Datalab Lift vs the Field: How a 9B Schema-First Extractor Compares with NuExtract3, LlamaExtract, Marker, and Docling

Datalab’s Lift is a focused document extraction tool with a specific promise: give it a PDF or image plus a JSON Schema, and it returns schema-shaped JSON directly. Instead of converting a document to Markdown first and then asking another model to extract fields, Lift reads rendered page images and attempts to emit the final structured object in a single pass. According to Datalab, Lift is a 9B vision model for structured JSON extraction from PDFs and images, supports schema-constrained decoding, and returns JSON that matches the user’s schema. That positioning matters because Lift is not mainly an OCR engine, not mainly a PDF-to-Markdown converter, and not a full enterprise document review platform. It is best understood as a schema-first document extractor: a model for turning visually complex documents into application-ready fields. First, the distinction that organizes everything: parsing vs. extraction Most document AI tools solve one of two different problems: Parsers turn documents into faithful intermediate representations: Markdown, HTML, JSON blocks, layout trees, tables, headings, reading order, and chunks for retrieval. Tools such as Docling, MinerU, Marker, Unstructured, PyMuPDF, OCRmyPDF, and Surya primarily fall into this category. Their output is document-shaped.