SimpleBench

Where Everyday Human Reasoning Still Surpasses Frontier Models

SimpleBench Team
Latest Leaderboard Report Try Yourself Hugging Face Public Dataset Code

Introduction

We introduce SimpleBench, a multiple-choice text benchmark for LLMs where individuals with unspecialized (high school) knowledge outperform SOTA models. SimpleBench includes over 200 questions covering spatio-temporal reasoning, social intelligence, and what we call linguistic adversarial robustness (or trick questions). For the vast majority of text-based benchmarks LLMs outperform a non-specialized human, and increasingly, exceed expert human performance. However, on SimpleBench, a non-specialized human baseline is 83.7%, based on our small sample of nine participants, outperforming every tested LLM, including today's top model, Claude Fable, which scored 81.9%. While we expect model performance to improve over time, the results of SimpleBench confirm that the memorized knowledge, and approximate reasoning retrieval, utilized by frontier LLMs is not always enough to answer basic questions just yet.

For a deeper dive into our results and our methods, check out the full technical report here.

Use all of these models on the Simple Bench app - LMcouncil.ai

Leaderboard

Rank Model Score (AVG@5) Organization

temperature: 0.7, top-p: 0.95 (except o1 series)
*See Human Evaluation section of Report for details on how we calculated Human Baseline.
**We try an engineered prompt to optimize benchmark specific performance. See LLM Eval section of Report for details.

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