---
title: "code-eval vs CommonGen-Eval"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/abacaj-code-eval-vs-allenai-commongen-eval"
tools: ["abacaj-code-eval", "allenai-commongen-eval"]
---

# code-eval vs CommonGen-Eval

*GraphCanon updated Oct 6, 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 CommonGen-Eval if commonGen-Eval is designed to evaluate large language models using the CommonGen-Lite dataset, focusing on generating diverse phrases and sentences.

[code-eval](https://github.com/abacaj/code-eval) reports 432 GitHub stars, 36 forks, and 5 open issues, last pushed Sep 12, 2023. [CommonGen-Eval](https://inklab.usc.edu/CommonGen/) has 95 stars, 3 forks, and 1 open issues, last pushed Mar 21, 2024. Figures are from public GitHub metadata via [code-eval's repository](https://github.com/abacaj/code-eval) and [CommonGen-Eval's repository](https://github.com/allenai/CommonGen-Eval).

| | [code-eval](/tools/abacaj-code-eval.md) | [CommonGen-Eval](/tools/allenai-commongen-eval.md) |
| --- | --- | --- |
| Tagline | Run evaluation on LLMs using human-eval benchmark. | Evaluating LLMs with CommonGen-Lite |
| Stars | 432 | 95 |
| Forks | 36 | 3 |
| Open issues | 5 | 1 |
| Language | Python | Python |
| Adopt for | code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability. | CommonGen-Eval is designed to evaluate large language models using the CommonGen-Lite dataset, focusing on generating diverse phrases and sentences. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | Apache-2.0 |
| Categories | Evaluation & Observability | Evaluation & Observability |

## Trust and health

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

| | [code-eval](/tools/abacaj-code-eval.md) | [CommonGen-Eval](/tools/allenai-commongen-eval.md) |
| --- | --- | --- |
| Days since push | 1120d | 901d |
| Open issues (now) | 5 | 1 |
| Stars delta | +1 (30d) | 0 (30d) |
| Owner type | User | Organization |
| Full report | [trust report](/tools/abacaj-code-eval/trust.md) | [trust report](/tools/allenai-commongen-eval/trust.md) |

## Shared compatibility

- **Python**: [code-eval](/tools/abacaj-code-eval.md) - Python runtime; [CommonGen-Eval](/tools/allenai-commongen-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: CommonGen-Eval

- **Requirements:** Install Python dependencies using `pip install -r requirements.txt`; Download necessary Spacy models with `python -m spacy download en_core_web_lg`
- **Adopt for:** CommonGen-Eval is designed to evaluate large language models using the CommonGen-Lite dataset, focusing on generating diverse phrases and sentences.

## Choose when

### Choose code-eval if…

- License: code-eval is MIT, CommonGen-Eval is Apache-2.0.
- Tags unique to code-eval: humaneval, wizardcoder.
- When you need clear comparisons of pass rates for different LLMs using standardized tests

### Choose CommonGen-Eval if…

- License: CommonGen-Eval is Apache-2.0, code-eval is MIT.
- Requirements: Install Python dependencies using `pip install -r requirements.txt`; Download necessary Spacy models with `python -m spacy download en_core_web_lg`.
- Tags unique to CommonGen-Eval: evaluation, llm-evaluation.
- Use CommonGen-Eval when you need to assess how well an LLM can generate a diverse set of common-sense facts or statements based on given concepts.

## 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 CommonGen-Eval

- Avoid using CommonGen-Eval if your evaluation priorities align more closely with task-specific benchmarks outside of general-language diversification.
- Do not use this tool if your project requires an evaluation framework that focuses heavily on the ability to answer specific factual questions or handle domain-specific language.

## Common questions

### What is the difference between code-eval and CommonGen-Eval?

code-eval: Run evaluation on LLMs using human-eval benchmark.. CommonGen-Eval: Evaluating LLMs with CommonGen-Lite. See the comparison table for live GitHub stats and shared categories.

### When should I choose code-eval over CommonGen-Eval?

Choose code-eval over CommonGen-Eval when License: code-eval is MIT, CommonGen-Eval is Apache-2.0; Tags unique to code-eval: humaneval, wizardcoder; When you need clear comparisons of pass rates for different LLMs using standardized tests.

### When should I choose CommonGen-Eval over code-eval?

Choose CommonGen-Eval over code-eval when License: CommonGen-Eval is Apache-2.0, code-eval is MIT; Requirements: Install Python dependencies using `pip install -r requirements.txt`; Download necessary Spacy models with `python -m spacy download en_core_web_lg`; Tags unique to CommonGen-Eval: evaluation, llm-evaluation; Use CommonGen-Eval when you need to assess how well an LLM can generate a diverse set of common-sense facts or statements based on given concepts.

### 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 CommonGen-Eval?

Avoid using CommonGen-Eval if your evaluation priorities align more closely with task-specific benchmarks outside of general-language diversification. Do not use this tool if your project requires an evaluation framework that focuses heavily on the ability to answer specific factual questions or handle domain-specific language.

### Is code-eval or CommonGen-Eval more popular on GitHub?

code-eval has more GitHub stars (432 vs 95). Stars measure visibility, not whether either tool fits your constraints.

### Are code-eval and CommonGen-Eval open source?

Yes - both are open-source projects on GitHub (code-eval: MIT, CommonGen-Eval: Apache-2.0).

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

GraphCanon lists graph-backed alternatives at [code-eval alternatives](/tools/abacaj-code-eval/alternatives) and [CommonGen-Eval alternatives](/tools/allenai-commongen-eval/alternatives) ([code-eval markdown twin](/tools/abacaj-code-eval/alternatives.md), [CommonGen-Eval markdown twin](/tools/allenai-commongen-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-allenai-commongen-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 CommonGen-Eval?

code-eval: Dormant. CommonGen-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 CommonGen-Eval?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [code-eval trust report](/tools/abacaj-code-eval/trust); [CommonGen-Eval trust report](/tools/allenai-commongen-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/_