Comparison
athina-evals vs VLMEvalKit
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 VLMEvalKit if vLMEvalKit is an open-source Python evaluation toolkit for large vision-language models that offers one-command evaluation with support for various benchmarks and models.
Markdown twin · athina-evals alternatives · VLMEvalKit alternatives
GraphCanon updated Sep 20, 2026
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Trust & integrity
| Signal | athina-evals | VLMEvalKit |
|---|---|---|
| Maintenance | Dormant (470d since push) As of Sep 20, 2026 · github_public_v1 | Very active (0d since push) As of Aug 17, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Aug 17, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings As of Jul 11, 2026 · 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
- VLMEvalKit
- An open-source evaluation toolkit for large vision-language models
Stars
- athina-evals
- 301
- VLMEvalKit
- 4.3k
Forks
- athina-evals
- 22
- VLMEvalKit
- 745
Open issues
- athina-evals
- 4
- VLMEvalKit
- 285
Language
- athina-evals
- Python
- VLMEvalKit
- 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.
- VLMEvalKit
- VLMEvalKit is an open-source Python evaluation toolkit for large vision-language models that offers one-command evaluation with support for various benchmarks and models.
Persona
- athina-evals
- -
- VLMEvalKit
- -
Runtime
- athina-evals
- -
- VLMEvalKit
- -
License
- athina-evals
- -
- VLMEvalKit
- Apache-2.0
Last pushed
- athina-evals
- Jun 6, 2025
- VLMEvalKit
- Aug 17, 2026
Categories
- athina-evals
- Evaluation & Observability
- VLMEvalKit
- Evaluation & Observability
Trust and health
Maintenance
- athina-evals
- Dormant (18%)
- VLMEvalKit
- Very active (96%)
Days since push
- athina-evals
- 470d
- VLMEvalKit
- 0d
Open issues (now)
- athina-evals
- 4
- VLMEvalKit
- 285
Stars delta
- athina-evals
- 0 (30d)
- VLMEvalKit
- +60 (30d)
Open issues delta
- athina-evals
- +1 (30d)
- VLMEvalKit
- +21 (30d)
OSV dependency advisories
- athina-evals
- No lockfile (source not queried)
- VLMEvalKit
- Published findings
Full report
- athina-evals
- Trust report
- VLMEvalKit
- Trust report
Choose athina-evals if…
- Tags unique to athina-evals: evaluation-framework, evaluation-metrics, llm-eval, llm-evaluation.
- When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics
- Leaner open-issue backlog (4).
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 VLMEvalKit if…
- Tags unique to VLMEvalKit: computer-vision, large-language-models, llm, multi-modal.
- When you need to evaluate models supporting thinking mode, as it provides a custom split_thinking function improving accuracy.
- More GitHub stars (4.3k vs 301) - visibility, not fit.
When NOT to use VLMEvalKit
- If your project requires evaluation tools that generate Excel files with individual cells larger than the default support of 32,767 characters and cannot switch to TSV format.
- When you do not need generation-based evaluation methods with exact matching and LLM-based answer extraction.
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 Sep 20, 2026
- GitHub forks (athina-ai/athina-evals) · observed Sep 20, 2026
- Last push (athina-ai/athina-evals) · observed Jun 6, 2025
- License file (unknown) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (open-compass/VLMEvalKit) · observed Sep 20, 2026
- GitHub forks (open-compass/VLMEvalKit) · observed Sep 20, 2026
- Last push (open-compass/VLMEvalKit) · observed Aug 17, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: athina-evals 301 · VLMEvalKit 4.3k (synced Sep 20, 2026).
Common questions
- What is the difference between athina-evals and VLMEvalKit?
- athina-evals: Python SDK for evaluating LLM generated responses. VLMEvalKit: An open-source evaluation toolkit for large vision-language models. See the comparison table for live GitHub stats and shared categories.
- When should I choose athina-evals over VLMEvalKit?
- Choose athina-evals over VLMEvalKit when Tags unique to athina-evals: evaluation-framework, evaluation-metrics, llm-eval, llm-evaluation; When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics; Leaner open-issue backlog (4).
- When should I choose VLMEvalKit over athina-evals?
- Choose VLMEvalKit over athina-evals when Tags unique to VLMEvalKit: computer-vision, large-language-models, llm, multi-modal; When you need to evaluate models supporting thinking mode, as it provides a custom split_thinking function improving accuracy; More GitHub stars (4.3k vs 301) - visibility, not fit.
- 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 VLMEvalKit?
- If your project requires evaluation tools that generate Excel files with individual cells larger than the default support of 32,767 characters and cannot switch to TSV format. When you do not need generation-based evaluation methods with exact matching and LLM-based answer extraction.
- Is athina-evals or VLMEvalKit more popular on GitHub?
- VLMEvalKit has more GitHub stars (4,345 vs 301). Stars measure visibility, not whether either tool fits your constraints.
- Are athina-evals and VLMEvalKit open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to athina-evals or VLMEvalKit?
- GraphCanon lists graph-backed alternatives at athina-evals alternatives and VLMEvalKit alternatives (athina-evals markdown twin, VLMEvalKit 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 VLMEvalKit?
- athina-evals: Dormant. VLMEvalKit: Very active. 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 VLMEvalKit?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: athina-evals trust report; VLMEvalKit trust report.