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.

---

📚 Read also

• AI Experience: How to Stop Competing with Thousands of Candidates
• AI is Not About Prompts
• The Perfect Resume: AI Pipeline and the Balance Between Responsibilities and Achievements
• Part-time, Subscription, or Full-time? What format does a business need an AI Strategist in?
• Your Own CMS on GitHub: How Copilot Helps Write, Publish, and Announce Content