--- title: "LLMEvaluation vs ARES" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alopatenko-llmevaluation-vs-stanford-futuredata-ares" tools: ["alopatenko-llmevaluation", "stanford-futuredata-ares"] --- # LLMEvaluation vs ARES *GraphCanon updated Sep 20, 2026* ## Verdict Pick LLMEvaluation if lLMEvaluation offers a detailed guide to evaluating large language models with specific methods and theories, aiming to improve model assessment practices; pick ARES if aRES is a Python-based tool for automated evaluation of RAG systems, offering scoring and comparison functionalities. [LLMEvaluation](https://alopatenko.github.io/LLMEvaluation/) reports 201 GitHub stars, 24 forks, and 2 open issues, last pushed Sep 15, 2026. [ARES](https://ares-ai.vercel.app/) has 733 stars, 67 forks, and 22 open issues, last pushed Mar 28, 2025. Figures are from public GitHub metadata via [LLMEvaluation's repository](https://github.com/alopatenko/LLMEvaluation) and [ARES's repository](https://github.com/stanford-futuredata/ARES). | | [LLMEvaluation](/tools/alopatenko-llmevaluation.md) | [ARES](/tools/stanford-futuredata-ares.md) | | --- | --- | --- | | Tagline | A comprehensive guide to LLM evaluation methods | Automated Evaluation of RAG Systems | | Stars | 201 | 733 | | Forks | 24 | 67 | | Open issues | 2 | 22 | | Language | HTML | Python | | Adopt for | LLMEvaluation offers a detailed guide to evaluating large language models with specific methods and theories, aiming to improve model assessment practices. | ARES is a Python-based tool for automated evaluation of RAG systems, offering scoring and comparison functionalities. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMEvaluation](/tools/alopatenko-llmevaluation.md) | [ARES](/tools/stanford-futuredata-ares.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 5d | 538d | | Open issues (now) | 2 | 22 | | Stars delta | +5 (30d) | +2 (30d) | | Open issues delta | -2 (30d) | +1 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/alopatenko-llmevaluation/trust.md) | [trust report](/tools/stanford-futuredata-ares/trust.md) | ## Decision facts: LLMEvaluation - **Adopt for:** LLMEvaluation offers a detailed guide to evaluating large language models with specific methods and theories, aiming to improve model assessment practices. ## Decision facts: ARES - **Requirements:** Requires over 100 GB of available disk space and a compatible GPU like the A100.; Installation of conda and gcc is necessary for setup. - **Adopt for:** ARES is a Python-based tool for automated evaluation of RAG systems, offering scoring and comparison functionalities. ## Choose when ### Choose LLMEvaluation if… - LLMEvaluation is primarily HTML; ARES is Python. - Tags unique to LLMEvaluation: evaluation, generative-ai-benchmarking, llm, llm-benchmarking. - When developing custom evaluation procedures for LLMs tailored to niche applications or industries requiring specialized assessments ### Choose ARES if… - ARES is primarily Python; LLMEvaluation is HTML. - Requirements: Requires over 100 GB of available disk space and a compatible GPU like the A100.; Installation of conda and gcc is necessary for setup.. - Tags unique to ARES: api integration, automated evaluation, comparison, evaluation criteria. - Use ARES when you need to evaluate RAG systems with specific criteria such as context relevance, answer faithfulness, and answer relevance. ## When NOT to use LLMEvaluation - If you seek ready-to-use software solutions rather than guidance on how to evaluate and improve your model's effectiveness - When looking for real-time monitoring tools; LLMEvaluation focuses more on theoretical frameworks and established practices than dynamic tooling ## When NOT to use ARES - Do not use ARES if you lack the necessary human-annotated datasets for evaluation criteria or if you do not have a large set of unlabeled triples for scoring. - Avoid ARES if your machine does not meet the requirements, such as having over 100 GB of available disk space and a compatible GPU like the A100. - Do not use ARES if you cannot set up the required environment, including conda and gcc, on your machine. ## Common questions ### What is the difference between LLMEvaluation and ARES? LLMEvaluation: A comprehensive guide to LLM evaluation methods. ARES: Automated Evaluation of RAG Systems. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLMEvaluation over ARES? Choose LLMEvaluation over ARES when LLMEvaluation is primarily HTML; ARES is Python; Tags unique to LLMEvaluation: evaluation, generative-ai-benchmarking, llm, llm-benchmarking; When developing custom evaluation procedures for LLMs tailored to niche applications or industries requiring specialized assessments. ### When should I choose ARES over LLMEvaluation? Choose ARES over LLMEvaluation when ARES is primarily Python; LLMEvaluation is HTML; Requirements: Requires over 100 GB of available disk space and a compatible GPU like the A100.; Installation of conda and gcc is necessary for setup.; Tags unique to ARES: api integration, automated evaluation, comparison, evaluation criteria; Use ARES when you need to evaluate RAG systems with specific criteria such as context relevance, answer faithfulness, and answer relevance. ### When should I avoid LLMEvaluation? If you seek ready-to-use software solutions rather than guidance on how to evaluate and improve your model's effectiveness When looking for real-time monitoring tools; LLMEvaluation focuses more on theoretical frameworks and established practices than dynamic tooling ### When should I avoid ARES? Do not use ARES if you lack the necessary human-annotated datasets for evaluation criteria or if you do not have a large set of unlabeled triples for scoring. Avoid ARES if your machine does not meet the requirements, such as having over 100 GB of available disk space and a compatible GPU like the A100. Do not use ARES if you cannot set up the required environment, including conda and gcc, on your machine. ### Is LLMEvaluation or ARES more popular on GitHub? ARES has more GitHub stars (733 vs 201). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMEvaluation and ARES open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to LLMEvaluation or ARES? GraphCanon lists graph-backed alternatives at [LLMEvaluation alternatives](/tools/alopatenko-llmevaluation/alternatives) and [ARES alternatives](/tools/stanford-futuredata-ares/alternatives) ([LLMEvaluation markdown twin](/tools/alopatenko-llmevaluation/alternatives.md), [ARES markdown twin](/tools/stanford-futuredata-ares/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/alopatenko-llmevaluation-vs-stanford-futuredata-ares.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLMEvaluation or ARES? LLMEvaluation: Very active. ARES: 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 LLMEvaluation and ARES? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMEvaluation trust report](/tools/alopatenko-llmevaluation/trust); [ARES trust report](/tools/stanford-futuredata-ares/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alopatenko-llmevaluation`](/api/graphcanon/graph?tool=alopatenko-llmevaluation) - 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/_