Choosing the Best OCR Large Model: DeepSeek‑OCR‑2, HunyuanOCR, PaddleOCR‑VL‑1.5, and GLM‑OCR Compared

Model Overviews

DeepSeek‑OCR‑2 (3 B parameters, released Jan 2026, Apache 2.0) uses a Visual Causal Flow architecture that mimics human visual encoding. It supports dynamic resolutions up to 1 M pixels and outputs Markdown, LaTeX, and HTML. Advantages include innovative architecture, high compression, flexible resolution, vLLM acceleration (~2500 tokens/s on A100‑40G), and an MIT‑friendly license. Disadvantages are larger parameter count, high GPU memory demand (20 GB+), and weaker multilingual coverage.

HunyuanOCR (1 B parameters, released Nov 2025, custom license) employs a native multimodal architecture that handles text spotting, complex document parsing, open‑domain information extraction (JSON schema), video subtitle extraction (dual‑language), and image‑text translation for 100+ languages. Its strengths are ultra‑lightweight deployment, unified multi‑task processing, and excellent multilingual support. Weaknesses include lower document‑parsing precision on OmniDocBench, slightly weaker formula recognition than DeepSeek‑OCR‑2, and limited cloud API availability.

PaddleOCR‑VL‑1.5 (0.9 B parameters, released Jan 2026, Apache 2.0) builds on ERNIE 4.5 with multi‑task training, supporting scanning, tilt, bend, screen‑capture, and lighting variations. It offers document parsing, spotting with polygon detection, seal recognition, cross‑page table merging, and cross‑page heading detection. Advantages are SOTA performance (OmniDocBench 94.5 points), real‑world robustness, lightweight model, fast inference (1.86 pages/s PDF on A100), and extensive language enhancements (Tibetan, Bengali). Drawbacks are a still‑maturing ecosystem compared to the classic PaddleOCR and limited API availability.

GLM‑OCR (0.9 B parameters, released Jan 2026, MIT) combines a GLM‑0.5B language decoder with a CogViT visual encoder. Core innovations are Multi‑Token Prediction (MTP) and full‑task reinforcement learning. It excels at formula and table recognition (OmniDocBench 94.62 points, highest overall), strict JSON‑Schema output, and diverse deployment options (vLLM, SGLang, Ollama). Limitations are support for only eight languages, prompt constraints to document parsing and information extraction, and a newer community with fewer resources.

Benchmark Performance

Overall OmniDocBench v1.5 scores: GLM‑OCR 94.62 (最高), PaddleOCR‑VL‑1.5 94.5, HunyuanOCR 94.10, DeepSeek‑OCR‑2 87.01.

Text recognition: DeepSeek‑OCR‑2 83.37, HunyuanOCR 94.73, PaddleOCR‑VL‑1.5 最佳, GLM‑OCR 94.73.

Formula recognition: DeepSeek‑OCR‑2 优秀, HunyuanOCR 稍弱, PaddleOCR‑VL‑1.5 优秀, GLM‑OCR SOTA.

Table recognition: DeepSeek‑OCR‑2 84.97, HunyuanOCR 优秀, PaddleOCR‑VL‑1.5 优秀, GLM‑OCR SOTA.

Real‑world robustness (Real5‑OmniDocBench): PaddleOCR‑VL‑1.5 consistently ★★★★★, GLM‑OCR ★★★★★, HunyuanOCR ★★★★, DeepSeek‑OCR‑2 ★★★.

Multilingual support: HunyuanOCR 100+ languages (including 14 low‑resource), PaddleOCR‑VL‑1.5 several languages (Tibetan, Bengali), GLM‑OCR 8 languages, DeepSeek‑OCR‑2 multiple but less extensive.

Parameter Efficiency & Deployment

When comparing parameter size, inference speed, and GPU memory:

DeepSeek‑OCR‑2: 3 B, ~2500 tokens/s, 20 GB+ memory, medium deployment difficulty.

HunyuanOCR: 1 B, moderate speed, 10‑15 GB memory, low deployment difficulty.

PaddleOCR‑VL‑1.5: 0.9 B, 1.86 pages/s PDF, 8‑12 GB memory, low deployment difficulty.

GLM‑OCR: 0.9 B, high speed, 8‑12 GB memory, low deployment difficulty.

Strengths & Weaknesses Summary

DeepSeek‑OCR‑2

Innovative Visual Causal Flow architecture.

High compression and vLLM acceleration.

Strong layout understanding.

Higher resource cost and weaker multilingual coverage.

HunyuanOCR

Ultra‑lightweight, excellent multilingual support.

Unified end‑to‑end model for multiple tasks.

Best for information extraction and video subtitles.

Lower document‑parsing precision on OmniDocBench.

PaddleOCR‑VL‑1.5

SOTA overall accuracy and real‑world robustness.

Fastest inference speed.

Rich ecosystem and mature tooling.

Multilingual support improving but not as broad as HunyuanOCR.

GLM‑OCR

Best formula and table recognition.

Strict JSON output, many deployment options.

Strong for academic papers and high‑precision needs.

Limited language coverage and prompt flexibility.

Recommendation Matrix

Enterprise users : primary choice is PaddleOCR‑VL‑1.5 for production‑ready performance; secondary options are GLM‑OCR for precision‑critical tasks or HunyuanOCR for multilingual requirements.

Research institutions : prioritize GLM‑OCR for formula‑heavy documents; consider DeepSeek‑OCR‑2 for architecture research.

Individual developers : start with PaddleOCR‑VL‑1.5 for ease of use; GLM‑OCR is a good alternative if deployment flexibility is needed.

Decision Tree (text version)

Start → Need real‑world document handling? → Yes → PaddleOCR‑VL‑1.5
      → Need highest formula/table accuracy? → Yes → GLM‑OCR
      → Need multilingual or info extraction? → Yes → HunyuanOCR
      → Need video subtitle extraction? → Yes → HunyuanOCR
      → Resource‑constrained (edge/high‑concurrency)? → Yes → PaddleOCR‑VL‑1.5 or GLM‑OCR
      → Research/complex layout? → Yes → DeepSeek‑OCR‑2
      → Default → PaddleOCR‑VL‑1.5

References

DeepSeek‑OCR‑2: GitHub https://github.com/deepseek-ai/DeepSeek-OCR, HuggingFace https://huggingface.co/deepseek-ai/DeepSeek-OCR-2, Paper https://arxiv.org/abs/2601.20552

HunyuanOCR: GitHub https://github.com/Tencent-Hunyuan/HunyuanOCR, HuggingFace https://huggingface.co/tencent/HunyuanOCR, Paper https://arxiv.org/abs/2511.19575

PaddleOCR‑VL‑1.5: GitHub https://github.com/PaddlePaddle/PaddleOCR, HuggingFace https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5, Paper https://arxiv.org/abs/2601.21957

GLM‑OCR: GitHub https://github.com/zai-org/GLM-OCR, HuggingFace https://huggingface.co/zai-org/GLM-OCR, Ollama https://ollama.com/library/glm-ocr

Benchmarks: OmniDocBench v1.5 https://github.com/opendatalab/OmniDocBench, Real5‑OmniDocBench https://huggingface.co/datasets/PaddlePaddle/Real5-OmniDocBench, OCRBench https://github.com/Yuliang-Liu/MultimodalOCR