reasoning benchmark
EvalPlus Leaderboard
A rigorous code synthesis evaluation framework that augments existing datasets with extensive test cases generated by LLM and mutation-based strategies to better assess functional correctness of generated code, including HumanEval+ with 80x more test cases
Updated Aug 17, 2026
Models4
Model coverage4
MetricScore
EvidenceB
EvalPlus Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMA | Score0.803 points | Percentile100.0% | Participants4 | EvidenceC | Evaluated |
| Rank02 | ModelAC | Score0.79 points | Percentile66.7% | Participants4 | EvidenceC | Evaluated |
| Rank03 | ModelAC | Score0.776 points | Percentile33.3% | Participants4 | EvidenceC | Evaluated |
| Rank04 | ModelAC | Score0.703 points | Percentile0.0% | Participants4 | EvidenceC | Evaluated |
EvalPlus Highlights
The leading models and scores on this benchmark.
EvalPlus Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for EvalPlus
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis evalplus AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01MAKimi K2 BaseMoonshot AI
- Score
- 0.803 points
- Price
- $0.60 input / $2.5 output per 1M tokens
- Speed
- Up to 45 tok/s via Novita
Strengths
- Ranks #1 of 4 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures EvalPlus, not total model capability
- 02ACQwen2 72B InstructAlibaba Cloud / Qwen Team
- Score
- 0.79 points
Strengths
- Ranks #2 of 4 compared models
- 67th percentile on this benchmark
- C evidence result
Considerations
- This result measures EvalPlus, not total model capability
- 03ACQwen3 235B A22BAlibaba Cloud / Qwen Team
- Score
- 0.776 points
- Price
- $0.70 input / $2.8 output per 1M tokens
- Speed
- Up to 68 tok/s via Fireworks
Strengths
- Ranks #3 of 4 compared models
- 33th percentile on this benchmark
- C evidence result
Considerations
- This result measures EvalPlus, not total model capability
- 04ACQwen2 7B InstructAlibaba Cloud / Qwen Team
- Score
- 0.703 points
Strengths
- Ranks #4 of 4 compared models
- 0th percentile on this benchmark
- C evidence result
Considerations
- This result measures EvalPlus, not total model capability
Selection summary
Best AI Models for EvalPlus
Kimi K2 Base currently leads EvalPlus with 0.803 points. 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 is EvalPlus?
What EvalPlus measures and how its scores work.
A rigorous code synthesis evaluation framework that augments existing datasets with extensive test cases generated by LLM and mutation-based strategies to better assess functional correctness of generated code, including HumanEval+ with 80x more test cases
Scores are shown in points. This benchmark is not independently verified and has an evidence level of B.
- Family
- EvalPlus
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- evalplus|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about EvalPlus.
Which model scores highest on EvalPlus?
Kimi K2 Base is currently ranked first with 0.803 points.
What does EvalPlus measure?
A rigorous code synthesis evaluation framework that augments existing datasets with extensive test cases generated by LLM and mutation-based strategies to better assess functional correctness of generated code, including HumanEval+ with 80x more test cases
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
4 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.