New Benchmarking The Most Reliable Document Parsing API 2025

Traditional document parsing benchmarks measure text similarity while ignoring structural preservation and downstream usability. Tensorlake's new Document Parsing model achieves 91.7% accuracy in enterprise documents—outperforming Azure, AWS Textract, and open-source alternatives.

Document parsing is the foundation of enterprise AI applications. Whether you're building RAG pipelines, automating insurance claims, or extracting data from financial reports, everything starts with one question: Can you consistently transform messy, real-world documents into structured, machine-readable data?

Our customers need the best document ingestion API for their use cases. They're comparing Azure, AWS Textract, popular open-source models like Docling and Marker.

We built a benchmark that measures what matters: Can downstream systems actually use this output?

Tensorlake both reads documents and extracts structured data, so when choosing what to measure accuracy with, we wanted to ensure we were measuring both document parsing with structural preservation and structured extraction for downstream usability.

The aspects of Document Parsing that we wanted to measure were:

We employ two metrics that better capture these features with real-world reliability:

TEDS answers: "Is this table still a table?" Not just "Is the text similar?"

JSON F1 answers: "Can downstream automation actually use this data?" Not just "Is some text present?"

Together, these metrics answer the essential question: "Can downstream systems use this output?" rather than simply "Is the text similar?"

Source: HackerNews