CLBench - Empirical Discovery & Simulation: leaderboard

CL-bench (context learning) category 'Empirical Discovery & Simulation': tasks that require discovering patterns from empirical data or simulations supplied in a professional context the model has to learn from. Solving rate in percent (published as a 0-1 fraction), higher is better; labels keep the reasoning effort the source records.

Metric: Solving Rate (%). Source: www.clbench.com. Saturation forecast: Around 2030. 36 models tracked.

Top models

#ModelScoreOverall rank
1GPT-5.4 (xHigh)22.8#83 (GPT-5.4)
2Hy3-preview19.6#166
3Kimi K2.518.1#152
4GPT-5.1 (High)18.1#145 (GPT-5.1)
5Seed 2.0 Pro (High)17.2#122 (Seed 2.0 Pro)
6Qwen 3.5 Plus (Thinking)16.7#133 (Qwen 3.5 Plus)
7Claude Opus 4.5 (Thinking)15.1#87 (Claude Opus 4.5)
8Gemini 3.1 Pro (Preview) (High)14.5#64 (Gemini 3.1 Pro (Preview))
9Seed 2.0 Pro (Medium)13.9#122 (Seed 2.0 Pro)
10O3 (High)13.7#132 (O3)
11GPT-5.113.6#145
12Qwen 3.6 Plus13.5#124
13Kimi K2 (Thinking)12.6#252 (Kimi K2)
14Grok 4.20 (Reasoning)12.3#179 (Grok 4.20)
15GPT-5.212.1#115

No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.

Interactive version: theaggregate.ai/benchmark?slug=clbench-empirical-discovery-simulation · How It Works · Data refreshed daily, snapshot 2026-11-05.