HumanEval: Python Function Generation from Instructions

Assesses how accurately language models can write correct Python functions based solely on natural-language instructions provided as docstrings.

Overview

HumanEval is a benchmark to evaluate a model’s performance on synthesizing programs from docstrings. This implementation is based on the official implementation.

Usage

Installation

There are two ways of using Inspect Evals, from pypi as a dependency of your own project and as a standalone checked out GitHub repository.

If you are using it from pypi, install the package and its dependencies via:

pip install inspect-evals

If you are using Inspect Evals in its repository, start by installing the necessary dependencies with:

uv sync

Running evaluations

Now you can start evaluating models. For simplicity’s sake, this section assumes you are using Inspect Evals from the standalone repo. If that’s not the case and you are not using uv to manage dependencies in your own project, you can use the same commands with uv run dropped.

uv run inspect eval inspect_evals/humaneval --model openai/gpt-5-nano

You can also import tasks as normal Python objects and run them from python:

from inspect_ai import eval
from inspect_evals.humaneval import humaneval
eval(humaneval)

After running evaluations, you can view their logs using the inspect view command:

uv run inspect view

For VS Code, you can also download Inspect AI extension for viewing logs.

If you don’t want to specify the --model each time you run an evaluation, create a .env configuration file in your working directory that defines the INSPECT_EVAL_MODEL environment variable along with your API key. For example:

INSPECT_EVAL_MODEL=anthropic/claude-opus-4-1-20250805
ANTHROPIC_API_KEY=<anthropic-api-key>

Options

You can control a variety of options from the command line. For example:

uv run inspect eval inspect_evals/humaneval --limit 10
uv run inspect eval inspect_evals/humaneval --max-connections 10
uv run inspect eval inspect_evals/humaneval --temperature 0.5

See uv run inspect eval --help for all available options.

Parameters

humaneval

• solver (Solver | None): The solver to use for this evaluation. Defaults to the default solver. (default: None)
• instruction_prompt (str): The prompt to prepend to the code problem. (default: '\nRead the following function signature and docstring, and fully implement\nthe function described. Your response should only contain the code for\nthis function.\n\n')
• scorer (Scorer | list[Scorer] | None): The scorer to use for this evaluation. Defaults to the default scorer. (default: None)
• sandbox (str): The sandbox to use for this evaluation. (default: 'docker')

Dataset

Here is an example prompt from the dataset:

from typing import List

def has_close_elements(numbers: List[float], threshold: float) -> bool:
   """Check if in given list of numbers, are any two numbers closer to
   each other than given threshold.
   >>> has_close_elements([1.0, 2.0, 3.0], 0.5)
   False
   >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)
   True
   """

The model is then tasked to fill in the missing code pieces in order to make this a working function.

Scoring

Once a generation is completed, the entire function, accompanied with unit tests, is run in a subprocess and is considered a success if all unit tests pass.

The benchmark uses the \(\text{pass}@k\) metric to measure functional correctness. In brief terms, this is the per problem probability of at least 1 correct sample generation given \(k\) generations. It is defined using the following expectation:

\[ \text{pass@}k := \underset{\text{Problems}}{\mathbb{E}}\left[1-\frac{{n-c}\choose{k}}{{n}\choose{k}}\right]\]

where we sample \(n \geq k\) generations to reduce variance. Note that the default in this benchmark implementation is \(n = 5\), and we evaluate \(\text{pass}@k\) for \(k \in \\{1, 2, 5\\}\).