lmms-eval vs VLMEvalKit

Common questions

What is the difference between lmms-eval and VLMEvalKit?

lmms-eval: One-for-All Multimodal Evaluation Toolkit Across Text, Image, Video, and Audio Tasks. VLMEvalKit: An open-source evaluation toolkit for large vision-language models. See the comparison table for live GitHub stats and shared categories.

When should I choose lmms-eval over VLMEvalKit?

Choose lmms-eval over VLMEvalKit when License: lmms-eval is Other, VLMEvalKit is Apache-2.0; VLMEvalKit has a 'successor' relationship to 'lmms-eval' because while lmms-eval provides a unified evaluation framework for multimodal large language models across various media types, VLMEvalKit specifically focuses on and simplifies the evaluation of vision-language models through dedicated generation-based methods and exact matching techniques, offering more specialized capabilities within a细分; Tags unique to lmms-eval: agi, audio-evaluation, benchmark, llm-evaluation; You need to evaluate LLaVA series models on different datasets with precise control over reproducibility details like torch/cuda versions.

When should I choose VLMEvalKit over lmms-eval?

Choose VLMEvalKit over lmms-eval when License: VLMEvalKit is Apache-2.0, lmms-eval is Other; VLMEvalKit has a 'successor' relationship to 'lmms-eval' because while lmms-eval provides a unified evaluation framework for multimodal large language models across various media types, VLMEvalKit specifically focuses on and simplifies the evaluation of vision-language models through dedicated generation-based methods and exact matching techniques, offering more specialized capabilities within a细分; Tags unique to VLMEvalKit: computer-vision, llm, multi-modal; When you need to evaluate models supporting thinking mode, as it provides a custom split_thinking function improving accuracy.

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.

When should I avoid VLMEvalKit?

If your project requires evaluation tools that generate Excel files with individual cells larger than the default support of 32,767 characters and cannot switch to TSV format. When you do not need generation-based evaluation methods with exact matching and LLM-based answer extraction.

Is lmms-eval or VLMEvalKit more popular on GitHub?

lmms-eval has more GitHub stars (4,368 vs 4,345). Stars measure visibility, not whether either tool fits your constraints.

Are lmms-eval and VLMEvalKit open source?

Yes - both are open-source projects on GitHub (lmms-eval: Other, VLMEvalKit: Apache-2.0).

Where can I find alternatives to lmms-eval or VLMEvalKit?

GraphCanon lists graph-backed alternatives at lmms-eval alternatives and VLMEvalKit alternatives (lmms-eval markdown twin, VLMEvalKit 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 VLMEvalKit?

lmms-eval: Active. VLMEvalKit: Very 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 lmms-eval and VLMEvalKit?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: lmms-eval trust report; VLMEvalKit trust report.