GLM 5V Turbo is a vision-language foundation model purpose-built for coding tasks, marking Z.ai's first natively multimodal design rather than a vision module bolted onto a text model. It accepts images, video, text, and file inputs and produces text output, combining a CogViT vision encoder with a Multi-Token Prediction architecture on top of the Turbo variant of the GLM-5 line. The design intent is to close the loop between visual perception and code generation: a screenshot, mockup, wireframe, or UI recording is parsed for layout, color, component hierarchy, and interaction logic, then emitted as a runnable front-end project. The model is positioned for long-horizon planning, complex coding, and action execution, and is deeply integrated with agent frameworks such as Claude Code and OpenClaw, where it handles environment understanding, action planning, and tool invocation end to end.
Practical strengths show up most clearly on the Design2Code benchmark, where GLM 5V Turbo reaches 94.8, opening a roughly seventeen-point lead over comparable frontier multimodal models on translating visual designs into HTML and CSS, and reports place it ahead of Claude Opus 4.6 on multimodal evaluation suites. The same family includes the text-only GLM-5 at 744B parameters and the text-optimized GLM-5-Turbo, with GLM 5V Turbo adding native multimodal understanding without changing the underlying API surface or pricing tier. It supports multiple thinking modes, streaming responses, function calling, and intelligent context caching, and ships with a large working memory suited to multi-step agentic sessions. Forward-looking, it is aimed at teams that want an agent-ready vision coder that can ingest screenshots and video, drive tool use, and ship production front-end output in a single pass.
