I benchmarked 4 Python text extraction libraries so you don’t have to (2025 results)

Comprehensive Benchmark of Python Text Extraction Libraries in 2025: Performance, Reliability, and Usability Compared

Exploring the Performance of Leading Python Libraries for Text Extraction in 2025

In the rapidly evolving world of document processing, selecting the right Python library for extracting text from diverse formats remains a critical challenge. To provide clarity, I recently conducted an extensive benchmark analysis of four prominent text extraction tools—Kreuzberg, Docling, MarkItDown, and Unstructured—evaluating their capabilities across a wide range of real-world documents. Here’s an in-depth look at the findings, which might help you make informed decisions for your projects.

Why This Benchmark Matters

As the creator of Kreuzberg, I aimed to assess how it compares with other leading solutions under diverse conditions. This evaluation is fully transparent, built on an open-source methodology, and designed to reflect actual usage scenarios without bias. Whether you’re working on enterprise document management, academic research, or lightweight applications, understanding each library’s strengths and weaknesses is essential.

The Libraries Under Review

Kreuzberg: My contribution—optimized for speed and lightweight deployment. (~71MB, 20 dependencies)
Docling: An IBM ML-powered solution known for deep understanding but with significant resource requirements (~1GB, 88 dependencies)
MarkItDown: Developed by Microsoft, excels at Markdown and straightforward document formats (~251MB, 25 dependencies)
Unstructured: Designed for enterprise environments, supporting complex document layouts (~146MB, 54 dependencies)

Testing Scope and Methodology

The benchmark incorporated 94 documents covering various formats—PDFs, Word documents, HTML pages, images, and spreadsheets—in six languages, including English, Hebrew, Chinese, and Japanese. Document sizes ranged from tiny files (<100KB) to massive academic papers (>50MB). To ensure fairness, tests were run solely on CPU resources, with consistent conditions. Multiple metrics, such as processing speed, memory consumption, and failure rates, provided a comprehensive performance profile.

Key Findings at a Glance

Speed and Efficiency