reasoning benchmark
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.
Updated Aug 11, 2026
Models1
Model coverage1
MetricScore
EvidenceB
BigCodeBench-Hard Ranking
Higher score ranks better on this benchmark.
| 1 | AC | 27.0% | 100.0% | 1 | C |
BigCodeBench-Hard Highlights
The leading models and scores on this benchmark.
What is BigCodeBench-Hard?
What BigCodeBench-Hard measures and how its scores work.
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.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- BigCodeBench-Hard
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- bigcodebench-hard|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about BigCodeBench-Hard.
Which model scores highest on BigCodeBench-Hard?
Qwen2.5-Coder 32B Instruct is currently ranked first with 27.0%.
What does BigCodeBench-Hard measure?
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.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
1 model results are currently shown.
Does this benchmark affect the overall score?
No. This benchmark is shown for reference but does not contribute to the overall score.