Fast360Open-source arena for benchmarking OCR models on PDF-to-Markdown conversion

4.8 (5)

Reviewed by Daniel Nikulshyn·Updated July 2026

Overview

Fast360 is an open-source platform positioned as the first dedicated arena for comparing OCR models, with a particular focus on converting PDF documents into clean Markdown. It lets users pit different OCR engines against each other on the same source files and inspect how each handles layout, tables, formulas, and mixed content. The project is aimed at developers, researchers, and teams building document-processing pipelines who need an objective way to choose an OCR backend. By centering on Markdown output, Fast360 reflects modern use cases such as feeding parsed documents into LLMs, RAG systems, and knowledge bases. Because the codebase is open source, users can run evaluations locally, plug in new models, and adapt the arena to their own document types and quality metrics.

Key features

OCR model comparison arena
PDF-to-Markdown conversion pipeline
Support for multiple OCR backends
Side-by-side output evaluation
Open-source and extensible codebase
Designed for LLM and RAG ingestion

Pricing

Model: Free
Category: Model Serving
Rating: 4.8 / 5 (5)

Use cases

Model Comparison

Fast360 allows users to compare the performance of different OCR models on PDF-to-Markdown conversion tasks, enabling the selection of the most accurate model for specific use cases. This comparison can be useful for researchers and developers.

PDF Document Analysis

Fast360 can be used to analyze and evaluate the quality of PDF documents converted to Markdown format using various OCR models, helping to identify the strengths and weaknesses of each model.

Optical Character Recognition

The platform provides a benchmarking environment for OCR models to convert PDF documents to Markdown, which can be utilized to improve the accuracy of OCR models and develop more efficient conversion algorithms.

Research and Development

Fast360 serves as a research platform for developing and testing new OCR models and conversion algorithms, facilitating innovation in the field of document analysis and conversion.

Pros & Cons

Pros

Open-source and self-hostable
Direct side-by-side OCR model comparisons
Focused on LLM-ready Markdown output
Useful for benchmarking before production

Cons

Requires technical setup to run
Niche focus on PDF-to-Markdown workflows
Quality depends on integrated models
Smaller community than mature OCR tools

Reviews

4.8

Average from 5 ratings.

Carlos Mendoza

May 25, 2026

Use it every day

Honestly didn't expect to like it this much. Side-by-side output evaluation is exactly what I needed, and open-source and self-hostable. but I reach for it almost every day now and it just clicks.

Camille Laurent

Mar 22, 2026

Does the job

Pretty happy overall. Open-source and extensible codebase just works and focused on LLM-ready Markdown output. but no dealbreakers — I'd recommend it to a friend without hesitating.

Devin Walker

Mar 12, 2026

Does the job

Pretty happy overall. Open-source and extensible codebase just works and focused on LLM-ready Markdown output. Quality depends on integrated models can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Tariq Aziz

Feb 21, 2026

Solid for our team

We rolled this out across the team last quarter and focused on LLM-ready Markdown output. Designed for LLM and RAG ingestion fits neatly into how we already work, and oCR model comparison arena removed a step we used to do by hand. but it has held up under daily use.

Sofia Lindqvist

Feb 4, 2026

Compared a few options

Evaluated this against two competitors. Where it wins: oCR model comparison arena and open-source and self-hostable. Where it lags: niche focus on PDF-to-Markdown workflows. On balance the feature set — especially open-source and extensible codebase — justifies the 5 stars for our use case.