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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](https://huggingface.co/spaces/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.
## 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
```
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