HumanEval+
Enhanced version of HumanEval that extends the original test cases by 80x using EvalPlus framework for rigorous evaluation of LLM-synthesized code functional correctness, detecting previously undetected wrong code
Phi 4 Reasoning from Microsoft currently leads the HumanEval+ leaderboard with a score of 0.929 across 9 evaluated AI models.
Phi 4 Reasoning leads with 92.9%, followed by
Phi 4 Reasoning Plus at 92.3% and
Granite 3.3 8B Base at 86.1%.
Progress Over Time
Interactive timeline showing model performance evolution on HumanEval+
HumanEval+ Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Microsoft | 14B | — | — | ||
| 2 | Microsoft | 14B | — | — | ||
| 3 | 8B | — | — | |||
| 3 | 8B | 128K | $0.50 / $0.50 | |||
| 5 | Microsoft | 15B | 16K | $0.07 / $0.14 | ||
| 6 | 7B | — | — | |||
| 7 | Alibaba Cloud / Qwen Team | 33B | — | — | ||
| 8 | Alibaba Cloud / Qwen Team | 15B | — | — | ||
| 9 | Baidu | 21B | 128K | $0.40 / $4.00 |
FAQ
Common questions about HumanEval+.
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