Reducto's own model currently outperforms Gemini Flash 2.0 on this benchmark (0.90 vs 0.84). However, as we review the lower-performing examples, most discrepancies turn out to be minor structural variations that would not materially affect an LLM’s understanding of the table.

You can recover word-level bounding boxes and confidence scores by using a traditional OCR engine such as AWS Textract and matching the results to Gemini’s output – see https://docless.app for a demo (disclaimer: I am the founder)