CommonWhy: leaderboard
To effectively interact with the real world, Large Language Models (LLMs) require entity-based commonsense reasoning, a challenging task that necessitates integrating factual knowledge about specific entities with commonsense inference. Existing datasets for evaluating LLM entity-based commonsense reasoning have largely focused on True/False or multiple-choice questions, leaving the explicit asses
Metric: BS-Prec (Long-Tail) (self-reported). Source: benchmarklist.com. 7 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Gemini 2.5 Flash | 27.7 | #245 |
| 2 | Llama 3.3 70B | 21.4 | #579 |
| 3 | O3 | 17.2 | #124 |
| 4 | GPT-5.1 | 7.2 | #135 |
| 5 | DeepSeek V3.2 | 3.2 | #205 |
Interactive version: theaggregate.ai/benchmark?slug=commonwhy · How It Works · Data refreshed daily, snapshot 2026-10-20.