Zhipu AI Unveils GLM-4.5V: Open-Source Multimodal Model with 64K Context and Tunable Thinking Mode

What GLM-4.5V Is

Zhipu AI has open-sourced GLM-4.5V, a next-generation vision-language model built on the GLM-4.5-Air backbone. The architecture totals 106 billion parameters but uses a Mixture-of-Experts design to activate about 12 billion parameters per inference. This design delivers strong real-world performance across images, video, GUIs, charts and long documents while keeping inference costs manageable.

Key Capabilities

Image reasoning: GLM-4.5V interprets complex scenes, recognizes spatial relationships, detects defects, and can reason across multiple images to infer context.
Video understanding: A 3D convolutional vision encoder enables long-video processing, temporal downsampling and automatic segmentation for event recognition and video summarization.
Spatial grounding: 3D Rotational Positional Encoding (3D-RoPE) enhances the model's perception of three-dimensional relationships in visual inputs.
GUI and agent tasks: The model excels at screen reading, icon and button localization, and describing or planning GUI operations, useful for accessibility and RPA workflows.
Chart and document parsing: GLM-4.5V extracts structured data and summaries from dense charts, infographics and long image-rich documents, supporting up to 64,000 tokens of multimodal context.
Precise grounding: It can localize objects and UI elements with semantic world knowledge, not just pixel cues, enabling AR, annotation and quality control use cases.

Architecture and Training

GLM-4.5V uses a hybrid vision-language pipeline combining a visual encoder, MLP adapter and language decoder to fuse visual and textual signals. The MoE approach maintains a 106B parameter footprint while activating 12B per inference for efficiency. Video inputs are processed with temporal downsampling plus 3D convolution to support native aspect ratios and high-resolution analysis. The training regimen mixes large-scale multimodal pretraining, supervised fine-tuning and Reinforcement Learning with Curriculum Sampling (RLCS) to improve long-chain reasoning and robustness on real-world tasks.

Thinking Mode: Tunable Reasoning Depth

A standout feature is the Thinking Mode toggle:

Thinking Mode ON: Prioritizes step-by-step, deliberative reasoning for complex multi-step tasks such as logical deduction or deep chart and document analysis.
Thinking Mode OFF: Returns faster, direct answers for routine lookups and simple Q&A.

This lets users trade off between speed and interpretability depending on the task.

Benchmarks and Real-World Impact

GLM-4.5V achieves state-of-the-art results across 41 to 42 public multimodal benchmarks including MMBench, AI2D, MMStar and MathVista. It outperforms many open and some proprietary models in areas such as STEM question answering, chart understanding, GUI operation and video comprehension. Early deployments demonstrate value in defect detection, automated report analysis, digital assistant creation and accessibility tools.

Use Cases and Practical Applications

Defect detection and content moderation via image reasoning
Surveillance review, sports analytics and lecture summarization with long-video understanding
Accessibility and automation through GUI reading and operation planning
Financial and scientific report analysis by parsing charts and long illustrated documents
AR, retail and robotics applications using precise visual grounding

Open Access and Resources

GLM-4.5V is released under the MIT license, making advanced multimodal reasoning broadly available to researchers, developers and enterprises. Zhipu AI provides links to the paper, Hugging Face model, GitHub repository and tutorials for adoption and experimentation.