AB
AB
AiBoss

All Categories

project

GLM-4.6V - Zhipu Open Source Multimodal Large Model Series

GLM-4.6V is a multimodal large model launched by Zhipu, which includes the basic version GLM-4.6V (106B-A12B) for cloud and high-performance cluster scenarios and the lightweight version GLM-4.6V-Flash (9B) for local deployment and low-latency applications.

What is GLM-4.6V?

GLM-4.6V is a multimodal large-scale model launched by Zhipu, including the basic version GLM-4.6V (106B-A12B) for cloud and high-performance cluster scenarios and the lightweight version GLM-4.6V-Flash (9B) for local deployment and low-latency applications. The model supports long context (128k tokens) and achieves top-tier accuracy in visual understanding for its parameter scale. It is the first to natively integrate tool invocation capabilities into the visual model, realizing a closed loop from visual perception to executable actions. The model is widely used in scenarios such as intelligent image and text creation, image-based shopping, front-end replication, and long document/video understanding, significantly outperforming the previous generation GLM-4.5V while reducing costs by 50%.

Main functions of GLM-4.6V

  • Multimodal understanding and generationThe model can process various input formats such as images, videos, and text to generate high-quality mixed text and image content, which is suitable for content creation, social media publishing and other scenarios.
  • Vision-driven tool callsIt supports using images, screenshots, etc. as tool parameters directly without text descriptions, reducing information loss. It can also handle multimodal results returned by the tool, such as statistical charts and webpage screenshots.
  • Long context processingThe context window has been increased to 128k tokens, enabling it to handle complex documents up to 150 pages long or 1 hour of video content, and supporting cross-document comparison analysis and key event localization in long videos.
  • Intelligent shopping guide and price comparisonIn e-commerce scenarios, it can realize a complete chain from image recognition to product search, price comparison and shopping guide list generation.
  • Front-end replication and interaction debuggingIt supports pixel-level front-end replication, can quickly convert design drafts into runnable web page code, and supports multi-round visual interaction modifications based on screenshots.
  • Multimodal interaction and reasoningIn complex tasks, it can combine visual input and text information to perform reasoning and generate structured output results, making it suitable for a variety of business scenarios.
  • High cost-effectiveness and flexible deploymentCompared to the previous generation model, the cost has been reduced by 50%, and it supports deployment in the cloud, local and various hardware environments to meet the needs of different scenarios.

GLM-4.6V performance

In validation tests on more than 30 multimodal benchmarks such as MMBench, MathVista, and OCR Bench, GLM-4.6V shows significant improvement over the previous generation model, especially in key capabilities such as multimodal interaction, logical reasoning, and long context processing, reaching top-level performance.

  • GLM-4.6V-Flash version 9BOverall performance surpasses Qwen3-VL-8B (8B parameter count), demonstrating higher efficiency and performance in multimodal tasks.
  • 106B parameters, 12B activated GLM-4.6VThe performance is comparable to that of the Qwen3-VL-235B with twice the number of parameters, demonstrating its significant advantage in parameter efficiency and achieving similar or even better performance with fewer computing resources.

GLM-4.6V Model Highlights

  • Self-invoking toolsThe model natively supports tool calls based on visual input, enabling it to handle more complex visual tasks such as mixed text and image layouts, image-based shopping and shopping guides, and agent scenarios.
  • 128k context windowTheoretically, it can understand 150-page complex documents, 200-page PPTs, or one-hour videos, and can process multiple long documents or videos in a single inference.
  • Improved coding skillsThe front-end replication and multi-round visual interaction modification capabilities have been optimized to help developers shorten the "design draft to working page" process.
  • SOTA in the same categoryUnder the same parameter scale, the model achieves state-of-the-art performance in key capabilities such as multimodal interaction, logical reasoning, and long context.

How to use GLM-4.6V

  • Local deploymentDownload the code and model weights from GitHub or other open-source platforms and run them on your local computer or server.
  • Cloud callAccess the Zhipu Open Platform, register an account, obtain an API key, and call the cloud model via network request.
  • Online experienceTo view the results, directly access z.ai or the Zhipu Qingyan APP/web version, select the GLM-4.6V model, upload an image or enter text, and click "Inference".
  • Integration into applications: Integrate the model into your own software or system via API or local deployment to achieve specific functions.
  • Using a reasoning frameworkLoad models into supported frameworks (such as SGLang, transformers, etc.) and run them with hardware (such as GPUs).

Project address for GLM-4.6V

  • GitHub repositoryhttps://github.com/zai-org/GLM-V
  • HuggingFace model libraryhttps://huggingface.co/collections/zai-org/glm-46v
  • Technical Papers: https://z.ai/blog/glm-4.6v

Example of using GLM-4.6V

Example: Input only the topic to generate graphic and text information.

Searching for the Chinese team's performance in the 2025 ITTF Mixed Team World Cup generates a news article with pictures and text.

Case Study: Search for similar products, compare prices, and generate shopping guides

Application scenarios of GLM-4.6V

  • Intelligent graphic creationInput a topic or mixed text and images, and the model will automatically generate well-structured, richly illustrated content suitable for social media, public accounts, and other platforms.
  • Visual-driven shoppingUpload images and issue commands; the model recognizes the shopping intent, searches for similar products, and generates a shopping guide list, thus improving the e-commerce shopping experience.
  • Front-end development assistanceUpload a screenshot of a webpage or a design draft, and the model will be accurately replicated to generate code. It supports multiple rounds of interactive modifications, accelerating the front-end development process.
  • Long document and video comprehensionIt can process long documents or videos, support cross-document comparison and analysis and key event location, and help understand and research complex content.
  • Multimodal intelligent customer serviceIt combines visual and textual information to provide accurate answers and suggestions, supports multi-turn dialogues, and improves customer service efficiency.
Back