Zhipu AI has released GLM-4.5V, a next-generation open-source vision-language model. Based on the 106-billion-parameter GLM-4.5-Air backbone, it implements a Mixture-of-Experts architecture that activates 12 billion parameters per query. All model code and weights are available under an MIT license, extending access to advanced multimodal AI capabilities.
Key features include:
- Image analysis: GLM-4.5V performs advanced scene parsing, multi-image cross-referencing and spatial inference. It can flag subtle defects in manufacturing lines and interpret geographic markers in aerial imagery for mapping tasks. This level of detail makes it useful for industrial inspection and remote sensing projects.
- Video processing: A 3D convolutional encoder handles long videos with temporal downsampling, automatic scene segmentation and detailed event detection. Applications range from sports analytics and storyboarding to surveillance review and lecture summarization.
- Spatial reasoning: 3D Rotational Positional Encoding (3D-RoPE) grants the model deep awareness of three-dimensional relationships, supporting accurate placement and grounding of visual elements in a scene.
- Interface reading and icon detection: The system reads desktop and mobile app interfaces, spots interactive elements such as buttons and icons, and supports robotic process automation and accessibility tools. It can generate metadata for each component and feed it into screen readers or automation scripts. Developers can use these metadata outputs to generate macros or enhance voice-based navigation for visually impaired users.
- Desktop workflow assistance: By interpreting GUI layouts, the model can plan and describe software operations step by step, guiding users through complex tasks or multi-stage procedures. This allows integration with macro-recording tools to automate repetitive workflows in enterprise applications. It exports step sequences into executable scripts for rapid deployment.
- Chart and infographic interpretation: It extracts structured data and summaries from charts, infographics and scientific diagrams embedded in PDF or PPT files, even when graphics are dense or lengthy.
- Document comprehension at scale: Support for up to 64,000 tokens of multimodal input lets GLM-4.5V parse and summarize extended, image-rich documents like research papers, legal contracts and compliance reports. It retains figure captions and table layouts to maintain coherence in summaries. Content layout and visual hierarchy are preserved, reducing need for manual editing.
- Precise element grounding: The model can localize objects, generate bounding boxes and describe specific UI components by combining pixel data with world knowledge and semantic context. It powers quality control pipelines, AR overlays and detailed annotation workflows. Combined with semantic context, this grounding streamlines automated annotation and augmented reality workflows at scale.
- Hybrid vision-language pipeline: A visual encoder, MLP adapter and language decoder form a unified system that treats static images, videos, GUIs, charts and documents as equal inputs. That fusion module ensures consistent performance across diverse data types. It enables a single model to serve caption generation, document QA and interactive multimedia chatbots.
- MoE efficiency: Although built with 106 billion parameters, only 12 billion activate during inference to deliver high throughput, low latency and reduced compute costs.
- 3D convolution enhancements: Three-dimensional kernels and temporal downsampling enable efficient processing of high-resolution video streams at native aspect ratios. This preserves fine visual details during fast-moving scenes and rapid camera pans. Frame-by-frame consistency checks also benefit motion analysis and object tracking tasks.
- Adaptive context window: Up to 64K tokens of context allow the model to handle multi-image prompts, concatenated documents and lengthy conversations in a single pass.
- Training methodology: Massive multimodal pretraining, supervised fine-tuning and Reinforcement Learning with Curriculum Sampling (RLCS) equip GLM-4.5V for robust, long-chain reasoning and real-world task performance.
The model offers a “Thinking Mode” toggle that lets users balance reasoning depth and speed. With Thinking Mode on, GLM-4.5V executes step-by-step logic ideal for complex workflows such as multi-stage chart analysis or extended document tasks. With it off, users receive rapid, direct answers for routine queries or simple Q&A sessions. This toggle lets developers adjust latency for deeper analyses or faster, high-volume responses.
GLM-4.5V has secured top marks on 41–42 public multimodal benchmarks—including MMBench, AI2D, MMStar and MathVista—outperforming open alternatives and some proprietary solutions on STEM question answering, chart comprehension, GUI operation and advanced video understanding. These results exceed those of many both open and paid models on critical reasoning tasks.
Businesses and research groups have applied GLM-4.5V to defect detection, automated report analysis, digital assistant creation and accessibility improvements, reporting major gains in efficiency and accuracy. Early adopters note significant reductions in manual review time and improved consistency across large-scale inspection tasks.
Published under an MIT license, the model opens up advanced multimodal reasoning that was once restricted to proprietary APIs. The open license grants full rights to use, modify and redistribute the model for academic and commercial use.