Datalab Lift vs Competitors: How a 9B Document Extractor Works with JSON Schema

Datalab Lift vs Competitors: How the 9B Document Extractor Works with JSON Schema

Datalab published a comparative analysis in July 2026 of its Lift tool — a 9-billion parameter model for extracting structured data from documents — against four popular alternatives: NuExtract3, LlamaExtract, Marker, and Docling.

How Lift Works

Lift is built on a schema-first principle: a PDF or page image is fed into the model along with a JSON Schema, and the model returns ready-made structured JSON without intermediate steps.

Most competing pipelines work differently: first, the document is converted to Markdown using a converter — OCR plus layout recognition — then a separate language model extracts the required fields from the text. Lift combines both stages into one: it analyzes rendered page images directly and immediately outputs the result in the required format.

Key characteristics of the tool:

• Model size — 9 billion parameters
• Input formats — PDF and page images
• Output format — JSON strictly following the provided JSON Schema
• Architecture — vision-based, no intermediate Markdown
• Competitors in comparison — NuExtract3, LlamaExtract, Marker, Docling

How Competitors Differ

Marker and Docling are document converters: they specialize in accurately reproducing page structure in Markdown or HTML. This is useful when a text layer is needed for search, indexing, or further processing by a language model — but by itself it does not provide structured data.

NuExtract3 and LlamaExtract are closer to Lift in terms of task: both accept a schema and return structured JSON. However, they typically work on top of already-converted text rather than with the raw visual representation of the page.

Lift's approach is niche: the model sacrifices universality — there is no "just read the document" mode — in favor of accuracy and straightforwardness in scenarios with a predefined data schema.

When the Schema-First Approach Wins

The schema-first architecture makes sense primarily in industrial pipelines where the structure of output data is determined in advance: extracting fields from invoices, contracts, medical records, customs declarations, bank statements.

In such cases, the two-step pipeline "convert to Markdown → LLM extraction" creates an unnecessary link: an error by the parser in the first step degrades quality in the second. Lift eliminates this risk by working directly with the visual representation of the page and targeting the final format immediately.

The limitation is a rigid dependency on the schema. For unstructured tasks or when "full document text" is required, Lift is not suitable. For such scenarios, Marker or Docling remain the more appropriate choice.

What This Means

Datalab's comparison marks a new dividing line in the document AI market: universal converters against specialized extractors. For teams with clear requirements for output data, schema-first tools can significantly simplify the pipeline and reduce the number of moving parts in production systems.