Use GLM models in AutoClaw

GLM-5.3-Flash is the first native multimodal model in the GLM-5 series, built for efficient coding and long-horizon Agent tasks. Its hybrid sparse and linear attention architecture preserves accurate long-context capabilities while significantly reducing compute overhead, outperforming GLM-5.2 across multiple benchmarks and real-world tasks. In AutoClaw, GLM-5.3-Flash is designed for frequent, lightweight tasks where timely feedback matters, keeping everyday work moving from understanding to delivery.

Image, screenshot, and visual-document understanding; Fast chart, file, and information processing; Documents, reports, and content workflows; Frequent, multi-step everyday tasks; Work that benefits from fast feedback and iteration

GLM-5.3 is Z.AI's latest flagship model for complex engineering and long-horizon agent tasks. It goes beyond generating code—it can stay focused across large projects, manage multi-step dependencies, use tools, and adapt as the work progresses. In AutoClaw, GLM-5.3 is best suited to demanding tasks that require less manual breakdown and more end-to-end ownership, from understanding the problem to execution, validation, and delivery.

Complex software engineering; End-to-end product development; Large codebases and refactoring; Tool-heavy, multi-step workflows; Long-running research and technical tasks

GLM-5.2 is designed for project-scale context and extended work. It can process large codebases, long documents, and tasks with substantial background information while retaining the details that matter. In AutoClaw, GLM-5.2 can work across your files and task history without requiring you to repeat the same context at every step. It is a strong fit for projects that involve ongoing analysis, revision, and validation.

Large projects and codebases; Long-document analysis; Deep research and synthesis; Multi-stage development work; Context-heavy workflows

GLM-5-Turbo is designed for complex workflows that involve multiple connected steps. It can stay aligned with the task, manage dependencies, and work with tools as the workflow progresses. In AutoClaw, it is a practical choice for structured everyday work—from gathering information and processing files to carrying out actions and organizing the results with less repeated guidance.

Workflow automation; Multi-tool tasks; Batch information processing; Repeatable business processes; Extended task execution

GLM-5V-Turbo brings visual understanding and visual coding to AutoClaw. It can interpret interfaces, screenshots, images, video, text, and files—not just written prompts. In AutoClaw, visual context can become the starting point for further work: analyzing a product screen, understanding a design, locating a UI issue, updating code, or producing a structured deliverable.

Screenshot and interface understanding; Design-to-code workflows; Document and chart analysis; Visual debugging; Multimodal agent workflows