--- title: "athina-evals vs HLCE" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/athina-ai-athina-evals-vs-humanity-s-last-code-exam-hlce" tools: ["athina-ai-athina-evals", "humanity-s-last-code-exam-hlce"] --- # athina-evals vs HLCE *GraphCanon updated Sep 20, 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 HLCE if hLCE offers evaluation scripts to assess code generation using LLMs, specifically for research purposes. [athina-evals](https://docs.athina.ai) reports 301 GitHub stars, 22 forks, and 4 open issues, last pushed Jun 6, 2025. [HLCE](https://humanity-s-last-code-exam.github.io/website/) has 96 stars, 8 forks, and 1 open issues, last pushed Aug 21, 2025. Figures are from public GitHub metadata via [athina-evals's repository](https://github.com/athina-ai/athina-evals) and [HLCE's repository](https://github.com/Humanity-s-Last-Code-Exam/HLCE). | | [athina-evals](/tools/athina-ai-athina-evals.md) | [HLCE](/tools/humanity-s-last-code-exam-hlce.md) | | --- | --- | --- | | Tagline | Python SDK for evaluating LLM generated responses | Source Evaluation scripts for Humanity's Last Code Exam | | Stars | 301 | 96 | | Forks | 22 | 8 | | Open issues | 4 | 1 | | 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. | HLCE offers evaluation scripts to assess code generation using LLMs, specifically for research purposes. | | Persona | - | - | | Runtime | - | - | | License | - | - | | Categories | Evaluation & Observability | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [athina-evals](/tools/athina-ai-athina-evals.md) | [HLCE](/tools/humanity-s-last-code-exam-hlce.md) | | --- | --- | --- | | Days since push | 470d | 383d | | Open issues (now) | 4 | 1 | | Open issues delta | +1 (30d) | 0 (30d) | | Full report | [trust report](/tools/athina-ai-athina-evals/trust.md) | [trust report](/tools/humanity-s-last-code-exam-hlce/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: HLCE - **Adopt for:** HLCE offers evaluation scripts to assess code generation using LLMs, specifically for research purposes. ## 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 GitHub stars (301 vs 96) - visibility, not fit. ### Choose HLCE if… - Tags unique to HLCE: benchmark, codegen, codellm. - Also covers LLM Frameworks. - When you are researching the capabilities of language models in generating code and need benchmarking tools that focus on this aspect exclusively. ## 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 HLCE - If you require tools that cater to general-purpose evaluation beyond the scope of LLM code generation in a research context. - When proprietary or non-research licenses are necessary, since HLCE does not detail its licensing beyond being for research purposes only. ## Common questions ### What is the difference between athina-evals and HLCE? athina-evals: Python SDK for evaluating LLM generated responses. HLCE: Source Evaluation scripts for Humanity's Last Code Exam. See the comparison table for live GitHub stats and shared categories. ### When should I choose athina-evals over HLCE? Choose athina-evals over HLCE 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 GitHub stars (301 vs 96) - visibility, not fit. ### When should I choose HLCE over athina-evals? Choose HLCE over athina-evals when Tags unique to HLCE: benchmark, codegen, codellm; Also covers LLM Frameworks; When you are researching the capabilities of language models in generating code and need benchmarking tools that focus on this aspect exclusively. ### 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 HLCE? If you require tools that cater to general-purpose evaluation beyond the scope of LLM code generation in a research context. When proprietary or non-research licenses are necessary, since HLCE does not detail its licensing beyond being for research purposes only. ### Is athina-evals or HLCE more popular on GitHub? athina-evals has more GitHub stars (301 vs 96). Stars measure visibility, not whether either tool fits your constraints. ### Are athina-evals and HLCE open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to athina-evals or HLCE? GraphCanon lists graph-backed alternatives at [athina-evals alternatives](/tools/athina-ai-athina-evals/alternatives) and [HLCE alternatives](/tools/humanity-s-last-code-exam-hlce/alternatives) ([athina-evals markdown twin](/tools/athina-ai-athina-evals/alternatives.md), [HLCE markdown twin](/tools/humanity-s-last-code-exam-hlce/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-humanity-s-last-code-exam-hlce.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, athina-evals or HLCE? athina-evals: Dormant. HLCE: 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 HLCE? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [athina-evals trust report](/tools/athina-ai-athina-evals/trust); [HLCE trust report](/tools/humanity-s-last-code-exam-hlce/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/_