Datalab Marker v2 vs MinerU, Docling, and Liteparse: Benchmark Breakdown
Datalab has released Marker 2, a full rewrite of its open supply doc conversion pipeline. Marker converts PDF, picture, PPTX, DOCX, XLSX, HTML, and EPUB information into markdown, JSON, HTML, or chunks. The Datalab group rebuilt it round three parts shipped over the previous months: Surya OCR 2, a 20M-param quick structure mannequin, and a rebuilt pdftext that’s 3× sooner than the earlier one.
The principal outcome comes from olmOCR-bench, a third-party benchmark from Allen AI. Marker 2’s balanced mode scores 76.0% total 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 identical harness.
What’s New in Marker 2
Marker 2 exposes three conversion paths as a substitute of 1:
- balanced — the Surya VLM handles structure, and the entire web page is re-OCR’d at any time when embedded textual content is unhealthy. Highest high quality, finest on GPU. 76.0% olmOCR-bench.
- quick — a light-weight rf-detr/onnx structure detector plus pdftext, with minimal, surgical VLM use. 66.6%, and far cheaper.
- –disable_ocr — pure text-layer extraction, no VLM calls in any respect. Runs solely on CPU. 43.6%, 23.7 pg/s.
Mode is now device-aware by default: balanced on GPU, quick on CPU/MPS, overridable with –mode. Full CPU assist is the second structural change. quick –disable_ocr wants no GPU and no inference server, and the 20M structure mannequin nonetheless reads columns, tables and headers on CPU.
The third change is architectural, and it’s the one which produces the throughput numbers. Many skinny CPU employees share a single Surya inference server. The guardian course of budgets VLM concurrency throughout them, so throughput scales with server capability fairly than per-process VRAM. Datalab reviews that balanced mode sustains ~2.9 pg/s in opposition to a ~0.3 pg/s single-stream price on the identical {hardware}.
Breaking modifications are value flagging earlier than an improve. Python 3.10+ is now required. Packaging moved from Poetry to uv, with hatchling because the construct backend, although pip set up marker-pdf is unchanged. The structured-extraction converter and extractors have been eliminated; Datalab factors customers to the hosted API or a –use_llm workflow as a substitute.
Comparison
The scoring benchmark is olmOCR-bench from Ai2: 1,403 PDFs with roughly 8,400 move/fail unit assessments overlaying math rendering, desk construction, studying order, headers and footers, and outdated scans. The total rating is the macro-average throughout the 8 classes, computed with the official olmOCR-bench checker. Throughput is sustained concurrent pg/s on one B200 host, not single-stream latency.
A observe on provenance. olmOCR-bench is a third-party benchmark from Ai2, however each rating and throughput determine beneath comes from Datalab’s personal runs. All of them are reproducible by the open harness within the Marker repository, which ships competitor runners for MinerU, Docling and LiteParse alongside Marker’s personal.
These numbers additionally mirror one benchmark’s doc combine measured on a single {hardware} setup, so outcomes by yourself paperwork might differ. Teams evaluating these programs ought to run the harness in opposition to their very own corpus, which is the one strategy to know the way the 4 rank on the paperwork they really course of.
Marker 2 vs MinerU
MinerU’s pipeline backend is the closest architectural match. Both learn the PDF textual content layer and OCR selectively. On total rating, Marker balanced leads 76.0 to 72.7. On born-digital paperwork the 2 are successfully tied: 83.5 in opposition to 83.3.
The separation is throughput. Marker balanced sustains 2.9 pg/s in opposition to MinerU’s 0.54 pg/s, a 5.4× hole at the next rating. Marker quick sustains 7.4 pg/s, roughly 13.7× MinerU’s pipeline price, however scores 6.1 factors beneath MinerU to do it.
MinerU additionally ships a VLM backend, which Datalab states scores increased than its pipeline backend. That backend is a full-page-VLM strategy and just isn’t on this desk. AI groups evaluating MinerU ought to benchmark that path individually.
Marker 2 vs Docling
Docling is the widest margin among the many GPU pipelines. Marker balanced leads 76.0 to 50.3 total and 83.5 to 64.0 on born-digital, whereas additionally operating sooner: 2.9 pg/s in opposition to 2.1 pg/s. Datalab notes Docling was run on its default pipeline, which makes use of the textual content layer for born-digital pages and OCR for picture areas.
Docling’s counterweight is governance and format breadth, not accuracy. The codebase is MIT-licensed, it originated at IBM Research, and it’s hosted as a mission within the LF AI & Data Foundation. Its enter record additionally extends previous paperwork into audio and electronic mail codecs.
Marker 2 vs LiteParse
LiteParse, from the LlamaIndex group, is a Rust doc parser. It doesn’t compete on the identical axis. On CPU it scores 22.4 total and 20.4 with OCR off, in opposition to Marker’s CPU-only 43.6.
But LiteParse with OCR disabled reviews 1721 pg/s — roughly 73× Marker’s CPU mode, which is the tradeoff. Marker’s quick –disable_ocr runs a 20M structure mannequin on CPU and nonetheless recovers construction, which is why it greater than doubles a plain textual content dump’s rating. LiteParse has no structure mannequin and collapses on something non-linear.
Marker 2 vs the full-page VLM tier
The Datalab group emphasizes that Marker is designed as a pipeline fairly than a VLM, clarifying that these are distinct instruments. In this analysis, their hosted Chandra 2 scores 85.8, whereas Gemini Flash 3.5 through API scores 76.4. Datalab’s Chandra repository additionally positions Ai2’s olmOCR 2 at 82.4 and dots.ocr 1.5 at 83.9 inside a separate desk. For scans, math-heavy pages, and attaining high accuracy, the VLM tier stays superior to all listed pipelines.
Marker’s balanced mode narrows the efficiency hole to only 0.4 factors behind Gemini Flash 3.5 total, and it even outperforms it on born-digital paperwork by a margin of 83.5 to 79.1—with out requiring a per-page API name.
Per-category conduct
The mode you choose modifications the failure profile, not simply the rating. Each row is one olmOCR-bench class, scored throughout all three modes. Math is the sharp edge: quick mode reads equations from the PDF textual content layer as a substitute of VLM-OCRing them, so arXiv math falls from 83.9 to 23.4, and –disable_ocr scores 0.0 there by design. Outside the 2 math classes, outdated scans is the weakest break up in each mode, topping out at 43.2.
Licensing
This is the place the 4 programs diverge most for business groups:
- Marker: code is Apache 2.0. Model weights use a modified AI Pubs OpenRAIL-M license — free for analysis, private use, and startups below $5M funding/income. Beyond that, business use of the weights requires a paid license.
- MinerU: now below the MinerU Open Source License, primarily based on Apache 2.0 with added situations. A separate business license is required above 100M MAU or $20M month-to-month income, and on-line providers constructed on it should disclose that reality.
- Docling: MIT, with mannequin licenses tracked individually of their authentic packages.
- LiteParse: open supply, from run-llama, with LlamaParse positioned because the paid cloud path for onerous paperwork.
Use Case- Comparison
Score alone doesn’t choose the instrument. Corpus kind, {hardware}, licensing band and output format resolve it. Try the interactive picker beneath to filter ten deployment situations by constraint and by instrument, and see which parser matches your use case.
Key Takeaways
- Marker 2 balanced scores 76.0% on olmOCR-bench at 2.9 pg/s — over 5× MinerU’s pipeline throughput.
- It beats Docling on each axes directly: 76.0% in opposition to 50.3%, and 2.9 pg/s in opposition to 2.1 pg/s.
- LiteParse trades construction for velocity — 1721 pg/s with OCR off, however 20.4% in opposition to Marker’s 43.6% on CPU.
- Fast mode with –disable_ocr runs solely on CPU, no inference server, at 23.7 pg/s.
- Licensing splits the sphere: Docling is MIT, MinerU stays free to $20M month-to-month income, and Marker’s weights want a paid license above $5M.
- All benchmark and throughput numbers ship with a reproducible benchmarks/ harness.
Interactive Dynamic Explainer
Links: GitHub repo | Release notes | Blog post | Announcement tweet
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