Practical code generation requiring use of libraries, APIs, and complex program structures. The 'Hard' subset tests non-trivial engineering tasks.
Why it matters: More realistic than HumanEval — tests practical programming skills including library usage, API calls, and multi-file reasoning.
Top Model
72.1%
Claude Opus 4.6
Average Score
66.9%
Across 3 models
Models Tested
3
Metric: pass@1
Human Baseline
—
Range: 0%–100%
All models with a reported BigCodeBench score, ranked by highest pass@1.
BigCodeBench 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 Opus 4.6 currently holds the top score on the BigCodeBench benchmark. See our full rankings table above for the complete leaderboard with 3 models.
We update benchmark data from multiple sources including HuggingFace Open LLM Leaderboard and LMArena. Scores are refreshed regularly as new evaluations are published and new models are released.
No. While BigCodeBench 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.