HumanEval tests whether AI models can generate correct Python functions from docstrings. Here's what it measures, why it's nearly saturated, and which benchmarks matter more in 2026.
HumanEval is a benchmark of 164 hand-written Python programming problems. Each problem gives the model a function signature and docstring, and the model must generate a working function body. The generated code is tested against unit tests to check if it actually works.
It was created by OpenAI in 2021 and quickly became the standard way to measure whether an AI model can write code.
Each problem in HumanEval includes:
The model generates code, and that code gets executed. If it passes all the unit tests, it's counted as correct. The final score is the percentage of problems solved (pass@1 means one attempt per problem).
This is important: HumanEval measures functional correctness, not whether the code looks right. A syntactically perfect solution that returns wrong answers scores zero. A ugly solution that passes all tests scores 100%.
Look at the scores on our HumanEval leaderboard:
When most top models score above 90%, the benchmark stops being useful for distinguishing between them. A model scoring 93 vs 91 on HumanEval doesn't tell you much about which one will be better at your actual coding tasks.
The problems in HumanEval are mostly introductory to intermediate difficulty — string manipulation, basic algorithms, and simple data structures. Frontier models have gotten too good at this level of coding.
HumanEval tests single-function generation. Real coding work involves:
These gaps are why benchmarks like SWE-bench Verified (real GitHub issue resolution) and LiveCodeBench (fresh competitive programming problems) are more informative in 2026.
HumanEval is still useful as a baseline filter. If a model scores below 80 on HumanEval, it's probably not competitive for coding tasks. But once you're above 85, you need to look at harder benchmarks to see real differences.
It's also useful for evaluating smaller or open-weight models where the gap between models is larger. A 15-point spread between open-weight models on HumanEval is meaningful in a way that a 2-point spread between frontier models isn't.
HumanEval was the right benchmark for 2022. In 2026, it's a checkbox — does the model clear the bar for basic code generation? — not a differentiator. For choosing between frontier models, check SWE-bench and LiveCodeBench instead.
See all coding benchmark scores on our coding leaderboard, or compare specific models on their model pages.
Data sourced from the BenchLM.ai HumanEval leaderboard. Last updated March 2026.
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