--- title: "lighteval vs ARES" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huggingface-lighteval-vs-stanford-futuredata-ares" tools: ["huggingface-lighteval", "stanford-futuredata-ares"] --- # lighteval vs ARES *GraphCanon updated Oct 12, 2026* ## Verdict Pick lighteval if lighteval is designed for evaluating language models across multiple backends. It integrates well with Hugging Face and provides a wide range of extras, making it particularly handy in non-Windows environments; pick ARES if aRES is a Python-based tool for automated evaluation of RAG systems, offering scoring and comparison functionalities. [lighteval](https://huggingface.co/docs/lighteval/en/index) reports 2.6k GitHub stars, 576 forks, and 437 open issues, last pushed Oct 6, 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 [lighteval's repository](https://github.com/huggingface/lighteval) and [ARES's repository](https://github.com/stanford-futuredata/ARES). | | [lighteval](/tools/huggingface-lighteval.md) | [ARES](/tools/stanford-futuredata-ares.md) | | --- | --- | --- | | Tagline | All-in-one toolkit for evaluating LLMs across multiple backends | Automated Evaluation of RAG Systems | | Stars | 2,554 | 733 | | Forks | 576 | 67 | | Open issues | 437 | 22 | | Language | Python | Python | | Adopt for | Lighteval is designed for evaluating language models across multiple backends. It integrates well with Hugging Face and provides a wide range of extras, making it particularly handy in non-Windows environments. | ARES is a Python-based tool for automated evaluation of RAG systems, offering scoring and comparison functionalities. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lighteval](/tools/huggingface-lighteval.md) | [ARES](/tools/stanford-futuredata-ares.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 5d | 538d | | Open issues (now) | 437 | 22 | | Stars delta | +19 (30d) | +2 (30d) | | Open issues delta | +32 (30d) | +1 (30d) | | Full report | [trust report](/tools/huggingface-lighteval/trust.md) | [trust report](/tools/stanford-futuredata-ares/trust.md) | ## Shared compatibility - **Python**: [lighteval](/tools/huggingface-lighteval.md) - Python runtime; [ARES](/tools/stanford-futuredata-ares.md) - Python runtime ## Decision facts: lighteval - **Adopt for:** Lighteval is designed for evaluating language models across multiple backends. It integrates well with Hugging Face and provides a wide range of extras, making it particularly handy in non-Windows environments. ## 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 lighteval if… - License: lighteval is MIT, ARES is Apache-2.0. - Tags unique to lighteval: evaluation, evaluation-framework, evaluation-metrics, huggingface. - When you need to evaluate the performance of various LLMs on different backend infrastructures, especially if you are working within Mac/Linux environments. ### Choose ARES if… - License: ARES is Apache-2.0, lighteval is MIT. - 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 lighteval - Avoid Lighteval for evaluations on Windows systems as it is currently untested and not supported there. - Should you require a solution that does not integrate with or depend on the Hugging Face ecosystem, Lighteval might not fulfill your needs. ## 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 lighteval and ARES? lighteval: All-in-one toolkit for evaluating LLMs across multiple backends. ARES: Automated Evaluation of RAG Systems. See the comparison table for live GitHub stats and shared categories. ### When should I choose lighteval over ARES? Choose lighteval over ARES when License: lighteval is MIT, ARES is Apache-2.0; Tags unique to lighteval: evaluation, evaluation-framework, evaluation-metrics, huggingface; When you need to evaluate the performance of various LLMs on different backend infrastructures, especially if you are working within Mac/Linux environments. ### When should I choose ARES over lighteval? Choose ARES over lighteval when License: ARES is Apache-2.0, lighteval is MIT; 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 lighteval? Avoid Lighteval for evaluations on Windows systems as it is currently untested and not supported there. Should you require a solution that does not integrate with or depend on the Hugging Face ecosystem, Lighteval might not fulfill your needs. ### 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 lighteval or ARES more popular on GitHub? lighteval has more GitHub stars (2,554 vs 733). Stars measure visibility, not whether either tool fits your constraints. ### Are lighteval and ARES open source? Yes - both are open-source projects on GitHub (lighteval: MIT, ARES: Apache-2.0). ### Where can I find alternatives to lighteval or ARES? GraphCanon lists graph-backed alternatives at [lighteval alternatives](/tools/huggingface-lighteval/alternatives) and [ARES alternatives](/tools/stanford-futuredata-ares/alternatives) ([lighteval markdown twin](/tools/huggingface-lighteval/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/huggingface-lighteval-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, lighteval or ARES? lighteval: 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 lighteval and ARES? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lighteval trust report](/tools/huggingface-lighteval/trust); [ARES trust report](/tools/stanford-futuredata-ares/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huggingface-lighteval`](/api/graphcanon/graph?tool=huggingface-lighteval) - 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/_