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LatestZhipu AI4.6V-Flash
Zhipu AIVision-Language

Zhipu AI Releases Fast, Open Vision Model GLM-4.6V-Flash

The new model from the GLM-4.6V family offers a fast, MIT-licensed option for developers working with both text and images.

Dec 7, 2025
NotableMIT
GLM-4.6V-Flash

Zhipu AI has released GLM-4.6V-Flash, a new open-source vision-language model. As its name suggests, this "Flash" version is designed for speed and efficiency, joining the company's growing GLM-4.6V family of multimodal models.

The model's most significant feature for the open-source community is its permissive MIT license. This allows for broad adoption, including in commercial products, without the usage restrictions common to many research-oriented releases. This choice lowers the barrier for developers looking to build and deploy applications that can understand both images and text.

While technical details like parameter count and architecture specifics were not detailed in the initial release card, GLM-4.6V-Flash's focus on performance is clear. It provides another strong alternative in the competitive landscape of open vision-language models, particularly for use cases where low latency is a key requirement, such as real-time visual analysis or interactive chatbots.

This release solidifies Zhipu AI's position as a consistent contributor to the open-source ecosystem. By providing a fast, commercially-friendly VLM, the company offers developers a valuable new tool for building the next wave of multimodal applications.

Sources

  • zai-org/GLM-4.6V-Flash

    Hugging Face

    Visit
OlderVoxCPM 1.5 Brings Open-Source Voice CloningOpenBMB · Text → Speech · 11 months agoNewerZhipu AI Releases Compact Bilingual Speech ModelZhipu AI · Speech → Text · 11 months ago

Get the model

Hugging Face

Specs

Context window131K tokens
Size20.6 GB
PrecisionBF16
ArchitectureGlm4vForConditionalGeneration
LicenseMIT
Downloads44.1K
Likes632

Can you run it?

Runs on a laptop — about 7.5 GB at 4-bit.

  • BF16 (as published)

    24 GB GPU (RTX 3090 / 4090) · 32 GB Mac

    21.9 GB
  • 8-bit

    16 GB GPU (RTX 4080 / 5070 Ti) · 24 GB Mac

    12.3 GB
  • 4-bit

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

    7.5 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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