AI is happening. The future is updating.GLM-4.6V-Flash
This model is part of the GLM-V family of models, introduced in the paper GLM-4.1V-Thinking and GLM-4.5V: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning. - **GLM-4.6V Blog**: https://z.ai/blog/glm-4.6v - **Paper**: https://huggingface.co/papers/2507.01006 - **GitHub Repository**: https://github.com/zai-org/GLM-V - **Online Demo**: https://chat.z.ai/ - **API Access**: Z.ai Open Platform - **Desktop Assistant App**: https://huggingface.co/spaces/zai-org/GLM-4.5V-Demo-App GLM-4.6V series model includes two versions: GLM-4.6V (106B), a foundation model designed for cloud and high-performance cluster scenarios, and GLM-4.6V-Flash (9B), a lightweight model optimized for local deployment and low-latency applications. GLM-4.6V scales its context window to 128k tokens in training, and achieves SoTA performance in visual understanding among models of similar parameter scales. Crucially, we integrate native Function Calling capabilities for the first time. This effectively bridges the gap between "visual perception" and "executable action" providing a unified technical foundation for multimodal agents in real-world business scenarios.
This model is part of the GLM-V family of models, introduced in the paper GLM-4.1V-Thinking and GLM-4.5V: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning. - **GLM-4.6V Blog**: https://z.ai/blog/glm-4.6v - **Paper**: https://huggingface.co/papers/2507.01006 - **GitHub Repository**: https://github.com/zai-org/GLM-V - **Online Demo**: https://chat.z.ai/ - **API Access**: Z.ai Open Platform - **Desktop Assistant App**: https://huggingface.co/spaces/zai-org/GLM-4.5V-Demo-App GLM-4.6V series model includes two versions: GLM-4.6V (106B), a foundation model designed for cloud and high-performance cluster scenarios, and GLM-4.6V-Flash (9B), a lightweight model optimized for local deployment and low-latency applications. GLM-4.6V scales its context window to 128k tokens in training, and achieves SoTA performance in visual understanding among models of similar parameter scales. Crucially, we integrate native Function Calling capabilities for the first time. This effectively bridges the gap between "visual perception" and "executable action" providing a unified technical foundation for multimodal agents in real-world business scenarios.