---
title: "code-eval vs human-eval"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/abacaj-code-eval-vs-openai-human-eval"
tools: ["abacaj-code-eval", "openai-human-eval"]
---

# code-eval vs human-eval

*GraphCanon updated Aug 5, 2026*

## Verdict

Pick code-eval if code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability; pick human-eval if human-eval is a tool designed for evaluating large language models trained specifically on code through human-written tests.

[code-eval](https://github.com/abacaj/code-eval) reports 431 GitHub stars, 37 forks, and 5 open issues, last pushed Sep 12, 2023. [human-eval](https://github.com/openai/human-eval) has 3.3k stars, 452 forks, and 44 open issues, last pushed Jan 17, 2025. Figures are from public GitHub metadata via [code-eval's repository](https://github.com/abacaj/code-eval) and [human-eval's repository](https://github.com/openai/human-eval).

| | [code-eval](/tools/abacaj-code-eval.md) | [human-eval](/tools/openai-human-eval.md) |
| --- | --- | --- |
| Tagline | Run evaluation on LLMs using human-eval benchmark. | Evaluating Large Language Models Trained on Code |
| Stars | 431 | 3,331 |
| Forks | 37 | 452 |
| Open issues | 5 | 44 |
| Language | Python | Python |
| Adopt for | code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability. | human-eval is a tool designed for evaluating large language models trained specifically on code through human-written tests. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | MIT |
| Categories | Evaluation & Observability | Evaluation & Observability |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [code-eval](/tools/abacaj-code-eval.md) | [human-eval](/tools/openai-human-eval.md) |
| --- | --- | --- |
| Days since push | 1058d | 564d |
| Open issues (now) | 5 | 44 |
| Owner type | User | Organization |
| Full report | [trust report](/tools/abacaj-code-eval/trust.md) | [trust report](/tools/openai-human-eval/trust.md) |

## Shared compatibility

- **Python**: [code-eval](/tools/abacaj-code-eval.md) - Python runtime; [human-eval](/tools/openai-human-eval.md) - Python runtime

## Decision facts: code-eval

- **Adopt for:** code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability.

## Decision facts: human-eval

- **Hosting:** self hosted - This evaluation framework must be installed and set up in your own environment, ensuring full control over the testing process.
- **Pricing:** freemium - The software is available under an MIT license for free use, yet advanced features or services beyond its core functionality might incur costs.
- **Adopt for:** human-eval is a tool designed for evaluating large language models trained specifically on code through human-written tests.

## Choose when

### Choose code-eval if…

- Tags unique to code-eval: humaneval, wizardcoder.
- When you need clear comparisons of pass rates for different LLMs using standardized tests
- Leaner open-issue backlog (5).

### Choose human-eval if…

- This evaluation framework must be installed and set up in your own environment, ensuring full control over the testing process.
- Pricing: The software is available under an MIT license for free use, yet advanced features or services beyond its core functionality might incur costs..
- Tags unique to human-eval: code evaluation, large language models, python.
- When you need to evaluate the performance of AI systems that have been trained exclusively on code datasets, as it allows testing via human-created benchmarks relevant only to code-based models.

## When NOT to use code-eval

- If the tool's results do not correlate well with the official published benchmarks due to unknown prompt differences
- For real-time or dynamic evaluations as this repo offers pre-computed static results only

## When NOT to use human-eval

- If you are interested in evaluating general natural language processing tasks without coding context, as human-eval is tailored specifically for assessing code-focused AI systems.
- When the required Python version is below 3.7; this tool mandates at least Python 3.7 to ensure compatibility with its dependencies.

## Common questions

### What is the difference between code-eval and human-eval?

code-eval: Run evaluation on LLMs using human-eval benchmark.. human-eval: Evaluating Large Language Models Trained on Code. See the comparison table for live GitHub stats and shared categories.

### When should I choose code-eval over human-eval?

Choose code-eval over human-eval when Tags unique to code-eval: humaneval, wizardcoder; When you need clear comparisons of pass rates for different LLMs using standardized tests; Leaner open-issue backlog (5).

### When should I choose human-eval over code-eval?

Choose human-eval over code-eval when This evaluation framework must be installed and set up in your own environment, ensuring full control over the testing process; Pricing: The software is available under an MIT license for free use, yet advanced features or services beyond its core functionality might incur costs.; Tags unique to human-eval: code evaluation, large language models, python; When you need to evaluate the performance of AI systems that have been trained exclusively on code datasets, as it allows testing via human-created benchmarks relevant only to code-based models.

### When should I avoid code-eval?

If the tool's results do not correlate well with the official published benchmarks due to unknown prompt differences For real-time or dynamic evaluations as this repo offers pre-computed static results only

### When should I avoid human-eval?

If you are interested in evaluating general natural language processing tasks without coding context, as human-eval is tailored specifically for assessing code-focused AI systems. When the required Python version is below 3.7; this tool mandates at least Python 3.7 to ensure compatibility with its dependencies.

### Is code-eval or human-eval more popular on GitHub?

human-eval has more GitHub stars (3,331 vs 431). Stars measure visibility, not whether either tool fits your constraints.

### Are code-eval and human-eval open source?

Yes - both are open-source projects on GitHub (code-eval: MIT, human-eval: MIT).

### Where can I find alternatives to code-eval or human-eval?

GraphCanon lists graph-backed alternatives at [code-eval alternatives](/tools/abacaj-code-eval/alternatives) and [human-eval alternatives](/tools/openai-human-eval/alternatives) ([code-eval markdown twin](/tools/abacaj-code-eval/alternatives.md), [human-eval markdown twin](/tools/openai-human-eval/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/abacaj-code-eval-vs-openai-human-eval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, code-eval or human-eval?

code-eval: Dormant. human-eval: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for code-eval and human-eval?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [code-eval trust report](/tools/abacaj-code-eval/trust); [human-eval trust report](/tools/openai-human-eval/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=abacaj-code-eval`](/api/graphcanon/graph?tool=abacaj-code-eval)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_