--- title: "athina-evals vs dart-math" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/athina-ai-athina-evals-vs-hkust-nlp-dart-math" tools: ["athina-ai-athina-evals", "hkust-nlp-dart-math"] --- # athina-evals vs dart-math *GraphCanon updated Jul 29, 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 dart-math if dART-Math provides sophisticated difficulty-aware rejection tuning for enhancing mathematical problem-solving capabilities of deep learning models. [athina-evals](https://docs.athina.ai) reports 301 GitHub stars, 22 forks, and 3 open issues, last pushed Jun 6, 2025. [dart-math](https://hkust-nlp.github.io/dart-math/) has 120 stars, 8 forks, and 5 open issues, last pushed Dec 10, 2024. Figures are from public GitHub metadata via [athina-evals's repository](https://github.com/athina-ai/athina-evals) and [dart-math's repository](https://github.com/hkust-nlp/dart-math). | | [athina-evals](/tools/athina-ai-athina-evals.md) | [dart-math](/tools/hkust-nlp-dart-math.md) | | --- | --- | --- | | Tagline | Python SDK for evaluating LLM generated responses | Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving | | Stars | 301 | 120 | | Forks | 22 | 8 | | Open issues | 3 | 5 | | Language | Python | Jupyter Notebook | | Adopt for | athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks. | DART-Math provides sophisticated difficulty-aware rejection tuning for enhancing mathematical problem-solving capabilities of deep learning models. | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | Categories | Evaluation & Observability | Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [athina-evals](/tools/athina-ai-athina-evals.md) | [dart-math](/tools/hkust-nlp-dart-math.md) | | --- | --- | --- | | Days since push | 417d | 595d | | Open issues (now) | 3 | 5 | | Full report | [trust report](/tools/athina-ai-athina-evals/trust.md) | [trust report](/tools/hkust-nlp-dart-math/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: dart-math - **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 - **Adopt for:** DART-Math provides sophisticated difficulty-aware rejection tuning for enhancing mathematical problem-solving capabilities of deep learning models. ## Choose when ### 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 ### 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 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 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. ## 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](/tools/athina-ai-athina-evals/alternatives) and [dart-math alternatives](/tools/hkust-nlp-dart-math/alternatives) ([athina-evals markdown twin](/tools/athina-ai-athina-evals/alternatives.md), [dart-math markdown twin](/tools/hkust-nlp-dart-math/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-hkust-nlp-dart-math.md) 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](/tools/athina-ai-athina-evals/trust); [dart-math trust report](/tools/hkust-nlp-dart-math/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/_