# Document Parsing

Nace released Drex, a decision model for document processing that, after the parsing engine returns layout-aware Markdown, judges page by page whether text matches the page image, whether a table kept its shape and whether a ticked box survived, and triggers correction only where something is in doubt. On Nace's own parsing leaderboard, NDI plus Drex scores a Parse Index of 0.834, ahead of all 12 parsers tested, including LlamaParse Agentic at 0.820; it improves on the engine alone on OmniDocBench, MDPBench and ExtractBench, and is within noise of it on ParseBench and RealDocBench.

Nace released Drex, a decision model for document processing that, after the parsing engine returns layout-aware Markdown, judges page by page whether text matches the page image, whether a table kept its shape and whether a ticked box survived, and triggers correction only where something is in doubt. On Nace's own parsing leaderboard, NDI plus Drex scores a Parse Index of 0.834, ahead of all 12 parsers tested, including LlamaParse Agentic at 0.820; it improves on the engine alone on OmniDocBench, MDPBench and ExtractBench, and is within noise of it on ParseBench and RealDocBench.

Aleph Alpha distilled its self-checking agentic parsing harness into a single vision-language model, Parsy, which handles each page in one pass. The harness reached 86.0 on ParseBench by running several models and up to three passes per page, too slow and costly for millions of pages a month. Parsy was trained on harness-labeled public documents whose labels were cleaned with an independent layout model, an ink-coverage check and a second OCR model; it scores 87.9 on ParseBench, parses hundreds of thousands of pages per day on one H100, and costs one to two orders of magnitude less per page.

Aleph Alpha distilled its self-checking agentic parsing harness into a single vision-language model, Parsy, which handles each page in one pass. The harness reached 86.0 on ParseBench by running several models and up to three passes per page, too slow and costly for millions of pages a month. Parsy was trained on harness-labeled public documents whose labels were cleaned with an independent layout model, an ink-coverage check and a second OCR model; it scores 87.9 on ParseBench, parses hundreds of thousands of pages per day on one H100, and costs one to two orders of magnitude less per page.