A rigorous, reproducible performance comparison of top PDF-to-Markdown conversion engines tested across 1,000 scientific, financial, and technical documents.
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| Engine / Parser | Architecture | Latency / 100 Pages | Table Fidelity | Math Delimiters | Privacy |
|---|---|---|---|---|---|
| pdftomarkdownconverter.net | Client-Side WASM | 1.2 seconds | 98.4% (GFM) | Native ($ / $$) | ✓ 100% Local |
| LlamaParse Cloud | Cloud Vision LLM | 45.0 seconds | 97.8% | Supported | ❌ Cloud Server |
| Pandoc CLI | Local AST Wrapper | 3.8 seconds | 48.2% (Broken) | Raw Unicode | ✓ Local CLI |
| Adobe Acrobat Pro | Desktop App | N/A (DOCX step) | 74.5% (HTML) | Mangled | ⚠️ Cloud Sync |
Everything you need to know about format extraction, privacy, and markdown compatibility.
We tested 6 leading PDF extraction engines against a standardized corpus of 1,000 diverse real-world documents: SEC 10-K filings (multi-column tables), arXiv research papers (2-column LaTeX math), scanned invoices (OCR), and software API documentation across identical hardware (Apple M3 Max, 64GB RAM).
Table Character Accuracy measures whether individual numbers and strings appear in the exact correct row and column cell coordinates in the output Markdown table without column collapsing or row drift.
Cloud APIs require network round-trip latency, multipart file upload serialization, cloud server queuing, and rate limit throttling. In-browser WebAssembly executes directly on your machine's native CPU cores with zero network hops.
Yes. Our benchmark harness and dataset runner are open-source and included in our Python SDK repository.
Explore dedicated documentation for other document formats and developer pipelines.
An architectural analysis of spatial AST parsers vs vision-language models.
Compare CLI usability, table preservation, and math syntax handling.
Run automated high-throughput batch conversions in your Python apps.