BrainBench: Exposing the Commonsense Reasoning Gap in Large Language Models

Researchers introduced BrainBench, a new benchmark revealing significant gaps in commonsense reasoning among leading LLMs. Even the best model (Claude Opus 4.6) achieved only 80.3% accuracy on 100 brainteaser questions, while GPT-4o scored just 39.7%, exposing fundamental reasoning deficits across frontier AI models.

• →BrainBench benchmark tests 100 brainteaser questions across 20 categories to expose LLM reasoning failures.
• →Top-performing Claude Opus 4.6 achieved only 80.3% accuracy while GPT-4o scored 39.7% on commonsense reasoning tasks.
• →All tested models showed 6-16 percentage point gaps between accuracy and consistency, indicating stochastic reasoning patterns.
• →Cross-lingual testing in Chinese revealed 2-8 percentage point performance degradation across most models.
• →The benchmark demonstrates LLMs often substitute surface-level heuristics for genuine commonsense reasoning.