reasoning benchmark
HumanEval Plus is an enhanced version of HumanEval that extends the original test cases by 80x using the EvalPlus framework.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMA | Score92.90% | 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 humaneval plus 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
Mistral Small 3.2 24B Instruct currently leads HumanEval Plus with 92.90%. 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 HumanEval Plus measures and how its scores work.
HumanEval Plus is a benchmark for evaluating LLM-synthesized code using extended HumanEval test cases.
It measures the functional correctness of LLM-synthesized code and detects previously undetected wrong code.
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 HumanEval Plus.
Mistral Small 3.2 24B Instruct is currently ranked first with 92.90%.
The current leaders are Mistral Small 3.2 24B Instruct (92.90%).
No matched official input price is currently available.
No matched runtime record is currently available.
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures the functional correctness of LLM-synthesized code and detects previously undetected wrong code.
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.