BIG-Bench Hard Leaderboard
23 challenging tasks from BIG-Bench that prior language models could not solve above average human performance. Tests multi-step reasoning including logical deduction, causal reasoning, and algorithmic thinking.
Why it matters: One of the best tests of structured reasoning ability. Scores range 60-95% for frontier models, providing good differentiation.
Top Model
93.1%
Claude 3.5 Sonnet
Average Score
78.6%
Across 47 models
Models Tested
47
Metric: accuracy
Human Baseline
-
Score Range: 0%–100%
BBH Scores - Top 25 Models
Ranked by BBH score (%)
Model Rankings
All models with a reported BBH score, ranked by highest accuracy.
About BBH
- Full Name
- BIG-Bench Hard
- Category
- Reasoning
- Metric
- accuracy (%)
- Score Range
- 0%–100%
- Human Baseline
- Not established
- Status
- Active
BBH is a standardized evaluation that measures AI model performance on specific tasks. It provides comparable scores across different models, helping developers choose the right model for their needs.
Claude 3.5 Sonnet currently holds the top score on the BBH benchmark. See our full rankings table above for the complete leaderboard with 47 models.
We update benchmark data from multiple sources including HuggingFace open-source model leaderboards and LMArena. Scores are refreshed regularly as new evaluations are published and new models are released.
No. While BBH is an important indicator, real-world performance depends on many factors including pricing, latency, context window, and specific task requirements. We recommend using our composite score which weighs multiple benchmarks and practical factors.