--- title: "code-eval vs athina-evals" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/abacaj-code-eval-vs-athina-ai-athina-evals" tools: ["abacaj-code-eval", "athina-ai-athina-evals"] --- # code-eval vs athina-evals *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 athina-evals if athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks. [code-eval](https://github.com/abacaj/code-eval) reports 431 GitHub stars, 37 forks, and 5 open issues, last pushed Sep 12, 2023. [athina-evals](https://docs.athina.ai) has 301 stars, 22 forks, and 3 open issues, last pushed Jun 6, 2025. Figures are from public GitHub metadata via [code-eval's repository](https://github.com/abacaj/code-eval) and [athina-evals's repository](https://github.com/athina-ai/athina-evals). | | [code-eval](/tools/abacaj-code-eval.md) | [athina-evals](/tools/athina-ai-athina-evals.md) | | --- | --- | --- | | Tagline | Run evaluation on LLMs using human-eval benchmark. | Python SDK for evaluating LLM generated responses | | Stars | 431 | 301 | | Forks | 37 | 22 | | Open issues | 5 | 3 | | Language | Python | Python | | Adopt for | code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability. | athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks. | | Persona | - | - | | Runtime | - | - | | License | 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) | [athina-evals](/tools/athina-ai-athina-evals.md) | | --- | --- | --- | | Days since push | 1058d | 417d | | Open issues (now) | 5 | 3 | | Owner type | User | Organization | | Full report | [trust report](/tools/abacaj-code-eval/trust.md) | [trust report](/tools/athina-ai-athina-evals/trust.md) | ## 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: athina-evals - **Adopt for:** athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks. ## 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 - More GitHub stars (431 vs 301) - visibility, not fit. ### Choose athina-evals if… - Tags unique to athina-evals: evaluation, evaluation-framework, evaluation-metrics, llm-eval. - When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics - More recently updated (last pushed Jun 6, 2025). ## 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 athina-evals - If open-source alternatives with transparent customization options are preferred over athina-evals' approach - In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments ## Common questions ### What is the difference between code-eval and athina-evals? code-eval: Run evaluation on LLMs using human-eval benchmark.. athina-evals: Python SDK for evaluating LLM generated responses. See the comparison table for live GitHub stats and shared categories. ### When should I choose code-eval over athina-evals? Choose code-eval over athina-evals when Tags unique to code-eval: humaneval, wizardcoder; When you need clear comparisons of pass rates for different LLMs using standardized tests; More GitHub stars (431 vs 301) - visibility, not fit. ### When should I choose athina-evals over code-eval? Choose athina-evals over code-eval when Tags unique to athina-evals: evaluation, evaluation-framework, evaluation-metrics, llm-eval; When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics; More recently updated (last pushed Jun 6, 2025). ### 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 athina-evals? If open-source alternatives with transparent customization options are preferred over athina-evals' approach In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments ### Is code-eval or athina-evals more popular on GitHub? code-eval has more GitHub stars (431 vs 301). Stars measure visibility, not whether either tool fits your constraints. ### Are code-eval and athina-evals open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to code-eval or athina-evals? GraphCanon lists graph-backed alternatives at [code-eval alternatives](/tools/abacaj-code-eval/alternatives) and [athina-evals alternatives](/tools/athina-ai-athina-evals/alternatives) ([code-eval markdown twin](/tools/abacaj-code-eval/alternatives.md), [athina-evals markdown twin](/tools/athina-ai-athina-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-athina-ai-athina-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 athina-evals? code-eval: Dormant. athina-evals: 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 athina-evals? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [code-eval trust report](/tools/abacaj-code-eval/trust); [athina-evals trust report](/tools/athina-ai-athina-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/_