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LatestZhipu AIOCR
Zhipu AIVision-Language

Zhipu AI Releases Multilingual GLM-OCR Vision Model

The new vision-language model from the creators of the GLM series is specialized for recognizing and extracting text from images across multiple languages.

Jan 30, 2026
NotableOther
GLM-OCR

Zhipu AI, the company behind the prominent GLM series of large language models, has released a new open-source model focused on a classic computer vision task: optical character recognition (OCR). The new model, called GLM-OCR, is a vision-language model (VLM) designed specifically to identify and extract text embedded in images.

The key feature of GLM-OCR is its multilingual capability. According to the project's official release page, the model is trained to handle text in Chinese, English, Korean, and Japanese, making it a potentially valuable tool for applications that need to process documents and images from across East Asia and the English-speaking world. You can find the model and usage instructions on its Hugging Face repository.

Why it matters

High-quality OCR is a foundational technology for digitizing documents, parsing user interfaces, and powering accessibility tools. While powerful OCR services are available through proprietary APIs, strong open-source alternatives empower developers to build applications with more privacy and control. GLM-OCR provides a new, specialized tool for this purpose, particularly for developers working with multilingual content.

While Zhipu AI has released the model weights, potential users should note the license. The model is available under a custom license that places limitations on its use for online services, a key distinction from more permissive licenses like Apache 2.0. This restricts its use in certain commercial applications, so developers should review the terms carefully before integrating it into their projects.

Sources

  • zai-org/GLM-OCR

    Hugging Face

    Visit
OlderBlack Forest Labs Releases FLUX.2 Klein 9BBlack Forest Labs · Text → Image · 9 months agoNewerQwen Releases Coder-Next, A New Open MoE Coding ModelQwen · Alibaba · Code · 9 months ago

Get the model

Hugging Face

Specs

Context window131K tokens
Size2.7 GB
PrecisionBF16
ArchitectureGlmOcrForConditionalGeneration
LicenseOTHER
Downloads1.8M
Likes2.2K

Can you run it?

Runs on just about anything.

  • BF16 (as published)

    8 GB GPU (RTX 4060) · 16 GB laptop or Mac

    4.2 GB
  • 8-bit

    Any modern laptop · 8 GB RAM

    2.9 GB
  • 4-bit

    Any modern laptop · 8 GB RAM

    2.3 GB
Your machine
GGUF builds
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Vision-Language

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