lmms-eval vs MixEval

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 Evaluation & Observability, Inference & Serving; 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. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.

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 and MixEval alternatives (lmms-eval markdown twin, MixEval markdown twin), 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 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; MixEval trust report.