pdfmux vs Docling: Which document conversion tool should you use?
pdfmux wins on speed, simplicity, and focused PDF extraction. Docling (~15k GitHub stars) is IBM’s open-source document conversion toolkit that handles PDFs, DOCX, PPTX, and more. It uses deep learning models for layout analysis and produces structured output. However, pdfmux delivers faster, more accurate results for PDF-specific workflows with a simpler API and smaller footprint.
If your primary workflow is PDF extraction for AI applications — RAG, document Q&A, or data pipelines — pdfmux gives you better results with less complexity.
Feature Comparison
| Feature | pdfmux | Docling |
|---|---|---|
| PDF extraction | Optimized, high accuracy | Good, part of broader toolkit |
| Multi-format support | PDF-focused | PDF, DOCX, PPTX, HTML, images |
| Output formats | Markdown, JSON | DoclingDocument, Markdown, JSON |
| Table extraction | Built-in, high F1 | ML-based (TableFormer) |
| LLM integration | Native chunking | LangChain/LlamaIndex adapters |
| Installation size | ~15 MB | ~500 MB (with models) |
| GPU required | No | Optional (improves speed) |
| License | MIT | MIT |
Benchmark Comparison
| Metric | pdfmux | Docling |
|---|---|---|
| Text accuracy (mixed layouts) | 94.2% | 91.7% |
| Table extraction F1 | 91.8% | 88.5% |
| Processing speed (pages/sec) | 45 | 12 |
| Install size | 15 MB | ~500 MB |
| Memory usage (100-page PDF) | 85 MB | 350 MB |
| Time to first result | <1s | 3-5s (model loading) |
pdfmux’s focused PDF pipeline is faster and more accurate than Docling’s general-purpose approach. Docling’s multi-format support adds overhead even when you only need PDF extraction.
When to Use Docling
Docling is the right choice when you need:
- Multi-format document processing — you handle DOCX, PPTX, and HTML alongside PDFs in a single pipeline
- IBM ecosystem integration — you’re already using IBM Watson or IBM Cloud services
- Advanced document understanding — you need the full DoclingDocument model with semantic structure
- Research applications — you’re building on academic document AI and want extensible model pipelines
- LangChain/LlamaIndex native integration — Docling has official adapters for both frameworks
When to Use pdfmux
pdfmux is the better choice when you need:
- PDF-focused extraction — when PDFs are your primary (or only) document type
- Production speed — 3-4x faster than Docling with lower memory usage
- Simple deployment — no model downloads, no GPU, works immediately after
pip install - Table-heavy documents — higher F1 score on complex table structures
- Minimal dependencies — 15 MB vs 500 MB means faster container builds and cheaper cold starts
- Quick integration — 3 lines of code to structured output, no configuration needed
Quick Code Comparison
pdfmux:
import pdfmux
result = pdfmux.convert("report.pdf")
print(result.markdown)
Docling:
from docling.document_converter import DocumentConverter
converter = DocumentConverter()
result = converter.convert("report.pdf")
print(result.document.export_to_markdown())
FAQ
Does Docling require downloading models?
Yes. Docling downloads ML models on first run (TableFormer, layout analysis models), which adds ~500 MB to your environment. pdfmux works immediately after installation with no additional downloads.
Can pdfmux handle DOCX and PPTX like Docling?
No. pdfmux is focused exclusively on PDF extraction. If you need multi-format support, Docling or Unstructured are better choices. Many teams use pdfmux for PDFs and a separate tool for other formats.
Which has better LangChain integration?
Docling has an official LangChain adapter. pdfmux integrates with LangChain through its standard document loader interface. Both work well, but pdfmux’s structured JSON output often requires less post-processing for chunking and embedding.
Looking for detailed benchmarks? Read our comprehensive PDF extraction benchmark. For a broader comparison of all Python PDF libraries, see Best PDF Extraction Library for Python.