--- title: "lmms-eval vs MixEval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/evolvinglmms-lab-lmms-eval-vs-jinjieni-mixeval" tools: ["evolvinglmms-lab-lmms-eval", "jinjieni-mixeval"] --- # lmms-eval vs MixEval *GraphCanon updated Jul 12, 2026* ## Verdict Pick lmms-eval when requirements: Min 4 GB RAM; Requires Python 3.12 or higher for installation.; Java 8 is required when testing datasets like COCO, RefCOCO, and NoCaps due to dependency on pycocoeval API.; pick MixEval when tags unique to MixEval: large language model, benchmarking-suite, benchmark-mixture, foundation models. [lmms-eval](https://www.lmms-lab.com) reports 4.3k GitHub stars, 616 forks, and 44 open issues, last pushed Jul 7, 2026. [MixEval](https://mixeval.github.io/) has 254 stars, 40 forks, and 7 open issues, last pushed Nov 10, 2024. Figures are from public GitHub metadata via [lmms-eval's repository](https://github.com/EvolvingLMMs-Lab/lmms-eval) and [MixEval's repository](https://github.com/JinjieNi/MixEval). | | [lmms-eval](/tools/evolvinglmms-lab-lmms-eval.md) | [MixEval](/tools/jinjieni-mixeval.md) | | --- | --- | --- | | Tagline | One-for-All Multimodal Evaluation Toolkit Across Text, Image, Video, and Audio Tasks | The official evaluation suite and dynamic data release for MixEval. | | Stars | 4,298 | 254 | | Forks | 616 | 40 | | Open issues | 44 | 7 | | Language | Python | Python | | Adopt for | lmms-eval is a comprehensive multimodal evaluation toolkit for large language models, enabling reproduction of LLaVA-1.5 results and supporting various datasets including text, images, videos, and audio tasks. | - | | Persona | - | - | | Runtime | - | - | | License | Other | - | | Categories | LLM Frameworks, Speech & Audio, Computer Vision | LLM Frameworks, Inference & Serving, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lmms-eval](/tools/evolvinglmms-lab-lmms-eval.md) | [MixEval](/tools/jinjieni-mixeval.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 4d | 608d | | Open issues (now) | 44 | 7 | | Owner type | Organization | User | | Security scan | No lockfile | 109 low (109 low) | | Full report | [trust report](/tools/evolvinglmms-lab-lmms-eval/trust.md) | [trust report](/tools/jinjieni-mixeval/trust.md) | ## Shared compatibility - **Python**: [lmms-eval](/tools/evolvinglmms-lab-lmms-eval.md) - Python runtime; [MixEval](/tools/jinjieni-mixeval.md) - Python runtime ## Decision facts: lmms-eval - **Requirements:** Min 4 GB RAM; Requires Python 3.12 or higher for installation.; Java 8 is required when testing datasets like COCO, RefCOCO, and NoCaps due to dependency on pycocoeval API. - **Adopt for:** lmms-eval is a comprehensive multimodal evaluation toolkit for large language models, enabling reproduction of LLaVA-1.5 results and supporting various datasets including text, images, videos, and audio tasks. ## Choose when ### Choose lmms-eval if… - Requirements: Min 4 GB RAM; Requires Python 3.12 or higher for installation.; Java 8 is required when testing datasets like COCO, RefCOCO, and NoCaps due to dependency on pycocoeval API.. - Tags unique to lmms-eval: large-language-models, multimodal-evaluation, audio-evaluation, agi. - Also covers Speech & Audio, Computer Vision. - When you need to evaluate the performance of multimodal large language models across diverse benchmarks including text, image, video, and audio. ### Choose MixEval if… - Tags unique to MixEval: large language model, benchmarking-suite, benchmark-mixture, foundation models. - Also covers Inference & Serving, Evaluation & Observability. - Leaner open-issue backlog (7). ## When NOT to use lmms-eval - If your project does not involve multimodal large language models or you are only interested in unimodal datasets. - When you need a simpler toolkit that focuses solely on text-based evaluations, as lmms-eval's extensive capabilities might be unnecessary and introduce complexity. ## When NOT to use MixEval - Last GitHub push was 609 days ago (dormant maintenance, Nov 10, 2024). Validate activity before betting a new project on MixEval. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## Common questions ### What is the difference between lmms-eval and MixEval? lmms-eval: One-for-All Multimodal Evaluation Toolkit Across Text, Image, Video, and Audio Tasks. MixEval: The official evaluation suite and dynamic data release for MixEval.. See the comparison table for live GitHub stats and shared categories. ### When should I choose lmms-eval over MixEval? Choose lmms-eval over MixEval when Requirements: Min 4 GB RAM; Requires Python 3.12 or higher for installation.; Java 8 is required when testing datasets like COCO, RefCOCO, and NoCaps due to dependency on pycocoeval API.; Tags unique to lmms-eval: large-language-models, multimodal-evaluation, audio-evaluation, agi; Also covers Speech & Audio, Computer Vision; When you need to evaluate the performance of multimodal large language models across diverse benchmarks including text, image, video, and audio. ### When should I choose MixEval over lmms-eval? Choose MixEval over lmms-eval when Tags unique to MixEval: large language model, benchmarking-suite, benchmark-mixture, foundation models; Also covers Inference & Serving, Evaluation & Observability; Leaner open-issue backlog (7). ### When should I avoid lmms-eval? If your project does not involve multimodal large language models or you are only interested in unimodal datasets. When you need a simpler toolkit that focuses solely on text-based evaluations, as lmms-eval's extensive capabilities might be unnecessary and introduce complexity. ### When should I avoid MixEval? Last GitHub push was 609 days ago (dormant maintenance, Nov 10, 2024). Validate activity before betting a new project on MixEval. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### Is lmms-eval or MixEval more popular on GitHub? lmms-eval has more GitHub stars (4,298 vs 254). Stars measure visibility, not whether either tool fits your constraints. ### Are lmms-eval and MixEval open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to lmms-eval or MixEval? GraphCanon lists graph-backed alternatives at [lmms-eval alternatives](/tools/evolvinglmms-lab-lmms-eval/alternatives) and [MixEval alternatives](/tools/jinjieni-mixeval/alternatives) ([lmms-eval markdown twin](/tools/evolvinglmms-lab-lmms-eval/alternatives.md), [MixEval markdown twin](/tools/jinjieni-mixeval/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/evolvinglmms-lab-lmms-eval-vs-jinjieni-mixeval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, lmms-eval or MixEval? lmms-eval: Very active. MixEval: 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 lmms-eval and MixEval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lmms-eval trust report](/tools/evolvinglmms-lab-lmms-eval/trust); [MixEval trust report](/tools/jinjieni-mixeval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=evolvinglmms-lab-lmms-eval`](/api/graphcanon/graph?tool=evolvinglmms-lab-lmms-eval) - 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/_