# open-compass/VLMEvalKit

Open-source evaluation toolkit of large multi-modality models (LMMs), support 220+ LMMs, 80+ benchmarks

Repository: https://github.com/open-compass/VLMEvalKit
Canonical: https://ross.abutalabs.com/products/vlmevalkit
Homepage: https://huggingface.co/spaces/opencompass/open_vlm_leaderboard
Language: Python
License: Apache-2.0
License Family: permissive
Topics: gpt-4v, large-language-models, llava, multi-modal, openai, vqa, llm, openai-api, qwen, gpt, computer-vision, pytorch, gpt4, chatgpt, clip, vit, evaluation, claude, gemini
Last push: 2026-08-26T11:31:03+00:00

## Health v2 (maintenance only)
Score: 62/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 8, longevity 71
- inputs: {"age_days": 1006, "days_push": 7, "days_rel": 527, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4359, forks 755 (observed 2026-08-28T04:08:46.488159+00:00)

## What it is
VLMEvalKit is an open-source Python toolkit for evaluating large vision-language models (LMMs/LVLMs) across 80+ benchmarks with support for 220+ models. It provides one-command, generation-based evaluation with exact matching and LLM-based answer extraction, plus public leaderboards.

## Use cases
- evaluate a vision-language model on VQA benchmarks
- benchmark multimodal LLMs like GPT-4V, LLaVA, Qwen-VL
- compare LMM performance across 80+ datasets
- run one-command evaluation of image and video understanding models
- generate leaderboard results for vision-language models
- evaluate models with thinking mode or long outputs

## When to avoid
- you need to evaluate text-only LLMs without vision inputs
- you want a custom benchmark pipeline outside the supported dataset formats
- you need training or fine-tuning tooling rather than evaluation

## Facets
- artifact type: library
- maturity: active
- function: benchmarking, machine-learning, llm-inference, computer-vision, nlp
- domain: machine-learning, computer-vision, large-language-models, artificial-intelligence, developer-tools
- platform: python, cross-platform
- tags: vision-language-models, multimodal-evaluation, vqa, llm-benchmarks, model-evaluation, leaderboard, gpu, linux

## Member repositories
- open-compass/VLMEvalKit (main) score 62

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:46.488159+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:21:28.135896+00:00, confidence not recorded.
  - readme: https://github.com/open-compass/VLMEvalKit (fetched 2026-08-28T04:08:46.488159+00:00, sha db9d11234e0c)
  - homepage: https://huggingface.co/spaces/opencompass/open_vlm_leaderboard (fetched 2026-08-29T09:10:10.815447+00:00, sha 2da9b0b0791d)
- Data as of 2026-08-30T08:39:29.467469+00:00.