SimpleBench

Where Everyday Human Reasoning Still Surpasses Frontier Models

SimpleBench Team

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 all 13 tested LLMs, including o1-preview, which scored 41.7%. 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.

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Leaderboard

Rank Model Score (AVG@5) Organization
- Human Baseline* 83.7%
1st o3 (high) 53.1% OpenAI
2nd Gemini 2.5 Pro 51.6% Google
3rd Claude 3.7 Sonnet (thinking) 46.4% Anthropic
4th Claude 3.7 Sonnet 44.9% Anthropic
5th o1-preview 41.7% OpenAI
6th Claude 3.5 Sonnet 10-22 41.4% Anthropic
7th o1-2024-12-17 (high) 40.1% OpenAI
8th o4-mini (high) 38.7% OpenAI
9th o1-2024-12-17 (med) 36.7% OpenAI
10th Grok 3 36.1% xAI
11th GPT-4.5 34.5% OpenAI
12th Gemini-exp-1206 31.1% Google
13th DeepSeek R1 30.9% DeepSeek
14th Gemini 2.0 Flash Thinking 30.7% Google
15th Llama 4 Maverick 27.7% Meta
16th Claude 3.5 Sonnet 06-20 27.5% Anthropic
17th DeepSeek V3 03-24 27.2% DeepSeek
18th Gemini 1.5 Pro 002 27.1% Google
19th GPT-4.1 27.0% OpenAI
20th GPT-4 Turbo 25.1% OpenAI
21st Claude 3 Opus 23.5% Anthropic
22nd Llama 3.1 405b instruct 23.0% Meta
23rd o3-mini (high) 22.8% OpenAI
24th Grok 2 22.7% xAI
25th Mistral Large v2 22.5% Mistral
26th Llama 3.3 70b instruct 19.9% Meta
27th DeepSeek V3 18.9% DeepSeek
28th Gemini 2.0 Flash Exp 18.9% Google
29th o1-mini 18.1% OpenAI
30th GPT-4o 08-06 17.8% OpenAI
31st Command R+ 17.4% Cohere
32nd GPT-4o mini 10.7% OpenAI
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.

Video Summary

Evaluating Reasoning and Prompting

Performance comparison of different models on selected benchmarks

To assess LLMs fairly, we standardized prompts across all models, directing them to choose the most realistic answer step-by-step (COT). Additionally, we tested a benchmark specific engineered prompt for select models. Prompt engineering showed slight improvements suggesting that while tailored prompts can aid performance, fundamental limitations remain. In the full report, we also hypothesize that the surprising underperformance of GPT4o stems from optimizing for specific industrial applications (math and coding) at the expense of holistic reasoning.

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