Comparison
athina-evals vs dart-math
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 dart-math if dART-Math provides sophisticated difficulty-aware rejection tuning for enhancing mathematical problem-solving capabilities of deep learning models.
Markdown twin · athina-evals alternatives · dart-math alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | athina-evals | dart-math |
|---|---|---|
| Maintenance | Dormant (417d since push) As of 3w · github_public_v1 | Dormant (595d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 3w · 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
- dart-math
- Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving
Stars
- athina-evals
- 301
- dart-math
- 120
Forks
- athina-evals
- 22
- dart-math
- 8
Open issues
- athina-evals
- 3
- dart-math
- 5
Language
- athina-evals
- Python
- dart-math
- Jupyter Notebook
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.
- dart-math
- DART-Math provides sophisticated difficulty-aware rejection tuning for enhancing mathematical problem-solving capabilities of deep learning models.
Persona
- athina-evals
- -
- dart-math
- -
Runtime
- athina-evals
- -
- dart-math
- -
License
- athina-evals
- -
- dart-math
- MIT
Last pushed
- athina-evals
- Jun 6, 2025
- dart-math
- Dec 10, 2024
Categories
- athina-evals
- Evaluation & Observability
- dart-math
- Evaluation & Observability, Inference & Serving, Model Training
Trust and health
Days since push
- athina-evals
- 417d
- dart-math
- 595d
Open issues (now)
- athina-evals
- 3
- dart-math
- 5
OSV dependency advisories
- athina-evals
- No lockfile (source not queried)
- dart-math
- No published findings from this source as of 2026-07-11
Full report
- athina-evals
- Trust report
- dart-math
- Trust report
Choose athina-evals if…
- athina-evals is primarily Python; dart-math is Jupyter Notebook.
- 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
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 dart-math if…
- dart-math is primarily Jupyter Notebook; athina-evals is Python.
- Requirements: Min 8 GB RAM; Requires a solid understanding of deep learning frameworks like TensorFlow or PyTorch; Primarily developed for Python environment with packages such as Jupyter Notebook.
- Tags unique to dart-math: deep-learning, llm, llm-inference, llm-training.
- Also covers Inference & Serving, Model Training.
- Consider DART-Math when you need to improve the performance of your model on specific mathematical problems where difficulty is a critical factor.
When NOT to use dart-math
- Avoid using DART-Math when simplicity and ease-of-implementation are prioritized over performance gains on complex mathematical problems.
- Do not use DART-Math if your application does not require fine-tuning for varying levels of difficulty in problem-solving scenarios; simpler methods may suffice.
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 (hkust-nlp/dart-math) · observed Jul 29, 2026
- GitHub forks (hkust-nlp/dart-math) · observed Jul 29, 2026
- Last push (hkust-nlp/dart-math) · observed Dec 10, 2024
- License file (MIT) · observed Jul 29, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: athina-evals 301 · dart-math 120 (synced Jul 28, 2026).
Common questions
- What is the difference between athina-evals and dart-math?
- athina-evals: Python SDK for evaluating LLM generated responses. dart-math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving. See the comparison table for live GitHub stats and shared categories.
- When should I choose athina-evals over dart-math?
- Choose athina-evals over dart-math when athina-evals is primarily Python; dart-math is Jupyter Notebook; 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.
- When should I choose dart-math over athina-evals?
- Choose dart-math over athina-evals when dart-math is primarily Jupyter Notebook; athina-evals is Python; Requirements: Min 8 GB RAM; Requires a solid understanding of deep learning frameworks like TensorFlow or PyTorch; Primarily developed for Python environment with packages such as Jupyter Notebook; Tags unique to dart-math: deep-learning, llm, llm-inference, llm-training; Also covers Inference & Serving, Model Training; Consider DART-Math when you need to improve the performance of your model on specific mathematical problems where difficulty is a critical factor.
- 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 dart-math?
- Avoid using DART-Math when simplicity and ease-of-implementation are prioritized over performance gains on complex mathematical problems. Do not use DART-Math if your application does not require fine-tuning for varying levels of difficulty in problem-solving scenarios; simpler methods may suffice.
- Is athina-evals or dart-math more popular on GitHub?
- athina-evals has more GitHub stars (301 vs 120). Stars measure visibility, not whether either tool fits your constraints.
- Are athina-evals and dart-math open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to athina-evals or dart-math?
- GraphCanon lists graph-backed alternatives at athina-evals alternatives and dart-math alternatives (athina-evals markdown twin, dart-math 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 dart-math?
- athina-evals: Dormant. dart-math: 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 dart-math?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: athina-evals trust report; dart-math trust report.