Which OCR Model Fits Which Workflow in 2026: GLM-OCR, FireRed-OCR, dots.ocr-1.5, and PaddleOCR-VL-1.5

Most OCR comparisons still start with the benchmark table.

The harder production question is simpler: which model breaks least often on the pages you actually have.

This guide is organised around that question. On our scan-heavy OCR pilot, the useful conclusion was not one universal winner. It was a routing rule:

• FireRed-OCR became the best default for text-first pages once its wrapper handled blank pages and preserved page images
• GLM-OCR stayed safer when the question depends on a small inline graph, apparatus, particle diagram, or reaction scheme
• dots.ocr-1.5 was more compelling when OCR was only one part of a broader visual parsing workflow
• PaddleOCR-VL-1.5 stayed relevant when a team wanted a mature OCR baseline tied to a broader parsing ecosystem

For the full scan-heavy benchmark method and the evidence behind this routing rule, see: https://instavar.com/blog/ai-production-stack/How_We_Benchmark_OCR_Models_on_Scan_Heavy_PDFs.

For the wider market map, see: https://instavar.com/blog/ai-production-stack/OCR_SOTA_Feb_2026_Open_Document_AI_Leaderboard.

Start here

If you only need the first pass:

• choose FireRed-OCR when the page is mostly notes, bullets, tables, worked answers, or formulas
• choose GLM-OCR when the page is really asking the reader to interpret a small local visual
• choose dots.ocr-1.5 when OCR is only one piece of a larger visual-language parsing workflow
• keep PaddleOCR-VL-1.5 in the shortlist if you want a mature baseline with a broader surrounding document stack

1 The shortest workflow-fit answer