Z.ai GLM.

Native visual capabilities enable the model to observe interfaces, rendering results, and interaction feedback—creating a closed loop across code, browsers, and GUIs. Efficient hybrid architecture: Combines linear and sparse attention with 320B total parameters and 18B activated, significantly reducing compute and KV-cache requirements. Beyond coding: Supports office document and financial research workflows, autonomously breaking down goals, using tools, and refining outputs. Learn more in our documentation .*

Stronger Coding Capabilities: GLM-5.3 delivers a significant improvement in coding capabilities, achieving a 50% gain over GLM-5.2 on Z.ai Code Bench and reaching state-of-the-art (SOTA) performance among open-source models on public benchmarks, including Terminal Bench 3.0. Emergent Cybersecurity Capabilities: GLM-5.3 matches Mythos 5 in white-box code review and vulnerability discovery. In collaboration with multiple cybersecurity teams, it has been tested on real-world targets and has identified a total of 2,436 vulnerabilities, including 1,097 medium- and high-severity vulnerabilities.Learn more in our documentation .*

Supports 1M lossless context, significantly improving long-horizon task capabilities and reducing context drift and goal forgetting in complex tasks Achieves open-source SOTA performance on coding and long-horizon task benchmarks, delivering more stable results in complex system engineering and deep debugging Significantly improves the real-world developer experience, with more reliable project-level context handling, adherence to engineering standards, and multi-platform development. Learn more in our documentation .*

Designed for long-horizon tasks, GLM-5.1 can work independently for up to 8 hours in a single run, enabling a full loop from planning and execution to iterative refinement and final delivery. It demonstrates stronger engineering intelligence across autonomous planning, sustained execution, bug fixing, and strategy iteration, while achieving comprehensive capability alignment with Claude Opus 4.6. Built with multi-turn SFT, RL, and a process-quality evaluation framework, GLM-5.1 further improves stability, consistency, and tool use over extended tasks. Learn more in our documentation .*

Designed for vision-based coding and claw-style Agent workflows, GLM-5V-Turbo brings native multimodal understanding to images, video, and text, enabling stronger planning, coding, and action execution in visually grounded tasks. It strengthens multimodal tool use and GUI task execution, allowing the model to better interpret interfaces, design mocks, documents, and charts, plan the next action, and complete end-to-end Agent workflows while maintaining strong pure-text coding and reasoning performance. Learn more in our documentation .*

Designed for high-throughput OpenClaw lobster workloads, GLM-5-Turbo focuses on improving stability and efficiency in long-chain Agent tasks, enabling smoother execution for complex, multi-step workflows. It strengthens tool and Skills integration and enhances complex instruction decomposition, allowing the model to better identify task goals, plan execution steps, coordinate across multiple agents, and maintain temporal consistency in extended tasks.Learn more in our documentation .*

Designed for complex system engineering and long-range Agent tasks, GLM-5 shifts the paradigm from coding to engineering, demonstrating strong deep-reasoning performance in backend architecture, complex algorithms, and stubborn bug fixing. It directly benchmarks against Claude Opus 4.5 in code-logic density and systems-engineering capability, and integrates DeepSeek Sparse Attention for higher token efficiency while preserving long-context quality.Learn more in our documentation .*

We’ve launched GLM-OCR, a compact and high-performance optical character recognition model powered by the self-developed CogViT and GLM-0.5B encoder-decoder architecture, enabling efficient cross-modal alignment through its dedicated connection layer. The update leverages CLIP pre-training on billions of image-text pairs to deliver robust visual semantic understanding and key token extraction capabilities, while maintaining a lightweight design for fast inference. Learn more in our documentation .*

We’ve launched GLM-4.7-Flash, a lightweight and efficient model designed as the free-tier version of GLM-4.7, delivering strong performance across coding, reasoning, and generative tasks with low latency and high throughput. The update brings competitive coding capabilities at its scale, offering best-in-class general abilities in writing, translation, long-form content, role play, and aesthetic outputs for high-frequency and real-time use cases. Learn more in our documentation .*

We’ve launched GLM-Image, a state-of-the-art image generation model built on a multimodal architecture and fully trained on domestic chips, combining autoregressive semantic understanding with diffusion-based decoding to deliver high-quality, controllable visual generation. The update significantly enhances performance in knowledge-intensive scenarios, with more stable and accurate text rendering inside images, making GLM-Image especially well suited for commercial design, educational illustrations, and content-rich visual applications.Learn more in our documentation .*

We’ve released GLM-4.7, our foundation model with significant improvements in coding, reasoning, and agentic capabilities. It delivers more reliable code generation, stronger long-context understanding, and improved end-to-end task execution across real-world development workflows. The update brings open-source SOTA performance on major coding and reasoning benchmarks, enhanced agentic coding for goal-driven, multi-step tasks, and improved front-end and document generation quality. Learn more in our documentation .*

We’ve launched AutoGLM-Phone-Multilingual, our latest multimodal mobile automation framework that understands screen content and executes real actions through ADB. It enables natural-language task execution across 50+ mainstream apps, delivering true end-to-end mobile control. The update introduces multilingual support (English & Chinese), enhanced workflow planning capabilities, and improved task execution reliability. Learn more in our documentation .*

We’ve launched GLM-ASR-2512, our ASR model, delivering industry-leading accuracy with a Character Error Rate of just 0.0717, and significantly improved performance across real-world multilingual and accent-rich scenarios. The update introduces enhanced custom dictionary support and expanded specialized terminology recognition. Learn more in our documentation .*

We’re excited to introduce GLM-4.6V, Z.ai’s latest iteration in multimodal large language models. This version enhances vision understanding, achieving state-of-the-art performance in tasks involving images and text. The update also expands the context window to 128K, enabling more efficient processing of long inputs and complex multimodal tasks. Learn more in our documentation .*

We’ve launched GLM-4.6, the flagship coding model, showcasing enhanced performance in both public benchmarks and real-world programming tasks, making it the leading coding model in China. The update also expands the context window to 200K, improving its ability to handle longer code and complex agent tasks. Learn more in our documentation .*

We’ve launched GLM-4.5V, a 100B-scale open-source vision reasoning model, supporting a broad range of visual tasks including video understanding, visual grounding, GUI agents and etc. The update also adds a new thinking mode. Learn more in our documentation .*

We’ve launched GLM Slide/Poster Agent, an AI-powered creation agent that combines information retrieval, content structuring, and visual layout design to generate professional-grade slides and posters from natural language instructions. The update also brings a seamless integration of content generation with design conventions. Learn more in our documentation .*

We’ve launched GLM-4.5, our latest native agentic LLM, delivering doubled parameter efficiency and strong reasoning, coding, and agentic capabilities. It also offers seamless one-click compatibility with the Claude Code framework. Learn more in our documentation .*

We’ve launched CogVideoX-3, an incremental upgrade to our video generation model with improved quality and new features. It adds support for start and end frame synthesis. Learn more in our documentation .*