zai-org/GLM-V

GLM-4.6V/4.5V/4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

What it solves

GLM-V is a series of vision-language models (VLMs) designed to move beyond basic multimodal perception toward complex reasoning, long-context understanding, and the ability to act as multimodal agents. It addresses the gap between seeing an image and executing a specific action or solving a complex problem based on visual data.

How it works

The project provides several model versions (GLM-4.6V, GLM-4.5V, and GLM-4.1V) with different capabilities:

  • GLM-4.6V integrates native function calling, allowing the model to use tools based on visual inputs (like screenshots) and generate interleaved image-text content.
  • GLM-4.5V focuses on real-world usability with a "Thinking Mode" switch to balance speed and deep reasoning across image, video, and document understanding.
  • GLM-4.1V-9B-Thinking uses Reinforcement Learning with Curriculum Sampling (RLCS) and a Chain-of-Thought reasoning mechanism to improve accuracy and interpretability in a smaller parameter scale.

Who it’s for

  • Developers building multimodal agents or GUI automation tools.
  • Researchers exploring vision-language reasoning and reinforcement learning for VLMs.
  • Enterprise users needing high-performance document parsing, UI-to-code replication, and visual grounding.

Highlights

  • Native Multimodal Function Calling: Bridges perception and execution by passing images directly as tool inputs.
  • Visual Grounding: Can identify and provide precise bounding boxes for objects described in natural language.
  • UI-to-Code: Capable of reconstructing pixel-accurate HTML/CSS from UI screenshots.
  • Long Context Support: GLM-4.6V supports up to 128K tokens for multi-document or long-document understanding.
  • Flexible Deployment: Compatible with SGLang, vLLM, and Transformers.

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