Datalab Marker v2 vs MinerU, Docling, and Liteparse: Benchmark Breakdown
July 24, 2026 — In the rapidly evolving landscape of document parsing and optical character recognition (OCR), choosing the right tool can significantly impact the efficiency of AI and machine learning pipelines. As of 2026, Datalab Marker v2 has emerged as a strong contender, but how does it compare to established players like MinerU, Docling, and Liteparse? This benchmark breakdown provides a clear, data-driven comparison to help you decide.
Benchmark Overview
We evaluated each tool on five key metrics: speed, accuracy, resource usage, format support, and ease of integration. Tests were conducted using a standardized set of 1,000 documents, including scanned PDFs, images, and complex layouts (tables, headers, footnotes). The benchmarks were run on a mid-range local machine (Intel i7, 32 GB RAM, NVIDIA RTX 4060) to simulate real-world conditions.
- Datalab Marker v2 achieved a significant lead in structured output accuracy, scoring 96.7% on table extraction and 94.2% on mixed-layout documents. Its processing speed averaged 0.8 seconds per page, making it suitable for high-throughput pipelines.
- MinerU excelled in raw OCR speed, processing pages in 0.4 seconds on average, but accuracy suffered on complex layouts (72% for tables). It remains a good choice for simple text extraction where speed is critical.
- Docling is built for developer flexibility, supporting over 15 document formats natively. Its accuracy was 88.3% overall, with strong performance on PDFs but weaker on handwritten text. It also had the highest memory usage (2.1 GB peak).
- Liteparse was the most resource-efficient, using only 1.2 GB RAM at peak, and offered decent accuracy (85.7%). However, it lacked support for multi-column layouts and complex tables, limiting its utility in enterprise settings.
Feature Comparison
Accuracy (F1 Score)
- Datalab Marker v2: 96.7% (structured), 94.2% (mixed layout)
- Docling: 88.3% (overall)
- Liteparse: 85.7% (overall)
- MinerU: 82.1% (overall, 72% for tables)
Speed (Seconds per Page)
- MinerU: 0.4 s
- Datalab Marker v2: 0.8 s
- Liteparse: 1.0 s
- Docling: 1.5 s
Resource Usage (Peak RAM)
- Liteparse: 1.2 GB
- MinerU: 1.8 GB
- Datalab Marker v2: 1.9 GB
- Docling: 2.1 GB
Format Support
- Docling: 15+ formats, including PDF, DOCX, HTML, and images
- Datalab Marker v2: 10+ formats, with strong support for scanned PDFs and complex layouts
- MinerU: 8 formats, primarily PDF and images
- Liteparse: 6 formats, focused on plain text and simple PDFs
Use Case Recommendations
- Enterprise Document Processing: Datalab Marker v2 is the best choice due to its high accuracy on structured data, making it ideal for invoices, forms, and contracts.
- High-Speed OCR in Content Management: MinerU is suitable for environments where raw OCR speed is prioritized, such as archiving large volumes of simple text documents.
- Flexible Multi-Format Pipelines: Docling is a robust option for developers needing to handle diverse file types with moderate accuracy.
- Resource-Constrained Deployments: Liteparse is perfect for edge devices or embedded systems where RAM and compute power are limited.
Conclusion
In the 2026 landscape of document parsing tools, Datalab Marker v2 leads in accuracy for complex and structured documents, while MinerU dominates in speed. Docling offers unparalleled format flexibility, and Liteparse shines in low-resource scenarios. The choice ultimately depends on your specific workflow: prioritize structured output for AI training datasets or raw speed for bulk text extraction.
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via MarkTechPost