BigCodeBench-Hard
BigCodeBench-Hard is a subset of 148 challenging programming tasks from BigCodeBench, designed to evaluate large language models' ability to solve complex, real-world programming problems. These tasks require diverse function calls from multiple libraries across 7 domains including computation, networking, data analysis, and visualization. The benchmark tests compositional reasoning and the ability to implement complex instructions that span 139 libraries with an average of 2.8 libraries per task.
Qwen2.5-Coder 32B Instruct from Alibaba Cloud / Qwen Team currently leads the BigCodeBench-Hard leaderboard with a score of 0.270 across 1 evaluated AI models.
Qwen2.5-Coder 32B Instruct leads with 27.0%.
Progress Over Time
Interactive timeline showing model performance evolution on BigCodeBench-Hard
BigCodeBench-Hard Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Alibaba Cloud / Qwen Team | 32B | 128K | $0.09 / $0.09 |
FAQ
Common questions about BigCodeBench-Hard.
More evaluations to explore
Related benchmarks in the same category
A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult, with PhD experts reaching 65% accuracy.
A more robust and challenging multi-task language understanding benchmark that extends MMLU by expanding multiple-choice options from 4 to 10, eliminating trivial questions, and focusing on reasoning-intensive tasks. Features over 12,000 curated questions across 14 domains and causes a 16-33% accuracy drop compared to original MMLU.
All 30 problems from the 2025 American Invitational Mathematics Examination (AIME I and AIME II), testing olympiad-level mathematical reasoning with integer answers from 000-999. Used as an AI benchmark to evaluate large language models' ability to solve complex mathematical problems requiring multi-step logical deductions and structured symbolic reasoning.
Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains
A verified subset of 500 software engineering problems from real GitHub issues, validated by human annotators for evaluating language models' ability to resolve real-world coding issues by generating patches for Python codebases.
Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions