Z.ai: GLM 4.5V

GLM-4.5V processes images and video frames through a unified vision-language encoder that maintains temporal coherence across sequential frames. The model uses a Mixture-of-Experts architecture where only 12B of 106B parameters activate per inference, routing visual tokens and text through specialized expert layers for efficient multi-modal fusion. This enables understanding of spatial relationships, object tracking, and temporal dynamics within video sequences without requiring separate video preprocessing pipelines.

GLM-4.5V generates natural language descriptions of images by encoding visual features through its vision encoder and decoding them via the language model head. The model produces detailed captions that go beyond object detection to include spatial relationships, actions, attributes, and contextual understanding. The MoE architecture allows selective activation of language generation experts based on caption complexity, optimizing for both brevity and detail depending on prompt instructions.

GLM-4.5V answers natural language questions about image content through a visual grounding mechanism that maps text tokens to image regions. The model maintains conversation context across multiple turns, allowing follow-up questions that reference previous answers or ask for clarification. The MoE architecture routes question-answering experts based on query complexity, enabling efficient handling of both simple factual questions and complex reasoning tasks requiring multi-step inference.

GLM-4.5V analyzes documents, tables, charts, and infographics by recognizing layout structure, text hierarchy, and visual elements. The model extracts structured information (tables, key-value pairs, hierarchies) and can convert visual data representations (charts, graphs) into textual or JSON formats. The vision encoder is optimized for document-specific patterns like text alignment, column detection, and chart type recognition, enabling accurate extraction without OCR preprocessing.

GLM-4.5V identifies objects within images and reasons about their spatial relationships, sizes, positions, and interactions. The model can count objects, describe relative positions ('left of', 'above', 'overlapping'), and infer relationships based on visual proximity or context. The vision encoder produces spatially-aware embeddings that enable the language model to ground references to specific image regions, supporting queries like 'How many people are standing to the left of the tree?'

GLM-4.5V can generate images from text descriptions by leveraging its vision-language understanding to ground concepts in visual space. The model uses its learned visual representations to synthesize images that match textual specifications, guided by the same multimodal embeddings used for understanding. The MoE architecture allows selective activation of generation experts based on prompt complexity, enabling efficient synthesis of both simple and complex visual concepts.

GLM-4.5V computes similarity between images and text by projecting both into a shared embedding space learned during multimodal training. The model can rank images by relevance to text queries, find similar images to a reference image, or match text descriptions to visual content. The unified embedding space enables efficient retrieval without separate encoding passes, leveraging the MoE architecture to route similarity computation through specialized experts.

GLM-4.5V can produce step-by-step reasoning about visual content, breaking down complex image understanding tasks into intermediate reasoning steps. The model generates explicit chains of thought that explain how it arrived at conclusions about images, enabling transparency and verification of visual reasoning. The language model component naturally supports this through its training on reasoning tasks, while the vision encoder grounds each reasoning step in visual evidence.

GLM-4.5V can process multiple images and text inputs in a single request while preserving context across inputs. The model maintains conversation state and visual references across multiple turns, enabling workflows where earlier images inform interpretation of later ones. The MoE architecture efficiently handles variable-length input sequences by routing different input types through specialized experts, reducing redundant computation.