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

# deepeval vs human-eval

*GraphCanon updated Aug 5, 2026*

## Verdict

Pick deepeval if deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies; pick human-eval if human-eval is a tool designed for evaluating large language models trained specifically on code through human-written tests.

[deepeval](https://deepeval.com) reports 17k GitHub stars, 1.7k forks, and 404 open issues, last pushed Jul 27, 2026. [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 [deepeval's repository](https://github.com/confident-ai/deepeval) and [human-eval's repository](https://github.com/openai/human-eval).

| | [deepeval](/tools/confident-ai-deepeval.md) | [human-eval](/tools/openai-human-eval.md) |
| --- | --- | --- |
| Tagline | LLM Evaluation Framework. | Evaluating Large Language Models Trained on Code |
| Stars | 17,226 | 3,331 |
| Forks | 1,736 | 452 |
| Open issues | 404 | 44 |
| Language | Python | Python |
| Adopt for | Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. | human-eval is a tool designed for evaluating large language models trained specifically on code through human-written tests. |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 License | MIT |
| Categories | Evaluation & Observability | Evaluation & Observability |

## Trust and health

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

| | [deepeval](/tools/confident-ai-deepeval.md) | [human-eval](/tools/openai-human-eval.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Dormant (18%) |
| Days since push | 1d | 564d |
| Open issues (now) | 404 | 44 |
| Full report | [trust report](/tools/confident-ai-deepeval/trust.md) | [trust report](/tools/openai-human-eval/trust.md) |

## Shared compatibility

- **Python**: [deepeval](/tools/confident-ai-deepeval.md) - Python runtime; [human-eval](/tools/openai-human-eval.md) - Python runtime

## Decision facts: deepeval

- **Requirements:** Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.
- **Adopt for:** Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies.
- **License detail:** Apache-2.0 License

## 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 deepeval if…

- License: deepeval is Apache-2.0, human-eval is MIT.
- Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities..
- Tags unique to deepeval: evaluation, llm-evaluation, metrics.
- When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics.

### Choose human-eval if…

- License: human-eval is MIT, deepeval is Apache-2.0.
- 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 deepeval

- For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill.
- In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis.

## 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 deepeval and human-eval?

deepeval: LLM Evaluation Framework.. human-eval: Evaluating Large Language Models Trained on Code. See the comparison table for live GitHub stats and shared categories.

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

Choose deepeval over human-eval when License: deepeval is Apache-2.0, human-eval is MIT; Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.; Tags unique to deepeval: evaluation, llm-evaluation, metrics; When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics.

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

Choose human-eval over deepeval when License: human-eval is MIT, deepeval is Apache-2.0; 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 deepeval?

For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis.

### 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 deepeval or human-eval more popular on GitHub?

deepeval has more GitHub stars (17,226 vs 3,331). Stars measure visibility, not whether either tool fits your constraints.

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

Yes - both are open-source projects on GitHub (deepeval: Apache-2.0, human-eval: MIT).

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

GraphCanon lists graph-backed alternatives at [deepeval alternatives](/tools/confident-ai-deepeval/alternatives) and [human-eval alternatives](/tools/openai-human-eval/alternatives) ([deepeval markdown twin](/tools/confident-ai-deepeval/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/confident-ai-deepeval-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, deepeval or human-eval?

deepeval: Very active. 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 deepeval and human-eval?

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

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=confident-ai-deepeval`](/api/graphcanon/graph?tool=confident-ai-deepeval)
- 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/_