Practical code generation requiring use of libraries, APIs, and complex program structures. The 'Hard' subset tests non-trivial engineering tasks.
为什么重要： More realistic than HumanEval — tests practical programming skills including library usage, API calls, and multi-file reasoning.
顶级模型
72.1%
Claude Opus 4.6
平均评分
66.9%
共3个模型
已测试模型
3
指标: pass@1
人类基准
—
评分范围: 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.