GLM-5V-Turbo

Z.AI released GLM-5V-Turbo as its first multimodal coding foundation model, built for vision-based coding and agentic tasks with native support for image, video, and text inputs. The model targets long-horizon planning, complex coding, and full perceive-plan-execute agent loops, per the Agent Native analysis. GLM-5V-Turbo was trained with a fully fused text+vision pipeline from pretraining through fine-tuning, using a CogViT visual encoder for image and video understanding. The analysis reports a BridgeBench SpeedBench score of 5 at 221.2 tokens/sec and notes synergy with Claude Code and OpenClaw, positioning it for GUI agents and autonomous UI exploration workflows.

The GLM-V Team published "GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents" on arXiv (2604.26752), submitted 29 Apr 2026 with a v3 revision on 12 May 2026. The paper positions GLM-5V-Turbo as a native foundation model integrating multimodal perception directly into reasoning, planning, tool use, and execution rather than as an auxiliary interface to a language model. This creator-attributed report is the primary technical source for the model. According to the abstract, the report covers improvements across model design, multimodal training, reinforcement learning, toolchain expansion, and integration with agent frameworks. The authors frame agentic capability as requiring perception of heterogeneous inputs including images, videos, webpages, documents, and GUIs. The paper is authored by the GLM-V Team led by Wenyi Hong, establishing direct creator attribution separate from Z.AI's serving role.

Z.AI's official documentation introduces GLM-5V-Turbo as the provider's first multimodal coding foundation model, built for vision-based coding tasks. It natively processes images, video, text, and files while outputting text, with a 200K context window and 128K maximum output tokens. The model supports multiple thinking modes, vision comprehension, streaming output, function calling, and context caching. It is optimized for agent workflows, integrating with Claude Code and OpenClaw for frontend recreation from mockups, GUI autonomous exploration, screenshot-based code debugging, and full agentic task loops.

The Baidu Baike encyclopedia entry records GLM-5V-Turbo as a native multimodal programming base model released by Zhipu AI on April 2, 2026. It lists the model as capable of natively understanding multimodal inputs such as images, videos, design drafts, and document layouts, and as supporting multimodal tool invocation Development history confirms an April 2, 2026 release by Zhipu AI, with stated capabilities spanning programming, long-term planning, and operation execution across text, image, and video modalities. The entry frames the model's purpose as enabling AI Agents to handle diverse input forms ranging from text to design dra

The alphaXiv technical paper frames GLM-5V-Turbo as a step toward native foundation models for multimodal agents, arguing that agentic capability depends not only on language reasoning but also on perceiving, interpreting, and acting over heterogeneous contexts such as images, videos, webpages, documents, and GUIs. Unl The paper summarizes main improvements across model design, multimodal training, reinforcement learning, toolchain expansion, and integration with agent frameworks. These developments yield strong performance in multimodal coding, visual tool use, and framework-based agentic tasks while preserving competitive text-only