The three-layer ladder for turning documents into LLM-readable content — conversion, extraction, and parsing. From picking the right layer to comparing MarkItDown, anydoc, MinerU, and the rest.
The most common mistake in feeding documents to an LLM isn't picking the wrong tool — it's picking the wrong layer. Structure already in the file goes to the conversion layer (milliseconds); text without structure goes to extraction; only inferred structure needs parsing. anydoc's 4.7ms against Docling's 513.6ms is a 109× gap, and most people jump straight to the most expensive layer.
A lightweight open-source tool from Microsoft that converts PDF, Office, images, audio, and more into Markdown — purpose-built for LLM pipelines.
Firecrawl's open-source Rust conversion library turns 14 office formats (including legacy .doc / .ppt / .xls) into GFM at a 4.7ms median — 109× faster than Docling under the same timing basis. The trade-off: it does no OCR at all.
Digital-native PDFs already contain readable text — what's missing is structure, and heuristics can recover it. PyMuPDF, pdfplumber, pypdf, and Tika do this with zero GPU and zero inference cost. The biggest selection trap isn't accuracy; it's PyMuPDF's AGPL-3.0 license.
Scans and complex layouts leave you no choice but to infer structure with a model. But the technical gap between MinerU, Marker, and Docling is far smaller than the licensing gap — MinerU needs a separate license past $20M monthly revenue, Marker's model weights need payment past a funding threshold, and only Docling is cleanly MIT. Read the LICENSE before the benchmark.