ZCode is the official agentic development environment for GLM-5.2, built for long-running coding tasks with stable context, file edits, terminal and browser state, Git review, mobile Remote, bot control, BYOK, and macOS, Windows, and Linux apps.
GLM-5-Turbo is Z.ai’s high-speed variant of GLM-5, deeply optimized for OpenClaw from the training stage. It excels at precise tool calling, complex command following, scheduled and persistent tasks, and long-chain execution with near-zero hallucinations. Faster, more reliable, and purpose-built for real agent workflows.
We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a solid 1M-token context.
GLM-5V-Turbo is Z.AI's first multimodal coding model. It understands images, video, files, and UI layouts, then turns that visual context into runnable code, debugging help, and stronger agent workflows with Claude Code and OpenClaw.
Stop hunting for startup ideas.
Start uncovering the problems people are actively trying to solve.
Problem Miner
Discover Pain. Validate Demand. Build With Confidence. ⛏️🚀
GLM-4.5 is a new 355B parameter open-weight MoE model (32B active). It delivers state-of-the-art performance on reasoning, code, and agentic tasks. Both the 355B flagship and a 106B Air version are now available, featuring dual-mode inference.
GLM-4.6V is GLM's newest open-source multimodal model with a 128k context window. It features native function calling, bridging visual perception with executable actions for complex agentic workflows like web search and coding.
A 744B MoE model (40B active) built for complex systems & agentic tasks. #1 open-source on Vending Bench 2, narrowing the gap with Claude Opus 4.5. Features DeepSeek Sparse Attention and "slime" RL infra.
A lightweight (0.9B) professional OCR model. Achieves SOTA (94.6 on OmniDocBench) on complex layouts, tables, and handwriting. Supports vLLM/SGLang for ultra-fast inference.
GLM-Image combines a 9B Auto-regressive model with a 7B Diffusion decoder. This hybrid architecture excels at knowledge-dense generation, perfect for posters, diagrams, and precise text rendering. Open-source and ready for T2I & I2I tasks.
