--- title: "deepeval vs lmms-eval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/confident-ai-deepeval-vs-evolvinglmms-lab-lmms-eval" tools: ["confident-ai-deepeval", "evolvinglmms-lab-lmms-eval"] --- # deepeval vs lmms-eval *GraphCanon updated Sep 20, 2026* ## Verdict Pick deepeval if deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies; pick lmms-eval if lmms-eval is a one-stop solution for benchmarking multimodal large language models across various tasks including text, image, video, and audio. [deepeval](https://deepeval.com) reports 18k GitHub stars, 2.0k forks, and 624 open issues, last pushed Sep 18, 2026. [lmms-eval](https://www.lmms-lab.com) has 4.4k stars, 639 forks, and 49 open issues, last pushed Aug 6, 2026. Figures are from public GitHub metadata via [deepeval's repository](https://github.com/confident-ai/deepeval) and [lmms-eval's repository](https://github.com/EvolvingLMMs-Lab/lmms-eval). | | [deepeval](/tools/confident-ai-deepeval.md) | [lmms-eval](/tools/evolvinglmms-lab-lmms-eval.md) | | --- | --- | --- | | Tagline | LLM Evaluation Framework. | One-for-All Multimodal Evaluation Toolkit Across Text, Image, Video, and Audio Tasks | | Stars | 18,342 | 4,368 | | Forks | 1,953 | 639 | | Open issues | 624 | 49 | | Language | Python | Python | | Adopt for | Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. | lmms-eval is a one-stop solution for benchmarking multimodal large language models across various tasks including text, image, video, and audio. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License | Other | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deepeval](/tools/confident-ai-deepeval.md) | [lmms-eval](/tools/evolvinglmms-lab-lmms-eval.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 1d | 11d | | Open issues (now) | 624 | 49 | | Stars delta | +1.1k (30d) | +52 (30d) | | Open issues delta | +220 (30d) | +9 (30d) | | Full report | [trust report](/tools/confident-ai-deepeval/trust.md) | [trust report](/tools/evolvinglmms-lab-lmms-eval/trust.md) | ## Shared compatibility - **Python**: [deepeval](/tools/confident-ai-deepeval.md) - Python runtime; [lmms-eval](/tools/evolvinglmms-lab-lmms-eval.md) - Python runtime ## Decision facts: deepeval - **Requirements:** Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities. - **Adopt for:** Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. - **License detail:** Apache-2.0 License ## Decision facts: lmms-eval - **Adopt for:** lmms-eval is a one-stop solution for benchmarking multimodal large language models across various tasks including text, image, video, and audio. ## Choose when ### Choose deepeval if… - License: deepeval is Apache-2.0, lmms-eval is Other. - Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.. - Tags unique to deepeval: metrics. - When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ### Choose lmms-eval if… - License: lmms-eval is Other, deepeval is Apache-2.0. - Tags unique to lmms-eval: agi, audio-evaluation, benchmark, large-language-models. - You need to evaluate LLaVA series models on different datasets with precise control over reproducibility details like torch/cuda versions. ## When NOT to use deepeval - For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. - In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis. ## When NOT to use lmms-eval - Looking for a tool that supports less than Python 3.12, as uv setup mandates this version. - Requiring support beyond text, image, video, and audio modalities which lmms-eval specifically covers. - Your project doesn't benefit from extensive results tracking in Google Sheets or relies solely on alternative reproducibility mechanisms without external dependencies. ## Common questions ### What is the difference between deepeval and lmms-eval? deepeval: LLM Evaluation Framework.. lmms-eval: One-for-All Multimodal Evaluation Toolkit Across Text, Image, Video, and Audio Tasks. See the comparison table for live GitHub stats and shared categories. ### When should I choose deepeval over lmms-eval? Choose deepeval over lmms-eval when License: deepeval is Apache-2.0, lmms-eval is Other; Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.; Tags unique to deepeval: metrics; When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ### When should I choose lmms-eval over deepeval? Choose lmms-eval over deepeval when License: lmms-eval is Other, deepeval is Apache-2.0; Tags unique to lmms-eval: agi, audio-evaluation, benchmark, large-language-models; You need to evaluate LLaVA series models on different datasets with precise control over reproducibility details like torch/cuda versions. ### When should I avoid deepeval? For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis. ### When should I avoid lmms-eval? Looking for a tool that supports less than Python 3.12, as uv setup mandates this version. Requiring support beyond text, image, video, and audio modalities which lmms-eval specifically covers. Your project doesn't benefit from extensive results tracking in Google Sheets or relies solely on alternative reproducibility mechanisms without external dependencies. ### Is deepeval or lmms-eval more popular on GitHub? deepeval has more GitHub stars (18,342 vs 4,368). Stars measure visibility, not whether either tool fits your constraints. ### Are deepeval and lmms-eval open source? Yes - both are open-source projects on GitHub (deepeval: Apache-2.0, lmms-eval: Other). ### Where can I find alternatives to deepeval or lmms-eval? GraphCanon lists graph-backed alternatives at [deepeval alternatives](/tools/confident-ai-deepeval/alternatives) and [lmms-eval alternatives](/tools/evolvinglmms-lab-lmms-eval/alternatives) ([deepeval markdown twin](/tools/confident-ai-deepeval/alternatives.md), [lmms-eval markdown twin](/tools/evolvinglmms-lab-lmms-eval/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/confident-ai-deepeval-vs-evolvinglmms-lab-lmms-eval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deepeval or lmms-eval? deepeval: Very active. lmms-eval: 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 deepeval and lmms-eval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deepeval trust report](/tools/confident-ai-deepeval/trust); [lmms-eval trust report](/tools/evolvinglmms-lab-lmms-eval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=confident-ai-deepeval`](/api/graphcanon/graph?tool=confident-ai-deepeval) - 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/_