OCR SOTA Feb 2026 Open Document AI Leaderboard and Deployment Guide

In less than one quarter, open OCR went from "good enough text extraction" to a crowded race where multiple compact vision-language models now compete on parsing quality, structure fidelity, grounding, and cost.

If you are choosing an OCR stack in February 2026, the bottleneck is no longer finding a capable model. The bottleneck is choosing the model that fails least on your actual documents.

TL;DR Top models are now close on headline benchmarks, but they are not interchangeable in production. GLM-OCR and PaddleOCR-VL-1.5 currently lead reported OmniDocBench scores, while LightOnOCR-2 has a strong speed/quality profile and DeepSeek-OCR-2 remains attractive for markdown-oriented workflows. Start with a use-case-first shortlist, then run a fixed 50-page bake-off before rollout.

Update (Feb 16, 2026): rednote-hilab/dots.ocr-1.5 was released with new reported comparisons against GLM-OCR and PaddleOCR-VL-1.5. We now treat it as a serious challenger, especially for teams that need OCR plus broader image parsing (SVG, web screens, and scene text). For a dedicated breakdown, see: https://instavar.com/blog/Dots_OCR_1_5_vs_GLM_OCR_vs_PaddleOCR_VL_1_5.

If you are short on time:

1. Read Section 1 for the first model to test.
2. Read Section 4 for use-case fit.
3. Use Section 5 as your production evaluation checklist.

1 Start here: which model should you test first?