Like many of you, I've been building RAG pipelines recently, and constantly hit a wall at the very first step: getting clean, structured Markdown from PDFs.

I found myself in a loop of "environment hell"—spinning up different Conda environments to test Marker, then PP-StructureV3, then MinerU, just to see which one worked best for a specific paper or financial report. It was a massive time sink. Static leaderboards weren't much help, because they don't tell you how a model will perform on your specific, messy document.

So, I built the tool I wished I had. It's a simple web utility that I call an "OCR Arena."

You can try it here: https://fast360.xyz

The idea is simple: upload a document, select from a lineup of 7 leading open-source models, and it runs them all in parallel, showing you the results side-by-side. The goal is to get you from "which parser should I use?" to having the best possible Markdown in under a minute.

It's completely free, and I made sure there's no login/signup required so you can try it with zero friction. Here’s a quick GIF of the workflow:

https://github.com/shijincai/fast360/blob/main/nologin.gif

The tech stack is a pretty standard setup: Next.js/React on the frontend, a Node.js/Express backend acting as a BFF, and a Python service that manages the model execution via a Redis/BullMQ queue.

This is a web service, not an open-source project, but I've set up a public GitHub repo to act as an information hub, a place to track community feedback, and to share more about the tech. You can find that here:

GitHub: https://github.com/shijincai/fast360

I built this to solve my own problem, but I'm hoping it might be useful to some of you as well. I'll be here all day to answer any questions and listen to your thoughts.