datalab-to

marker

Datalab's 39k-star PDF-to-Markdown/JSON converter: a layout pipeline plus an optional LLM pass for tables, forms and equations, with chunk output and form-value extraction built in.

39,140 2,803 Python Apache-2.0updated 17 days ago

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Curator's take

The default answer when you need a lot of documents turned into clean Markdown on your own hardware: fast per page, tuned by people who do this for a living, and it can call Gemini/Claude/OpenAI-compatible/Ollama models only on the blocks that need help. Two caveats before you ship it: the code is Apache-2.0 but the model weights are OpenRAIL-M (free for research, personal use and companies under $5M funding/revenue), and on genuinely nasty input — scans, handwriting, dense multi-column tables — Datalab's own VLM chandra beats it. Born-digital corpus and no appetite for model calls? Use a deterministic parser instead.

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README.md

Datalab

State of the Art models for Document Intelligence

Marker

Marker converts documents to markdown, JSON, chunks, and HTML quickly and accurately.

Converts PDF, image, PPTX, DOCX, XLSX, HTML, EPUB files in all languages
Formats tables, forms, equations, inline math, links, references, and code blocks
Extracts and saves images
Removes headers/footers/other artifacts
Extensible with your own formatting and logic
Optionally boost accuracy with LLMs (and your own prompt)
Works on GPU, CPU, or MPS

Try Datalab's Managed Platform

Our managed platform runs a version of our latest open source model, Chandra — higher accuracy than Marker, with zero data retention by default, SOC 2 Type 2, and custom BAAs.

If you have high volume workloads, we offer a batch processing service that has processed 1B+ pages per week — we manage the infrastructure so your workloads finish on time.

Get started with $5 in free credits — sign up.

Performance

We measure marker on olmocr-bench, a third-party benchmark of 1,403 PDFs with tests covering math, tables, multi-column layout, scans, and hard edge cases. Balanced mode scores 76.0% overall — 83.5% on born-digital PDFs — ahead of MinerU and docling and within range of much larger VLMs, while fast mode runs the layout + text-layer path far cheaper (and a no-OCR mode goes faster still). Scores are the olmocr-bench overall (macro-average across the 8 categories).

See below for the full per-category scores, the competitive comparison, and instructions on how to run your own benchmarks.

Hybrid Mode

For the highest accuracy, pass the --use_llm flag to use an LLM alongside marker. This will do things like merge tables across pages, handle inline math, format tables properly, and extract values from forms. It works with Gemini, Claude, OpenAI-compatible, Azure, Vertex, OpenRouter, or Ollama models. By default, it uses gemini-3.5-flash. See below for details.

Examples

PDF	File type	Markdown	JSON
Think Python	Textbook	[View](https://github.com/VikParuchuri/marker/blob/master/data/

Continue your stack

What teams reach for next — and why each earns a place beside marker. Ranked by curator confidence.

Pairs well with

pdf-inspector

Firecrawl's Rust PDF triage: classifies text-based vs scanned in ~10-50ms, extracts positioned text and clean Markdown without OCR — routing the ~54% of PDFs that never needed a model.

Why it fitsCheap router in front of an expensive parser: pdf-inspector classifies text-based vs scanned in milliseconds, so marker's model passes only run on the pages that actually need them.

contextgem

Declarative LLM extraction from documents: describe Aspects and Concepts in plain language, get structured values back with paragraph- or sentence-level references and built-in justifications.

Why it fitsmarker turns the document into Markdown; contextgem then pulls declared Aspects and Concepts out of that text with sentence-level references — conversion then extraction.

FreeToken

Edge-native MoE serving engine: bandwidth-adaptive CPU-GPU co-execution, global LRU expert caching and elastic VRAM run 290B+ frontier MoE models on a gaming PC at interactive speed.

Why it fitsmarker's --use_llm hybrid mode accepts any OpenAI-compatible endpoint, so FreeToken can serve those per-page model calls off your own GPU instead of metering Gemini across a large corpus.

Alternative to See all + compare →

chandra

Datalab's SOTA open OCR model: images/PDFs to structured HTML/Markdown/JSON with layout, tables, forms, checkboxes, handwriting and math, in 90+ languages. Local HF or vLLM inference.

Why it fitsSame shop, two tiers: marker is the pipeline you run at volume with selective LLM assist, chandra is the vision-language model you reach for when accuracy on scans, handwriting and dense tables matters more than throughput.

MinerU

Heavyweight document-to-markdown/JSON parser — PDFs plus Office (docx/pptx/xlsx) through layout analysis and OCR into LLM-ready output for RAG and agentic pipelines. 73k stars, self-hostable.

Why it fitsBoth convert PDF and Office files to LLM-ready Markdown/JSON. MinerU casts the wider format and pipeline net; marker is leaner to install, faster per page, and pushes hard blocks to an LLM of your choosing.

olmocr

Open toolkit that linearizes messy PDFs — scans, tables, equations, handwriting — into clean ordered Markdown with a self-hosted vision-language model. Built for LLM training data and RAG ingestion.

Why it fitsolmOCR runs one vision-language model over every page; marker runs layout analysis first and calls a model only where needed — cheaper at corpus scale, weaker on truly messy scans.