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VLMEvalKit

BENCHMARK

Open-source evaluation toolkit for large multi-modality models (LMMs). Supports 220+ LMMs and 80+ benchmarks including MMMU, MathVista, and ChartQA. Powers the OpenVLM Leaderboard. Apache 2.0 licensed

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Overview

VLMEvalKit is a free benchmark on OpenRuna. Open-source evaluation toolkit for large multi-modality models (LMMs). Supports 220+ LMMs and 80+ benchmarks including MMMU, MathVista, and ChartQA. Powers the OpenVLM Leaderboard.

What this benchmark does

"VLMEvalKit" packages a proven benchmark so you can skip the trial-and-error of writing one from scratch. Open-source evaluation toolkit for large multi-modality models (LMMs). Supports 220+ LMMs and 80+ benchmarks including MMMU, MathVista, and ChartQA. Powers the OpenVLM Leaderboard. Apache 2.0 licensed It is catalogued next to similar resources on OpenRuna, so the rest of the toolkit you need is close by. Open a new conversation and paste it in, wire it into an agent, or keep it in your team's prompt library.

Use cases

  • Reach for it during planning or review sessions when you want consistent, AI-assisted structure.
  • Combine it with related tools and prompts in the same OpenRuna category to build an end-to-end workflow.
  • Hand "VLMEvalKit" to a new teammate so their benchmark output matches your team's quality bar from day one.
  • Use "VLMEvalKit" when you need a repeatable benchmark for professional work without rewriting instructions every time.

Example output

Ask the model to apply "VLMEvalKit" to your scenario and it returns a structured answer — clear sections, actionable steps, and assumptions stated upfront — ready to paste into docs, tickets, or code comments. Add one example of your own and the output quality jumps noticeably.

Tips by platform

Claude

Claude works best when you paste this benchmark up front and ask it to outline its plan before writing. Use the artifact panel to refine structured output turn by turn.

ChatGPT

For ChatGPT, save this benchmark as a Custom Instruction or a saved prompt so it is one click away. Add your specifics in a follow-up rather than editing the original.

Cursor

Add this benchmark to your Cursor rules and invoke it from Agent mode for repeatable results. Link back to its OpenRuna page in the rule so the source stays discoverable.

Frequently asked questions

What is "VLMEvalKit"?
It is a benchmark listed on OpenRuna — Open-source evaluation toolkit for large multi-modality models (LMMs). Supports 220+ LMMs and 80+ benchmarks including MMMU, MathVista, and ChartQA. Powers the OpenVLM Leaderboard. Apache 2.0 licensed You can copy and adapt it for ChatGPT, Claude, Cursor, or any other AI assistant.
Is "VLMEvalKit" free to use?
Most OpenRuna resources are open or CC0-licensed. Check the license shown on this page before commercial use; premium collections are clearly marked as such.
How do I get the best results from this benchmark?
Replace any placeholders, add your project context, and ask the model to confirm its assumptions first. Iterate over 2–3 follow-up turns rather than expecting a perfect first response.
Does "VLMEvalKit" work with both Claude and ChatGPT?
Yes — it is model-agnostic text, so it runs on Claude, ChatGPT, Gemini, and Cursor. The tips on this page cover each of those assistants specifically.
Where can I find resources related to "VLMEvalKit"?
Scroll to the Related resources section on this page, or open the matching category hub on OpenRuna to find connected prompts, tools, agents, and datasets in the same topic area.

Related resources