Comparing 4 Python Text Extraction Libraries: Benchmark Results for 2025 (So You Don’t Have To)

Comprehensive Benchmark of Python Text Extraction Libraries in 2025: Performance Insights and Recommendations

Navigating the landscape of Python text extraction tools can be complex, especially when efficiency and reliability are critical. In this detailed review, we compare four prominent libraries—Kreuzberg, Docling, MarkItDown, and Unstructured—based on rigorous testing across diverse real-world documents. Whether you’re developing an enterprise solution or working on a machine learning preprocessing pipeline, these insights will help you choose the right tool for your needs.

Understanding the Benchmark Setup

As the creator of Kreuzberg, I aimed to provide an honest, data-driven comparison of popular Python text extraction libraries. The testing process involved 94 documents varying in format, size, and language—ranging from small text files to extensive academic PDFs—ensuring a comprehensive evaluation. The benchmarks were executed using automated, reproducible methods, with all data openly accessible for validation and further analysis.

The Libraries Under Scrutiny

Kreuzberg: A lightweight, high-performance library designed for speed and efficiency, built to handle diverse document types with minimal dependencies.
Docling: An enterprise-grade, machine learning-based solution known for its advanced understanding of complex document structures.
MarkItDown: A specialized tool optimized for converting simplified documents and Markdown preprocessing tasks.
Unstructured: A versatile, enterprise-focused library emphasizing reliability across various document formats and complexities.

Benchmark Highlights

Speed and Performance

Kreuzberg emerges as the clear leader, capable of processing over 35 documents per second comfortably, making it ideal for high-throughput environments.
Unstructured offers moderate but reliable performance, excelling in scenarios involving more intricate documents.
MarkItDown performs best on straightforward files but shows diminished efficiency with complex or large documents.
Docling exhibits significant latency, often taking an hour or more per file, which limits its suitability for time-sensitive applications.

Installation Footprint

The size and dependency footprint of each library are crucial considerations: