--- title: "athina-evals vs human-eval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/athina-ai-athina-evals-vs-openai-human-eval" tools: ["athina-ai-athina-evals", "openai-human-eval"] --- # athina-evals vs human-eval *GraphCanon updated Aug 5, 2026* ## Verdict 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; pick human-eval if human-eval is a tool designed for evaluating large language models trained specifically on code through human-written tests. [athina-evals](https://docs.athina.ai) reports 301 GitHub stars, 22 forks, and 3 open issues, last pushed Jun 6, 2025. [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 [athina-evals's repository](https://github.com/athina-ai/athina-evals) and [human-eval's repository](https://github.com/openai/human-eval). | | [athina-evals](/tools/athina-ai-athina-evals.md) | [human-eval](/tools/openai-human-eval.md) | | --- | --- | --- | | Tagline | Python SDK for evaluating LLM generated responses | Evaluating Large Language Models Trained on Code | | Stars | 301 | 3,331 | | Forks | 22 | 452 | | Open issues | 3 | 44 | | Language | Python | Python | | Adopt for | athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks. | human-eval is a tool designed for evaluating large language models trained specifically on code through human-written tests. | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [athina-evals](/tools/athina-ai-athina-evals.md) | [human-eval](/tools/openai-human-eval.md) | | --- | --- | --- | | Days since push | 417d | 564d | | Open issues (now) | 3 | 44 | | Full report | [trust report](/tools/athina-ai-athina-evals/trust.md) | [trust report](/tools/openai-human-eval/trust.md) | ## 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. ## 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 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). ### 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 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 ## 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 athina-evals and human-eval? athina-evals: Python SDK for evaluating LLM generated responses. human-eval: Evaluating Large Language Models Trained on Code. See the comparison table for live GitHub stats and shared categories. ### When should I choose athina-evals over human-eval? Choose athina-evals over human-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 choose human-eval over athina-evals? Choose human-eval over athina-evals 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 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 ### 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 athina-evals or human-eval more popular on GitHub? human-eval has more GitHub stars (3,331 vs 301). Stars measure visibility, not whether either tool fits your constraints. ### Are athina-evals and human-eval open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to athina-evals or human-eval? GraphCanon lists graph-backed alternatives at [athina-evals alternatives](/tools/athina-ai-athina-evals/alternatives) and [human-eval alternatives](/tools/openai-human-eval/alternatives) ([athina-evals markdown twin](/tools/athina-ai-athina-evals/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/athina-ai-athina-evals-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, athina-evals or human-eval? athina-evals: 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 athina-evals and human-eval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [athina-evals trust report](/tools/athina-ai-athina-evals/trust); [human-eval trust report](/tools/openai-human-eval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=athina-ai-athina-evals`](/api/graphcanon/graph?tool=athina-ai-athina-evals) - 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/_