Glm 4.5V

GLM-4.5V is a multimodal vision-language model introduced by the GLM-V Team as part of a research effort to advance versatile reasoning across text and images. The model is formally described in the arXiv paper "GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning," which was submitted on 1 July 2025 and most recently revised as version v6 on 1 January 2026. The paper is catalogued under arXiv's Computer Vision and Pattern Recognition subject area, signalling its focus on visual understanding alongside language tasks. The named author group includes contributors such as Wenyi Hong, Wenmeng Yu, Xiaotao Gu, Guo Wang, and Guobing Gan, situating GLM-4.5V within a broader line of multimodal research from the same team.

The model's stated direction in the paper is versatile multimodal reasoning developed with scalable reinforcement learning, pairing it with a sibling thinking-oriented variant called GLM-4.1V-Thinking. This framing positions GLM-4.5V as a practical tool for tasks that combine visual inputs with language understanding and reasoning, where the open-weight release makes it suitable for self-hosting and downstream adaptation. Its pairing with a thinking-focused sibling suggests a design emphasis on deliberative, step-by-style inference rather than single-shot answers, which is useful for analytical workflows involving images and structured outputs. For teams building assistants, document understanding pipelines, or agentic systems that benefit from open weights, GLM-4.5V offers a recent, reasoning-oriented vision-language option from the GLM family.