README.md

metadata

title: P-Bench
emoji: 🥇
colorFrom: green
colorTo: indigo
sdk: gradio
pinned: true
license: apache-2.0
short_description: A cost/quality/speed Leaderboard for Inference Providers!
sdk_version: 5.19.0
tags:
  - leaderboard

P-Bench

Compare text-to-image, text-to-video, image-to-video, and video-to-video models on quality, speed, and price. This repo is the Gradio dashboard: leaderboards, Pareto plots, and side-by-side samples.

The live Space is PrunaAI/P-Bench.

The current P-Video-2 Pro evaluation snapshots are data/t2v-pro.csv (text-to-video) and data/i2v-pro.csv (image-to-video). Both feed the leaderboards and price/time Pareto plots with Datapoint and Rapidata Elo. All models appear in the video-generation leaderboards, including variants with missing scores. Missing scores and ranks display as dashes; Pareto plots include only models with the required score and price or timing. Image-to-video sample media is not included in these exports. Rapidata Elo comes directly from these exports.

License

The P-Bench code (the Gradio app, app.py, ui.py, model_display.py) is released under the Apache License 2.0.

The benchmark prompts and third-party data in data/ are not covered by that license. They keep the license or terms of their original source:

Data in this Space	Source	License / terms
data/oneig_combined/prompts.jsonl: 299 OneIG-Bench prompts (anime, human and object alignment)	OneIG-Bench (Chang et al., 2025)	CC BY-NC 4.0
data/qwen_image_bench_combined/prompts.jsonl: 100 Qwen-Image-Bench prompts	Qwen-Image-Bench (Li et al., 2026)	Apache 2.0
data/video_generation_combined/prompts.jsonl: 90 VBench-2.0 prompts	VBench-2.0 (Zheng et al., 2025)	Apache 2.0
data/arena_ai_text_to_image_leaderboard.csv	Arena text-to-image leaderboard	the source's terms of use
data/artificial_analysis_text_to_image_leaderboard.csv	Artificial Analysis text-to-image arena	the source's terms of use

The OneIG-Bench, Qwen-Image-Bench and VBench-2.0 prompts are copied verbatim. P-Bench uses a selection of each benchmark and adds its own prompt IDs. The generated images and videos come from the listed models. Human preference scores (Elo) were collected by Pruna AI through Datapoint and Rapidata.

Because the OneIG-Bench prompts are licensed for non-commercial use only (CC BY-NC 4.0), any reuse of data/oneig_combined/ must follow that license.

Citation

If you use P-Bench, please cite it together with the benchmarks whose prompts it uses:

@misc{P-Bench,
    title={P-Bench: A Leaderboard for Text-to-Image Models},
    author={PrunaAI},
    year={2026},
    howpublished={\url{https://huggingface.co/spaces/PrunaAI/P-Bench}}
}

@article{chang2025oneig,
    title={OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation},
    author={Jingjing Chang and Yixiao Fang and Peng Xing and Shuhan Wu and Wei Cheng and Rui Wang and Xianfang Zeng and Gang Yu and Hai-Bao Chen},
    journal={arXiv preprint arXiv:2506.07977},
    year={2025}
}

@misc{li2026qwenimagebenchgenerationcreationtexttoimage,
    title={Qwen-Image-Bench: From Generation to Creation in Text-to-Image Evaluation},
    author={Niantong Li and Guangzheng Hu and Weixu Qiao and Ying Ba and Qichen Hong and Shijun Shen and Jinlin Wang and Fan Zhou and Jianye Kang and Xin Shang and Ziyi He and Wei Wang and Dalin Li and Jiahao Li and Jie Zhang and Kaiyuan Gao and Kun Yan and Lihan Jiang and Ningyuan Tang and Shengming Yin and Tianhe Wu and Xiao Xu and Xiaoyue Chen and Yuxiang Chen and Yan Shu and Yanran Zhang and Yilei Chen and Yixian Xu and Zekai Zhang and Zhendong Wang and Zihao Liu and Zikai Zhou and Hongzhu Shi and Yi Wang and Bing Zhao and Hu Wei and Lin Qu and Chenfei Wu},
    year={2026},
    eprint={2605.28091},
    archivePrefix={arXiv},
    primaryClass={cs.CV},
    url={https://arxiv.org/abs/2605.28091}
}

@article{zheng2025vbench2,
    title={{VBench-2.0}: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness},
    author={Zheng, Dian and Huang, Ziqi and Liu, Hongbo and Zou, Kai and He, Yinan and Zhang, Fan and Zhang, Yuanhan and He, Jingwen and Zheng, Wei-Shi and Qiao, Yu and Liu, Ziwei},
    journal={arXiv preprint arXiv:2503.21755},
    year={2025}
}

Run locally

From the repo root:

python -m venv .venv
source .venv/bin/activate
pip install "gradio==5.19.0" pandas -r requirements.txt
python app.py

requirements.txt lists Plotly. Gradio and pandas are required locally; Hugging Face Spaces installs Gradio from the YAML sdk_version above.

Deploy

origin is the Space (https://huggingface.co/spaces/PrunaAI/P-Bench). Publish the current branch to the live app with:

git push origin HEAD:main