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.
I tested 10 open-source PDF parsing tools on four scanned NTU graduate entrance exams. VLM-based tools—Firecrawl, MinerU 3.4, and Marker v2—overwhelmingly beat conventional OCR on formulas and code, but installation was the real barrier: MinerU's old package name creates dependency hell, Marker's first model download takes 10 minutes, and PaddleOCR needs a separate engine. In practice, use RapidOCR for screening and MinerU or Firecrawl for close inspection.
Traditional document parsing runs a fixed pipeline regardless of input, but contracts, financial reports, and technical manuals each need different strategies. Agentic Parsing lets LLM agents observe a document and dynamically choose tools — AgenticOCR parses only the regions that matter (70%+ visual token savings), and ParseBench shows even the best method scores only 84.9% across 2,000 enterprise pages. No silver bullet.
MinerU（OpenDataLab / opendatalab/MinerU）是一個開源文件解析引擎，把 PDF、圖像、DOCX、PPTX、XLSX 轉成結構化 Markdown 與 JSON，內建公式識別、表格提取與 109 語言 OCR，起源於 InternLM 前訓練階段，適合 RAG 前處理與知識庫建構。
Docling (IBM Research Zurich, now governed by the Linux Foundation AI & Data, MIT license, v2.100.0 released 2026-06-09) is an open-source document parsing standard library with structured JSON (DoclingDocument) as its core output. It supports PDF, DOCX, PPTX, XLSX, HTML, EPUB, Apple Pages, video (MP4/AVI/MOV with ASR transcription and keyframes), audio (WAV/MP3), email (EML/MSG), ODF, and XBRL financial reports, with a swappable-stage pipeline parser (pure CPU or GPU-accelerated), VlmPipeline option (GraniteDocling 258M VLM), MCP server and API server (docling-serve), and native integrations with LangChain, LlamaIndex, Crew AI, and Haystack.
Marker (Datalab open source, Apache 2.0 license, v2.0.0 released 2026-07-20, 39.5k stars) is a pipeline-style document parsing standard library that outputs Markdown and JSON, supports optional LLM boost (`--use_llm`, default `gemini-3.5-flash`), custom formatting logic, table/formula/inline-math/link/reference/code formatting, image extraction and preservation, header/footer removal, and runs on pure CPU, GPU, or MPS (`Apple Silicon`). Unlike [Docling](/posts/tech/2026-09-06-docling-document-parsing) (structured JSON core, dedicated XML exports, pure MIT), Marker centers on Markdown/JSON under `Apache 2.0` with a separate model-weight license (`AI Pubs Open Rail-M`, $5M commercial threshold); unlike [MinerU](/posts/tech/2026-09-05-mineru-ocr-doc-parsing) (custom agreement with MAU/revenue thresholds + attribution obligations), Marker offers a simpler licensing story (`Apache 2.0` code) but requires accepting a separate model-weight license for weights. The series framework ([three-layer model](/posts/ai/2026-08-06-document-parsing-three-layers)) positions all three (`MinerU`, `Docling`, `Marker`) as pipeline-based parsing-layer options with distinct licensing, output, and speed trade-offs.
Three routes to commercial document parsing: specialized parsers (Cohere Parse at $1.50/k pages, LlamaParse Agentic Plus at 90.2% on ParseBench), Big Three cloud prebuilts (Azure/Google/AWS for structured field extraction), and general-purpose VLMs (Fable 5.1 scores 78.92 on ParseBench and crushes specialized parsers on charts, but costs 3–16× more and hallucinates). At 100K pages/month, plain OCR runs ~$150 across providers; add tables and AWS jumps to $1,500, Claude Sonnet 5 to $900. The first question isn't 'which is most accurate' — it's 'do you need transcription or comprehension?'