reasoning benchmark
BigCodeBench-Full evaluates large language models on complex, practical programming tasks through code generation.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAC | Score49.60% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis bigcodebench-full AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Qwen2.5-Coder 32B Instruct currently leads BigCodeBench-Full with 49.60%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What BigCodeBench-Full measures and how its scores work.
BigCodeBench-Full is a benchmark containing 1,140 fine-grained tasks across 7 domains that use function calls from 139 libraries.
It measures large language models' ability to generate code, invoke multiple function calls as tools, and handle complex instructions in programming tasks.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about BigCodeBench-Full.
Qwen2.5-Coder 32B Instruct is currently ranked first with 49.60%.
The current leaders are Qwen2.5-Coder 32B Instruct (49.60%).
No matched official input price is currently available.
The fastest matched records are Qwen2.5-Coder 32B Instruct (44.00 tok/s via DeepInfra).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures large language models' ability to generate code, invoke multiple function calls as tools, and handle complex instructions in programming tasks.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.