Datalab Marker v2 vs MinerU, Docling, and Liteparse: Benchmark Breakdown
Datalab has released Marker 2, an open-source document conversion pipeline that significantly outperforms competitors like MinerU and Docling in speed and accuracy. The update introduces device-aware modes and architectural changes to improve throughput for processing various document formats into structured data.
Why it matters
Efficient document conversion is critical for training large language models, and these benchmarks provide a standard for evaluating data ingestion pipelines.
Datalab has released Marker 2 , a full rewrite of its open source document conversion pipeline. Marker converts PDF, image, PPTX, DOCX, XLSX, HTML, and EPUB files into markdown, JSON, HTML, or chunks. The Datalab team rebuilt it around three components shipped over the preceding months: Surya OCR 2 , a 20M-param fast layout model , and a rebuilt pdftext that is 3× faster than the previous one.
The main result comes from olmOCR-bench , a third-party benchmark from Allen AI. Marker 2’s balanced mode scores 76.0% overall and 83.5% on born-digital PDFs. It sustains 2.9 pages per second on a single B200 GPU. That is over 5× the throughput of MinerU’s pipeline backend, which scores 72.7% at 0.54 pages per second. Docling scores 50.3% at 2.1 pages per second on the same harness.
Marker 2 exposes three conversion paths instead of one:
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