The newly released 4B parameter vision-language model can decode documents at more than 2,800 tokens per second and is currently powering all modes of LlamaParse. LlamaIndex designed the model to extract data from complex enterprise files, including scanned PDFs, unlabeled charts, and nested tables with merged cells. In performance tests, the tool scored 87.4 on the ParseBench benchmark, outperforming similar models such as Mistral OCR 4.1 and Chandra-OCR 2.
OCria-1 also led three third-party benchmarks, recording a 92.9 score on the olmOCR-bench for tables and 84.1 on PulseBench-Tab. LlamaIndex is offering up to $3,000 in credits to eligible organizations that book a guided evaluation to process documents at scale. The research team optimized the training pipeline and dataset to ensure the model handles billions of pages without breaking on irregular grids or formatting.
