NEWVectors or files. Pick a path.Start →
    Models/Text Extraction/opendatalab/MinerU2.5-Pro-2604-1.2B
    HFOCRApache 2.0

    MinerU2.5-Pro-2604-1.2B

    by opendatalab

    1.2B document parser achieving 95.69 on OmniDocBench through pure data engineering

    Identifiers
    Model ID
    opendatalab/MinerU2.5-Pro-2604-1.2B
    Feature URI
    mixpeek://image_extractor@v1/opendatalab_mineru25_pro_v1

    Overview

    MinerU2.5-Pro is a 1.2B-parameter document parsing model that achieves state-of-the-art results on OmniDocBench v1.6 (95.69) without any architectural changes over the base MinerU2.5. The improvement comes entirely from a Data Engine that expands training data from under 10M to 65.5M samples using Diversity-and-Difficulty-Aware Sampling, Cross-Model Consistency Verification, and a Judge-and-Refine annotation pipeline.

    On Mixpeek, MinerU2.5-Pro delivers the highest-accuracy document parsing available at compact model size, surpassing models with 200x more parameters on complex layouts, tables, formulas, and mixed-content documents. Its coarse-to-fine two-stage strategy first analyzes layout on downsampled images then performs targeted OCR on native-resolution crops, balancing throughput with extraction quality.

    Architecture

    Decoupled coarse-to-fine two-stage architecture. Stage 1: layout analysis on downsampled images for structural element identification. Stage 2: targeted content recognition on native-resolution crops. 0.5B Qwen2-Instruct decoder. 1.2B total parameters. Three-stage progressive training: pre-training, hard sample fine-tuning, GRPO alignment.

    Mixpeek SDK Integration

    // No extractor parameter takes a Hugging Face model id (checked against
    // GET /v1/discovery/extractors, which returns 13), so MinerU2.5-Pro-2604-1.2B runs
    // on your side and the output is upserted through POST
    // /v1/namespaces/{namespace_id}/documents/upsert. On Enterprise the other
    // path is to upload the weights instead: POST /v1/namespaces/{id}/models
    // accepts the huggingface format and a custom plugin loads them.
    const res = await fetch(
      "https://api.mixpeek.com/v1/namespaces/ns_your_namespace/documents/upsert",
      {
        method: "POST",
        headers: {
          Authorization: "Bearer API_KEY",
          "Content-Type": "application/json",
        },
        body: JSON.stringify({
          collection_id: "col_your_collection",
          documents: [
            {
              document_id: "asset-00412",
              // The model produces text, so it lands in payload. Give the
              // collection a text vector index and embed that text to make it
              // searchable rather than only filterable.
              payload: { extracted_text: modelOutput, source_key: "archive/2026/asset-00412" },
              vectors: { "text-embedding": embeddingOfModelOutput },
            },
          ],
        }),
      },
    );
    
    // Managed alternative, if this exact model is not the requirement:
    // document_graph_extractor@v1 runs intfloat/multilingual-e5-large-instruct
    // (1024-d) over a bucket, with no inference of your own.

    Capabilities

    • 95.69 on OmniDocBench v1.6 (SOTA across all model sizes)
    • Surpasses models with 200x more parameters
    • Native-resolution OCR on extracted layout crops
    • Complex layouts: tables, formulas, nested structures, diagrams
    • 1.2B parameters: deployable on modest GPU hardware

    Use Cases on Mixpeek

    High-accuracy PDF and document parsing for financial reports, contracts, and regulatory filings
    Scientific paper extraction preserving formulas, tables, and cross-references
    Large-scale document digitization pipelines balancing accuracy with throughput

    Benchmarks

    DatasetMetricScoreSource
    OmniDocBench v1.6Overall Score95.69OpenDataLab, Apr 2026: arXiv 2604.04771
    OmniDocBench v1.6 (tables)Table Score97.2OpenDataLab, Apr 2026: arXiv 2604.04771

    Performance

    Input SizeVariable resolution (coarse-to-fine multi-scale)
    GPU Latency~45ms / page (A100)
    GPU Throughput~22 pages/sec (A100)
    GPU Memory~3.5 GB

    Specification

    FrameworkHF
    Organizationopendatalab
    FeatureOCR
    Outputtext + bbox
    Modalitiesvideo, image, document
    RetrieverText-in-Image
    Parameters1.2B
    LicenseApache 2.0
    Downloads/mo283K

    Research Paper

    MinerU2.5-Pro: Pushing the Limits of Data-Centric Document Parsing at Scale

    arxiv.org

    Build a pipeline with MinerU2.5-Pro-2604-1.2B

    Add this model to a processing pipeline alongside other extractors. Combine with retrieval stages for end-to-end search.

    Run it on your own data, free