OCR and VLM 2026: Who Leads in Document Recognition
OCR and VLM 2026: Who Leads in Document Recognition
The OCR and Visual-Language Model (VLM) industry has experienced a real boom in the last few months. It seems you barely get a handle on one novelty before several new players emerge. We've put together a fresh overview and compared the most interesting models to understand who truly deserves a spot in your production pipeline.
1. DeepSeek-OCR 2
🐋 DeepSeek-OCR 2 is a 3B model focused on complex documents and OCR with structure understanding. The main innovation is DeepEncoder V2, which works almost like a human: first, a global understanding of the image is formed, and then a logical reading order is established.
Pros:
- Excellently handles complex layouts, tables, signatures, and structured text.
- Outperforms Gemini Pro on several benchmarks.
- Can be run locally and fine-tuned via Unsloth.
Cons:
- 3B model size → higher GPU requirements for high-frequency inference.
License: Apache 2.0 Links: Hugging Face | Documentation
2. Step3-VL-10B
🌟 Step3-VL-10B from Stepfun.ai is an example of a compact yet "heavy-duty" VLM. With only 10B parameters, it aims to compete with models 10-20 times larger, including Gemini 2.5 Pro and GLM-4.6V.
Features:
- 1.8B visual encoder + Qwen3-8B decoder.
- Trained on 1.2 trillion tokens with RLVR+RLHF.
- High results on OCRBench and math task benchmarks.
Cons:
- For top scores, PaCoRe = 16 parallel rollouts → x16 computational resources.
- OCR is only part of its capabilities; the primary focus is VLM.
License: Apache 2.0 Links: vLLM / OpenAI-compatible API
3. PaddleOCR-VL-1.5
🐼 PaddleOCR-VL-1.5 is a compact model (0.9B) optimized for "field" conditions. It was trained considering curved scans, glary photos, and crumpled pages.
Features:
- OmniDocBench v1.5 — 94.5% accuracy.
- Text spotting, seal recognition, table stitching across pages.
- Support for rare languages, Tibetan, and Bengali.
- Easy integration via transformers, Docker, and Paddle.
Cons:
- Handwritten text is still poor.
- Page-by-page parsing via transformers is limited.
License: Apache 2.0 Links: Hugging Face | GitHub
4. GLM-OCR
📄 GLM-OCR is a multimodal OCR model with 0.9B parameters. It's based on GLM-V with a CogViT visual encoder and a GLM-0.5B decoder. It supports layout analysis via PP-DocLayout-V3 and parallel recognition.
Pros:
- OmniDocBench v1.5 — 94.62% (#1).
- Supports tables, formulas, seals, and code-heavy documents.
- Fast inference: vLLM / SGLang / Ollama.
- SDK and simple integration, open-source.
Cons:
- JSON schema for Information Extraction requires strict adherence.
License: MIT (layout — Apache 2.0) Links: Hugging Face | GitHub
Comparison Table
| Model | Parameters | Primary Focus | Benchmark | OCR / Doc Score | License | Deployment |
|---|---|---|---|---|---|---|
| DeepSeek-OCR 2 | 3B | OCR + Structure | OCRBench | +4% vs v1, beats Gemini Pro | Apache 2.0 | HF, Unsloth |
| Step3-VL-10B | 10B | Universal VLM | OCRBench | 86.75 | Apache 2.0 | vLLM, OpenAI-API |
| PaddleOCR-VL-1.5 | 0.9B | Field OCR | OmniDocBench v1.5 | 94.5 | Apache 2.0 | Paddle, Docker |
| GLM-OCR | 0.9B | OCR + IE | OmniDocBench v1.5 | 94.62 (#1) | MIT | vLLM, SGLang, Ollama |
Conclusion
- Leaders on OmniDocBench: GLM-OCR (94.62%) and PaddleOCR-VL-1.5 (94.5%).
- Lightest and fastest for production: PaddleOCR-VL-1.5 and GLM-OCR.
- Most "intelligent" architecturally: DeepSeek-OCR 2 with DeepEncoder V2.
- Most versatile VLM: Step3-VL-10B (OCR is only part of its capabilities).
OCR and VLM have reached a level of maturity that allows for the implementation of document recognition in real production scenarios: from tables and formulas to multi-page PDFs with code and seals. The race for speed, accuracy, and document "understanding" continues.