://noometry.com/models/gpt-6-1-sol) | [OpenAI](https://noometry.com/providers/openai) | 61% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-29 | | 6 | [GPT-5.4 Pro](https://noometry.com/models/gpt-5-4-pro) | [OpenAI](https://noometry.com/providers/openai) | 58.6% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-03-19 | | 7 | [Gemini 3.1 Pro Preview](https://noometry.com/models/gemini-3-1-pro-preview) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 55% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-19 | | 8 | [GPT-5.5](https://noometry.com/models/gpt-5-5) | [OpenAI](https://noometry.com/providers/openai) | 54% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-04-24 | | 9 | [GPT-5.6 Terra](https://noometry.com/models/gpt-5-6-terra) | [OpenAI](https://noometry.com/providers/openai) | 54% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-09 | | 10 | [Gemini 3.5 Flash](https://noometry.com/models/gemini-3-5-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 50% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-05-28 | | 11 | [GPT-5.2](https://noometry.com/models/gpt-5-2) | [OpenAI](https://noometry.com/providers/openai) | 49% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-15 | | 12 | [Claude Fable 5.1](https://noometry.com/models/claude-fable-5-1) | [Anthropic](https://noometry.com/providers/anthropic) | 47% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-01 | | 13 | [DeepSeek V4 Pro](https://noometry.com/models/deepseek-v4-pro) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 47% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-18 | | 14 | [Gemini 3.7 Flash](https://noometry.com/models/gemini-3-7-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 47% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-14 | | 15 | [GPT-5.4](https://noometry.com/models/gpt-5-4) | [OpenAI](https://noometry.com/providers/openai) | 44% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-03-11 | | 16 | [Gemini 3.6 Flash](https://noometry.com/models/gemini-3-6-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 43% | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 17 | [Claude Opus 5](https://noometry.com/models/claude-opus-5) | [Anthropic](https://noometry.com/providers/anthropic) | 42% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-24 | | 18 | [Claude Fable 5](https://noometry.com/models/claude-fable-5) | [Anthropic](https://noometry.com/providers/anthropic) | 41% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 | | 19 | [Gemini 3 Flash Preview](https://noometry.com/models/gemini-3-flash-preview) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 40% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 | | 20 | [GPT-5.6 Luna](https://noometry.com/models/gpt-5-6-luna) | [OpenAI](https://noometry.com/providers/openai) | 40% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-09 | | 21 | [Grok 4.6](https://noometry.com/models/grok-4-6) | [xAI](https://noometry.com/providers/xai) | 40% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-12 | | 22 | [Qwen3.8 Max](https://noometry.com/models/qwen3-8-max) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 40% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-02 | | 23 | [Kimi K3](https://noometry.com/models/kimi-k3) | [Moonshot AI](https://noometry.com/providers/moonshot) | 39% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-16 | | 24 | [Grok 4.7](https://noometry.com/models/grok-4-7) | [xAI](https://noometry.com/providers/xai) | 38% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-22 | | 25 | [Muse Spark 1.3](https://noometry.com/models/muse-spark-1-3) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 38% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-18 | | 26 | [o3](https://noometry.com/models/o3) | [OpenAI](https://noometry.com/providers/openai) | 38% | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 27 | [GPT-5](https://noometry.com/models/gpt-5) | [OpenAI](https://noometry.com/providers/openai) | 37% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-08 | | 28 | [Grok 4.5](https://noometry.com/models/grok-4-5) | [xAI](https://noometry.com/providers/xai) | 36% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-08 | | 29 | [Claude Sonnet 5](https://noometry.com/models/claude-sonnet-5) | [Anthropic](https://noometry.com/providers/anthropic) | 35% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-30 | | 30 | [Claude Opus 4.8](https://noometry.com/models/claude-opus-4-8) | [Anthropic](https://noometry.com/providers/anthropic) | 34% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-05-29 | | 31 | [DeepSeek V4 Flash](https://noometry.com/models/deepseek-v4-flash) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 33% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-02 | | 32 | [GPT-5.1](https://noometry.com/models/gpt-5-1) | [OpenAI](https://noometry.com/providers/openai) | 32% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-08 | | 33 | [Gemini 3 Pro](https://noometry.com/models/gemini-3-pro) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 31% | | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-08 | | 34 | [GPT-6 Luna](https://noometry.com/models/gpt-6-luna) | [OpenAI](https://noometry.com/providers/openai) | 31% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-22 | | 35 | [Claude Opus 4.7](https://noometry.com/models/claude-opus-4-7) | [Anthropic](https://noometry.com/providers/anthropic) | 30% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-04-20 | | 36 | [GPT-5.4 nano](https://noometry.com/models/gpt-5-4-nano) | [OpenAI](https://noometry.com/providers/openai) | 30% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-04-14 | | 37 | [GPT-5 Mini](https://noometry.com/models/gpt-5-mini) | [OpenAI](https://noometry.com/providers/openai) | 30% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 38 | [Grok 4](https://noometry.com/models/grok-4) | [xAI](https://noometry.com/providers/xai) | 28% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-01-30 | | 39 | [GPT-5 Nano](https://noometry.com/models/gpt-5-nano) | [OpenAI](https://noometry.com/providers/openai) | 27% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 40 | [Kimi K2.6](https://noometry.com/models/kimi-k2-6) | [Moonshot AI](https://noometry.com/providers/moonshot) | 26% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-05-07 | | 41 | [o4-mini](https://noometry.com/models/o4-mini) | [OpenAI](https://noometry.com/providers/openai) | 26% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-08 | | 42 | [Qwen3.6 35B-A3B](https://noometry.com/models/qwen3-6-35b-a3b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 26% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 43 | [Gemini 3.1 Flash Lite](https://noometry.com/models/gemini-3-1-flash-lite) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 25% | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 | | 44 | [Grok 4.3](https://noometry.com/models/grok-4-3) | [xAI](https://noometry.com/providers/xai) | 25% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-17 | | 45 | [GPT-5.4 mini](https://noometry.com/models/gpt-5-4-mini) | [OpenAI](https://noometry.com/providers/openai) | 24% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 46 | [Grok 4.20 (Non-Reasoning)](https://noometry.com/models/grok-4-20) | [xAI](https://noometry.com/providers/xai) | 24% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-13 | | 47 | [Qwen3.7 Plus](https://noometry.com/models/qwen3-7-plus) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 24% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 48 | [Qwen3.7 Flash](https://noometry.com/models/qwen3-7-flash) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 23% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 49 | [Gemini 3.5 Flash Lite](https://noometry.com/models/gemini-3-5-flash-lite) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 22% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 | | 50 | [Qwen3.5 Plus](https://noometry.com/models/qwen3-5-plus) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 22% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 51 | [Qwen3.6 27B](https://noometry.com/models/qwen3-6-27b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 22% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 52 | [GLM-5.2](https://noometry.com/models/glm-5-2) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 21% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-17 | | 53 | [GLM-5.3](https://noometry.com/models/glm-5-3) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 21% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-24 | | 54 | [Inkling](https://noometry.com/models/inkling) | [T Thinking Machines Lab](https://noometry.com/providers/thinking-machines) | 21% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-05 | | 55 | [Kimi K2.7 Code](https://noometry.com/models/kimi-k2-7-code) | [Moonshot AI](https://noometry.com/providers/moonshot) | 21% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 56 | [Qwen3.5-Flash](https://noometry.com/models/qwen3-5-flash) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 21% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 57 | [Gemini 2.5 Pro](https://noometry.com/models/gemini-2-5-pro) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 20% | | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-08 | | 58 | [gpt-oss-120b](https://noometry.com/models/gpt-oss-120b) | [OpenAI](https://noometry.com/providers/openai) | 20% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-11 | | 59 | [Kimi K2 Thinking Turbo](https://noometry.com/models/kimi-k2-thinking-turbo) | [Moonshot AI](https://noometry.com/providers/moonshot) | 20% | | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-10 | | 60 | [Qwen3.6 Flash](https://noometry.com/models/qwen3-6-flash) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 20% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 61 | [Qwen3.6 Max Preview](https://noometry.com/models/qwen3-6-max-preview) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 20% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 62 | [GLM-5.1](https://noometry.com/models/glm-5-1) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 19% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 | | 63 | [Qwen3.7 Max](https://noometry.com/models/qwen3-7-max) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 19% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 64 | [Inkling-Small](https://noometry.com/models/inkling-small) | [T Thinking Machines Lab](https://noometry.com/providers/thinking-machines) | 18% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-14 | | 65 | [Claude Opus 4.6](https://noometry.com/models/claude-opus-4-6) | [Anthropic](https://noometry.com/providers/anthropic) | 17% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-06 | | 66 | [o3-mini](https://noometry.com/models/o3-mini) | [OpenAI](https://noometry.com/providers/openai) | 17% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-08 | | 67 | [Qwen3.6 Plus](https://noometry.com/models/qwen3-6-plus) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 17% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 68 | [o1](https://noometry.com/models/o1) | [OpenAI](https://noometry.com/providers/openai) | 15% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 69 | [DeepSeek-V3.2-Exp](https://noometry.com/models/deepseek-v3-2-exp) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 14% | | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-16 | | 70 | [GLM-5.3-Flash](https://noometry.com/models/glm-5-3-flash) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 14% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-26 | | 71 | [MiniMax-M3](https://noometry.com/models/minimax-m3) | [![](/logos/minimax.svg) MiniMax](https://noometry.com/providers/minimax) | 14% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 | | 72 | [Claude Sonnet 4.6](https://noometry.com/models/claude-sonnet-4-6) | [Anthropic](https://noometry.com/providers/anthropic) | 13% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-20 | | 73 | [GPT-4o](https://noometry.com/models/gpt-4o) | [OpenAI](https://noometry.com/providers/openai) | 13% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-15 | | 74 | [Qwen3.5 397B-A17B](https://noometry.com/models/qwen3-5-397b-a17b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 13% | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 75 | [Claude Opus 4.5](https://noometry.com/models/claude-opus-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 12% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-08 | | 76 | [Claude Sonnet 4.5](https://noometry.com/models/claude-sonnet-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 12% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-08 | | 77 | [GPT-5.5 Instant](https://noometry.com/models/gpt-5-5-instant) | [OpenAI](https://noometry.com/providers/openai) | 12% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-02 | | 78 | [Kimi K2.5](https://noometry.com/models/kimi-k2-5) | [Moonshot AI](https://noometry.com/providers/moonshot) | 12% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-01-28 | | 79 | [Nemotron 3 Ultra](https://noometry.com/models/nemotron-3-ultra) | [![](/logos/nvidia.svg) NVIDIA](https://noometry.com/providers/nvidia) | 12% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 | | 80 | [Qwen3 235B-A22B](https://noometry.com/models/qwen3-235b-a22b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 12% | | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-11 | | 81 | [Qwen3.5-9B](https://noometry.com/models/qwen3-5-9b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 12% | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 | | 82 | [GLM-5](https://noometry.com/models/glm-5) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 10% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-12 | | 83 | [Qwen3.5 35B-A3B](https://noometry.com/models/qwen3-5-35b-a3b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 10% | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 | | 84 | [Claude Haiku 4.5](https://noometry.com/models/claude-haiku-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 8% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-16 | | 85 | [Qwen3-30B-A3B](https://noometry.com/models/qwen3-30b-a3b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 8% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 | | 86 | [Claude Opus 4.1](https://noometry.com/models/claude-opus-4-1) | [Anthropic](https://noometry.com/providers/anthropic) | 7% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-20 | | 87 | [GPT-4.1 mini](https://noometry.com/models/gpt-4-1-mini) | [OpenAI](https://noometry.com/providers/openai) | 7% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-15 | | 88 | [Gemma 4 26B A4B IT](https://noometry.com/models/gemma-4-26b-a4b-it) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 6% | minimal | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 | | 89 | [GLM-4.7](https://noometry.com/models/glm-4-7) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 6% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-01-29 | | 90 | [GPT-4.1](https://noometry.com/models/gpt-4-1) | [OpenAI](https://noometry.com/providers/openai) | 6% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 91 | [GPT-4 Turbo](https://noometry.com/models/gpt-4-turbo) | [OpenAI](https://noometry.com/providers/openai) | 6% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-15 | | 92 | [Claude 3 Opus](https://noometry.com/models/claude-3-opus) | [Anthropic](https://noometry.com/providers/anthropic) | 5% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-15 | | 93 | [Gemma 4 31B IT](https://noometry.com/models/gemma-4-31b-it) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 5% | minimal | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 | | 94 | [Qwen3 32B](https://noometry.com/models/qwen3-32b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 5% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 | | 95 | [Qwen3 8B](https://noometry.com/models/qwen3-8b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 5% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 | | 96 | [QwQ-32B](https://noometry.com/models/qwq-32b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 5% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 | | 97 | [GPT-4](https://noometry.com/models/gpt-4) | [OpenAI](https://noometry.com/providers/openai) | 4% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 98 | [gpt-oss-20b](https://noometry.com/models/gpt-oss-20b) | [OpenAI](https://noometry.com/providers/openai) | 4% | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 | | 99 | [Qwen3 14B](https://noometry.com/models/qwen3-14b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 4% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 | | 100 | [Qwen3-4B](https://noometry.com/models/qwen3-4b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 4% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 | | 101 | [Qwen3 Max](https://noometry.com/models/qwen3-max) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 4% | | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-10 | | 102 | [Magistral Small](https://noometry.com/models/magistral-small) | [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 3% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 | | 103 | [DeepSeek-R1-Distill-Qwen-14B](https://noometry.com/models/deepseek-r1-distill-qwen-14b) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 1% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 | | 104 | [DeepSeek-R1-Distill-Qwen-32B](https://noometry.com/models/deepseek-r1-distill-qwen-32b) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 1% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 | | 105 | [Mistral Small 3.1](https://noometry.com/models/mistral-small-3-1) | [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 1% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 | | 106 | [Mistral Small 3.2](https://noometry.com/models/mistral-small-3-2) | [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 1% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 | | 107 | [Phi-4](https://noometry.com/models/phi-4) | [![](/logos/microsoft.svg) Microsoft](https://noometry.com/providers/microsoft) | 1% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 | | 108 | [DeepSeek LLM 67B](https://noometry.com/models/deepseek-llm-67b) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 | | 109 | [DeepSeek-R1-Distill-Qwen-1.5B](https://noometry.com/models/deepseek-r1-distill-qwen-1-5b) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 | | 110 | [Gemma 3 12B](https://noometry.com/models/gemma-3-12b) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 | | 111 | [Gemma 3 1B](https://noometry.com/models/gemma-3-1b) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 | | 112 | [Gemma 3 27B](https://noometry.com/models/gemma-3-27b) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 | | 113 | [Gemma 3 4B](https://noometry.com/models/gemma-3-4b) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 | | 114 | [GLM-4.7-Flash](https://noometry.com/models/glm-4-7-flash) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 | | 115 | [GPT-3.5-turbo](https://noometry.com/models/gpt-3-5-turbo) | [OpenAI](https://noometry.com/providers/openai) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 | | 116 | [GPT-4o mini](https://noometry.com/models/gpt-4o-mini) | [OpenAI](https://noometry.com/providers/openai) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-15 | | 117 | [Granite 4.0 Micro](https://noometry.com/models/granite-4-0-micro) | [IBM](https://noometry.com/providers/ibm) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 | | 118 | [Llama 2-13B](https://noometry.com/models/llama-2-13b) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 | | 119 | [Llama 2-7B](https://noometry.com/models/llama-2-7b) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 | | 120 | [Llama 3.1-8B](https://noometry.com/models/llama-3-1-8b) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 | | 121 | [Llama 3.2 1B](https://noometry.com/models/llama-3-2-1b) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 | | 122 | [Llama 3-8B](https://noometry.com/models/llama-3-8b) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 | | 123 | [Mistral 7B](https://noometry.com/models/mistral-7b) | [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 | | 124 | [Mistral Small 3](https://noometry.com/models/mistral-small-3) | [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 | | 125 | [Phi 3 Mini 4k Instruct](https://noometry.com/models/phi-3-mini-4k-instruct) | [![](/logos/microsoft.svg) Microsoft](https://noometry.com/providers/microsoft) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 | | 126 | [Qwen2.5 32B Instruct](https://noometry.com/models/qwen2-5-32b-instruct) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 | | 127 | [Qwen2.5 7B Instruct](https://noometry.com/models/qwen2-5-7b-instruct) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 | | 128 | [Qwen3-1.7B](https://noometry.com/models/qwen3-1-7b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 0% | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 | | 129 | [Qwen3.5-2B](https://noometry.com/models/qwen3-5-2b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 0% | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 | ## Compare the leaders - [GPT-6 Astra vs GPT-5.5 Pro](https://noometry.com/compare/gpt-5-5-pro-vs-gpt-6-astra) - [GPT-6 Astra vs GPT-5.6 Sol](https://noometry.com/compare/gpt-5-6-sol-vs-gpt-6-astra) - [GPT-6 Astra vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-gpt-6-astra) - [GPT-6 Astra vs GPT-6.1 Sol](https://noometry.com/compare/gpt-6-1-sol-vs-gpt-6-astra) - [GPT-5.5 Pro vs GPT-5.6 Sol](https://noometry.com/compare/gpt-5-5-pro-vs-gpt-5-6-sol) - [GPT-5.5 Pro vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-gpt-5-5-pro) ## Other reasoning benchmarks - [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) - [SimpleBench](https://noometry.com/benchmarks/simplebench) - [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) - [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) - [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) - [CritPt](https://noometry.com/benchmarks/critpt) - [EnigmaEval](https://noometry.com/benchmarks/enigmaeval) - [Thematic Generalization](https://noometry.com/benchmarks/thematic-generalization) - [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) - [EBR-Bench](https://noometry.com/benchmarks/ebr-bench) - [LiveBench Reasoning](https://noometry.com/benchmarks/livebench-reasoning) - [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) ## Frequently asked questions ### What does Chess Puzzles measure? Chess tactics puzzles solved from a text position, without an engine. ### Which model has the highest Chess Puzzles score? As of October 2026, GPT-6 Astra has the highest published Chess Puzzles score on Noometry at 72%, out of 129 models with results. ### What is the best open-weight model on Chess Puzzles? DeepSeek V4 Pro has the highest Chess Puzzles accuracy among open-weight models at 47%, ranking 13 of 129 overall. ### Cite this page Noometry. (2026). Chess Puzzles leaderboard. Retrieved October 10, 2026, from https://noometry.com/benchmarks/chess-puzzles Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/benchmarks/chess-puzzles.md).