Reasoning benchmark # EnigmaEval leaderboard > EnigmaEval results for 38 AI models, led by Claude Fable 5 at 39.3%. What the benchmark measures, who runs it, and a source for every score. - Canonical page: https://noometry.com/benchmarks/enigmaeval - Last updated: 2026-10-10 - Title: EnigmaEval Leaderboard (October 2026): Scores by Model As of October 2026, Claude Fable 5 has the highest published EnigmaEval score on Noometry at 39.3%, out of 38 models with results. Last verified October 10, 2026 ## About EnigmaEval Long multimodal puzzle-hunt problems that require creative, multi-step reasoning. - **Category:** [Reasoning](https://noometry.com/best/reasoning) - **Introduced:** 2025 - **Format:** Puzzles - **Unit:** Percent (random guessing ≈ 0%) - **Official site:** [scale.com](https://scale.com/leaderboard) ## Top 15 models Top models on EnigmaEval 1. Claude Fable 5 39.3% 2. GPT-5.6 Sol 37.1% 3. Gemini 3.1 Pro Preview 36.8% 4. Gemini 3.5 Flash 25.4% 5. GPT-5.4 Pro 23.8% 6. Claude Opus 4.8 23.5% 7. GPT-5 Pro 18.8% 8. Gemini 3 Pro 18.2% 9. GPT-5.4 16% 10. o3 13.1% 11. Claude Opus 4.5 11.9% 12. GPT-5.1 11.2% 13. GPT-5 10.5% 14. GPT-5.2 10.4% 15. o4-mini 9.2% 16. 010203040 Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise) ## All results EnigmaEval results by model | # | Model | Provider | Score | Setting | Source | Date | | --- | --- | --- | --- | --- | --- | --- | | 1 | [Claude Fable 5](https://noometry.com/models/claude-fable-5) | [Anthropic](https://noometry.com/providers/anthropic) | 39.3% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 2 | [GPT-5.6 Sol](https://noometry.com/models/gpt-5-6-sol) | [OpenAI](https://noometry.com/providers/openai) | 37.1% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 3 | [Gemini 3.1 Pro Preview](https://noometry.com/models/gemini-3-1-pro-preview) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 36.8% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 4 | [Gemini 3.5 Flash](https://noometry.com/models/gemini-3-5-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 25.4% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 5 | [GPT-5.4 Pro](https://noometry.com/models/gpt-5-4-pro) | [OpenAI](https://noometry.com/providers/openai) | 23.8% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 6 | [Claude Opus 4.8](https://noometry.com/models/claude-opus-4-8) | [Anthropic](https://noometry.com/providers/anthropic) | 23.5% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 7 | [GPT-5 Pro](https://noometry.com/models/gpt-5-pro) | [OpenAI](https://noometry.com/providers/openai) | 18.8% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 8 | [Gemini 3 Pro](https://noometry.com/models/gemini-3-pro) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 18.2% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 9 | [GPT-5.4](https://noometry.com/models/gpt-5-4) | [OpenAI](https://noometry.com/providers/openai) | 16% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 10 | [o3](https://noometry.com/models/o3) | [OpenAI](https://noometry.com/providers/openai) | 13.1% | medium | [Epoch AI](https://epoch.ai/benchmarks) | | | 11 | [Claude Opus 4.5](https://noometry.com/models/claude-opus-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 11.9% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 12 | [GPT-5.1](https://noometry.com/models/gpt-5-1) | [OpenAI](https://noometry.com/providers/openai) | 11.2% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 13 | [GPT-5](https://noometry.com/models/gpt-5) | [OpenAI](https://noometry.com/providers/openai) | 10.5% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 14 | [GPT-5.2](https://noometry.com/models/gpt-5-2) | [OpenAI](https://noometry.com/providers/openai) | 10.4% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 15 | [o4-mini](https://noometry.com/models/o4-mini) | [OpenAI](https://noometry.com/providers/openai) | 9.2% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 16 | [GPT-5 Mini](https://noometry.com/models/gpt-5-mini) | [OpenAI](https://noometry.com/providers/openai) | 8.2% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 17 | [Claude Opus 4.6](https://noometry.com/models/claude-opus-4-6) | [Anthropic](https://noometry.com/providers/anthropic) | 7.6% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 18 | [Claude Opus 4.1](https://noometry.com/models/claude-opus-4-1) | [Anthropic](https://noometry.com/providers/anthropic) | 7.2% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 19 | [o1-pro](https://noometry.com/models/o1-pro) | [OpenAI](https://noometry.com/providers/openai) | 6.1% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 20 | [Claude Sonnet 4.5](https://noometry.com/models/claude-sonnet-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 6% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 21 | [o1](https://noometry.com/models/o1) | [OpenAI](https://noometry.com/providers/openai) | 5.7% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 22 | [Claude Opus 4](https://noometry.com/models/claude-opus-4) | [Anthropic](https://noometry.com/providers/anthropic) | 5.6% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 23 | [Gemini 2.5 Pro](https://noometry.com/models/gemini-2-5-pro) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 5.6% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 24 | [Claude 3.7 Sonnet](https://noometry.com/models/claude-3-7-sonnet) | [Anthropic](https://noometry.com/providers/anthropic) | 4.2% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 25 | [Kimi K2.5](https://noometry.com/models/kimi-k2-5) | [Moonshot AI](https://noometry.com/providers/moonshot) | 3.4% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 26 | [GPT-4.5](https://noometry.com/models/gpt-4-5) | [OpenAI](https://noometry.com/providers/openai) | 3.2% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 27 | [Claude Sonnet 4](https://noometry.com/models/claude-sonnet-4) | [Anthropic](https://noometry.com/providers/anthropic) | 3.1% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 28 | [Gemini 3.1 Flash Lite](https://noometry.com/models/gemini-3-1-flash-lite) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 3% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 29 | [Gemini 2.5 Flash](https://noometry.com/models/gemini-2-5-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 2.7% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 30 | [GPT-4.1](https://noometry.com/models/gpt-4-1) | [OpenAI](https://noometry.com/providers/openai) | 2.2% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 31 | [Gemini 2.0 Flash (Feb 2025)](https://noometry.com/models/gemini-2-0-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 1.1% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 32 | [Claude 3.5 Sonnet](https://noometry.com/models/claude-3-5-sonnet) | [Anthropic](https://noometry.com/providers/anthropic) | 0.9% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 33 | [Pixtral Large](https://noometry.com/models/pixtral-large) | [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 0.8% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 34 | [Claude 3 Opus](https://noometry.com/models/claude-3-opus) | [Anthropic](https://noometry.com/providers/anthropic) | 0.8% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 35 | [GPT-4o](https://noometry.com/models/gpt-4o) | [OpenAI](https://noometry.com/providers/openai) | 0.8% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 36 | [Gemini 2.0 Pro](https://noometry.com/models/gemini-2-0-pro) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 0.7% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 37 | [Llama 4 Maverick](https://noometry.com/models/llama-4-maverick) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 0.6% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 38 | [Llama 3.2 90B](https://noometry.com/models/llama-3-2-90b) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 0.4% | | [Epoch AI](https://epoch.ai/benchmarks) | | ## Compare the leaders - [Claude Fable 5 vs GPT-5.6 Sol](https://noometry.com/compare/claude-fable-5-vs-gpt-5-6-sol) - [Claude Fable 5 vs Gemini 3.1 Pro Preview](https://noometry.com/compare/claude-fable-5-vs-gemini-3-1-pro-preview) - [Claude Fable 5 vs Gemini 3.5 Flash](https://noometry.com/compare/claude-fable-5-vs-gemini-3-5-flash) - [Claude Fable 5 vs GPT-5.4 Pro](https://noometry.com/compare/claude-fable-5-vs-gpt-5-4-pro) - [GPT-5.6 Sol vs Gemini 3.1 Pro Preview](https://noometry.com/compare/gemini-3-1-pro-preview-vs-gpt-5-6-sol) - [GPT-5.6 Sol vs Gemini 3.5 Flash](https://noometry.com/compare/gemini-3-5-flash-vs-gpt-5-6-sol) ## 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) - [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) - [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 EnigmaEval measure? Long multimodal puzzle-hunt problems that require creative, multi-step reasoning. ### Which model has the highest EnigmaEval score? As of October 2026, Claude Fable 5 has the highest published EnigmaEval score on Noometry at 39.3%, out of 38 models with results. ### What is the best open-weight model on EnigmaEval? Kimi K2.5 has the highest EnigmaEval accuracy among open-weight models at 3.4%, ranking 25 of 38 overall. ### Cite this page Noometry. (2026). EnigmaEval leaderboard. Retrieved October 10, 2026, from https://noometry.com/benchmarks/enigmaeval Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/benchmarks/enigmaeval.md).