--- title: "code-eval vs evals" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/abacaj-code-eval-vs-openai-evals" tools: ["abacaj-code-eval", "openai-evals"] --- # code-eval vs evals *GraphCanon updated Aug 7, 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 evals if evals is an evaluation framework from OpenAI for assessing large language models and systems built with them. It includes an open-source registry of benchmarks and tools to create custom evaluations. [code-eval](https://github.com/abacaj/code-eval) reports 431 GitHub stars, 37 forks, and 5 open issues, last pushed Sep 12, 2023. [evals](https://github.com/openai/evals) has 19k stars, 3.0k forks, and 213 open issues, last pushed Apr 14, 2026. Figures are from public GitHub metadata via [code-eval's repository](https://github.com/abacaj/code-eval) and [evals's repository](https://github.com/openai/evals). | | [code-eval](/tools/abacaj-code-eval.md) | [evals](/tools/openai-evals.md) | | --- | --- | --- | | Tagline | Run evaluation on LLMs using human-eval benchmark. | Framework for evaluating LLMs and LLM systems with an open-source registry of benchmarks. | | Stars | 431 | 19,127 | | Forks | 37 | 3,050 | | Open issues | 5 | 213 | | Language | Python | Python | | Adopt for | code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability. | Evals is an evaluation framework from OpenAI for assessing large language models and systems built with them. It includes an open-source registry of benchmarks and tools to create custom evaluations. | | Persona | - | - | | Runtime | - | - | | License | MIT | Other | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [code-eval](/tools/abacaj-code-eval.md) | [evals](/tools/openai-evals.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1058d | 115d | | Open issues (now) | 5 | 213 | | Owner type | User | Organization | | Full report | [trust report](/tools/abacaj-code-eval/trust.md) | [trust report](/tools/openai-evals/trust.md) | ## Shared compatibility - **Python**: [code-eval](/tools/abacaj-code-eval.md) - Python runtime; [evals](/tools/openai-evals.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: evals - **Adopt for:** Evals is an evaluation framework from OpenAI for assessing large language models and systems built with them. It includes an open-source registry of benchmarks and tools to create custom evaluations. ## Choose when ### Choose code-eval if… - License: code-eval is MIT, evals is Other. - Tags unique to code-eval: humaneval, wizardcoder. - When you need clear comparisons of pass rates for different LLMs using standardized tests ### Choose evals if… - License: evals is Other, code-eval is MIT. - Tags unique to evals: benchmarking, custom eval creation, evaluation-framework, large language models. - * When you need a comprehensive set of pre-existing evals and the ability to create your own tailored tests using specific use cases, especially within the OpenAI model ecosystem. ## 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 evals - * When evaluating models or systems that do not benefit from being integrated with the OpenAI API, as some features like direct evals configuration in the OpenAI Dashboard require an OpenAI key. - * If you are looking for an evaluation framework that doesn’t involve external dependencies such as Git Large File Storage (LFS) and specific Python version requirements (Python 3.9 minimum), or if a ## Common questions ### What is the difference between code-eval and evals? code-eval: Run evaluation on LLMs using human-eval benchmark.. evals: Framework for evaluating LLMs and LLM systems with an open-source registry of benchmarks.. See the comparison table for live GitHub stats and shared categories. ### When should I choose code-eval over evals? Choose code-eval over evals when License: code-eval is MIT, evals is Other; 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 evals over code-eval? Choose evals over code-eval when License: evals is Other, code-eval is MIT; Tags unique to evals: benchmarking, custom eval creation, evaluation-framework, large language models; * When you need a comprehensive set of pre-existing evals and the ability to create your own tailored tests using specific use cases, especially within the OpenAI model ecosystem. ### 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 evals? * When evaluating models or systems that do not benefit from being integrated with the OpenAI API, as some features like direct evals configuration in the OpenAI Dashboard require an OpenAI key. * If you are looking for an evaluation framework that doesn’t involve external dependencies such as Git Large File Storage (LFS) and specific Python version requirements (Python 3.9 minimum), or if a ### Is code-eval or evals more popular on GitHub? evals has more GitHub stars (19,127 vs 431). Stars measure visibility, not whether either tool fits your constraints. ### Are code-eval and evals open source? Yes - both are open-source projects on GitHub (code-eval: MIT, evals: Other). ### Where can I find alternatives to code-eval or evals? GraphCanon lists graph-backed alternatives at [code-eval alternatives](/tools/abacaj-code-eval/alternatives) and [evals alternatives](/tools/openai-evals/alternatives) ([code-eval markdown twin](/tools/abacaj-code-eval/alternatives.md), [evals markdown twin](/tools/openai-evals/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-evals.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, code-eval or evals? code-eval: Dormant. evals: Slowing. 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 evals? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [code-eval trust report](/tools/abacaj-code-eval/trust); [evals trust report](/tools/openai-evals/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/_