Comparison
athina-evals vs human-eval
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.
Markdown twin · athina-evals alternatives · human-eval alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | athina-evals | human-eval |
|---|---|---|
| Maintenance | Dormant (417d since push) As of 3w · github_public_v1 | Dormant (564d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No published findings from this source as of 2026-07-11 As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- athina-evals
- Python SDK for evaluating LLM generated responses
- human-eval
- Evaluating Large Language Models Trained on Code
Stars
- athina-evals
- 301
- human-eval
- 3.3k
Forks
- athina-evals
- 22
- human-eval
- 452
Open issues
- athina-evals
- 3
- human-eval
- 44
Language
- athina-evals
- Python
- human-eval
- Python
Adopt for
- athina-evals
- athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks.
- human-eval
- human-eval is a tool designed for evaluating large language models trained specifically on code through human-written tests.
Persona
- athina-evals
- -
- human-eval
- -
Runtime
- athina-evals
- -
- human-eval
- -
License
- athina-evals
- -
- human-eval
- MIT
Last pushed
- athina-evals
- Jun 6, 2025
- human-eval
- Jan 17, 2025
Categories
- athina-evals
- Evaluation & Observability
- human-eval
- Evaluation & Observability
Trust and health
Days since push
- athina-evals
- 417d
- human-eval
- 564d
Open issues (now)
- athina-evals
- 3
- human-eval
- 44
OSV dependency advisories
- athina-evals
- No lockfile (source not queried)
- human-eval
- No published findings from this source as of 2026-07-11
Full report
- athina-evals
- Trust report
- human-eval
- Trust report
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 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
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (athina-ai/athina-evals) · observed Jul 28, 2026
- GitHub forks (athina-ai/athina-evals) · observed Jul 28, 2026
- Last push (athina-ai/athina-evals) · observed Jun 6, 2025
- License file (unknown) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (openai/human-eval) · observed Aug 5, 2026
- GitHub forks (openai/human-eval) · observed Aug 5, 2026
- Last push (openai/human-eval) · observed Jan 17, 2025
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: athina-evals 301 · human-eval 3.3k (synced Jul 28, 2026).
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 and human-eval alternatives (athina-evals markdown twin, human-eval markdown twin), 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 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; human-eval trust report.