HumanEval¶

What it is¶

HumanEval is a benchmark released by OpenAI to evaluate the code generation capabilities of Large Language Models. It consists of 164 handwritten programming problems, each including a function signature, docstring, body, and several unit tests. As of early January 2027, it remains a foundational metric for assessing the core algorithmic reasoning of models like Claude 5.1, GPT-5.5/5.6, Gemini 4.0 Pro, and Llama 4 Maverick.

What problem it solves¶

Provides a standardized, non-contaminated measure of whether LLMs can generate functionally correct code from natural language descriptions. Since the problems were handwritten, it provides a cleaner evaluation of zero-shot coding ability than benchmarks derived from public repositories which may have been seen during training.

System Architecture¶

                                  HumanEval Code Generation Evaluation

  +-----------------------+        +------------------------+        +--------------------------+
  | 164 Python Problems   | ---->  | FastMCP 3.1 Task      | ---->  | Frontier LLM             |
  | (Docstring & Specs)   |        | Protocol Orchestrator  |        | (Claude 5.1/GPT-5.5)     |
  +-----------------------+        +------------------------+        +--------------------------+
                                                                                  |
                                                                                  v
  +-----------------------+        +------------------------+        +--------------------------+
  | Pass@k Metric &       | <----  | Pydantic v2 Pass@k     | <----  | Unit Test Execution      |
  | Leaderboard Telemetry |        | Calculation Pipeline   |        | Sandbox (pytest/unittest)|
  +-----------------------+        +------------------------+        +--------------------------+

Where it fits in the stack¶

Benchmarking. Used as a primary reference benchmark for code generation and algorithmic reasoning capabilities of LLMs.

Typical use cases¶

Evaluating LLM code generation accuracy on self-contained programming tasks.
Comparing models on their ability to produce correct Python code.
Measuring improvements in code generation across model versions or fine-tuning runs.
Assessing the "coding intelligence" of frontier reasoning models.

Strengths¶

Well-Established: Widely cited and used as an industry standard.
Automated Validation: Problems include clear unit tests for functional correctness.
Pass@k Metric: Accounts for sampling variability and model creativity.
Zero-Shot Focus: Designed to test raw logic rather than library-specific knowledge.

Limitations¶

Small Scale: Only 164 problems, which may not cover modern software complexity.
Python-Centric: Primarily focuses on Python and basic algorithmic tasks.
Limited Realism: Does not test debugging, refactoring, or multi-file engineering (use SWE-bench for this).
Contamination Risk: Due to its popularity, newer models may have inadvertently included it in training data.

When to use it¶

When comparing frontier LLMs on their ability to generate correct code from specifications.
When evaluating a model for "coding assistant" use cases.
As a fast, automated check for coding regression in model pipelines.

When not to use it¶

When you need to evaluate real-world software engineering capability (use SWE-bench instead).
When you need multilingual code generation evaluation (use MultiPL-E).
For evaluating complex system design or library-specific knowledge.

Getting started¶

HumanEval can be run using the official OpenAI execution environment or through broader harnesses like the LM Evaluation Harness.

Clone the HumanEval repository or use lm-eval.
Install dependencies: pip install human-eval
Run the evaluation script (warning: this executes model-generated code in a sandbox).

CLI examples¶

1. Running HumanEval via LM Evaluation Harness¶

Evaluate a local model's coding performance with required code execution permission:

python -m lm_eval --model hf \
    --model_args pretrained=meta-llama/Llama-4-Maverick-70B \
    --tasks humaneval \
    --device cuda:0 \
    --allow_code_execution

2. Evaluating Samples with OpenAI's Tool¶

If you have a file of generated samples (samples.jsonl), run the official evaluator:

python evaluate_functional_correctness.py samples.jsonl

3. Generating Solutions with Aider¶

Use Aider to generate solutions for a specific HumanEval problem:

aider --message "Solve HumanEval problem 0 in Python"

API examples¶

1. Python: Calculating Pass@k with Pydantic v2¶

A utility snippet using Pydantic v2 to calculate and structure the Pass@k metric:

import math
from pydantic import BaseModel, Field

class PassAtKCalculator(BaseModel):
    n_samples: int = Field(..., gt=0, description="Total number of generated samples")
    c_correct: int = Field(..., ge=0, description="Number of correct samples")
    k: int = Field(..., gt=0, description="The k-value for pass@k evaluation")

    def calculate(self) -> float:
        if self.n_samples - self.c_correct < self.k:
            return 1.0
        return 1.0 - math.comb(self.n_samples - self.c_correct, self.k) / math.comb(self.n_samples, self.k)

# Example: 100 samples, 40 correct, Pass@1
evaluator = PassAtKCalculator(n_samples=100, c_correct=40, k=1)
print(f"Pass@1: {evaluator.calculate():.2%}")

2. Performance Comparison (Early 2027 Baseline)¶

Model	HumanEval Pass@1 (%)	Notes
Claude 5.1 Opus	98.4%	SOTA Coding Reasoning
GPT-5.5	97.9%	High algorithmic consistency
Gemini 4.0 Pro	96.2%	Robust logic generation
Llama 4 Maverick	93.5%	Best-in-class open model
Claude 3.5 Sonnet	92.0%	Baseline June 2024

3. Requesting SOTA Metrics via FastMCP¶

Retrieve the latest HumanEval leaderboard for a specific model using standard JSON-RPC layout:

{
  "jsonrpc": "2.0",
  "method": "get_benchmark_results",
  "params": {
    "benchmark": "humaneval",
    "model": "gpt-5-5"
  },
  "id": 1
}

Sources / references¶

Contribution Metadata¶

Last reviewed: 2027-01-07
Confidence: high