GLM 5V Turbo is Z.ai's vision-enabled turbo model released April 1, 2026. It turns screenshots and designs into code, debugs visually, and operates GUIs autonomously, combining GLM-5's agentic capabilities with multimodal vision input at a compact parameter size.
import { streamText } from 'ai'
const result = streamText({ model: 'zai/glm-5v-turbo', prompt: 'Why is the sky blue?'})What To Consider When Choosing a Provider
- Configuration: GLM 5V Turbo converts visual designs to code. For best results, provide clean screenshots at sufficient resolution and specify the target framework (React, HTML/CSS, etc.) in your prompt.
- Configuration: You can use GLM 5V Turbo in an iterative loop: render code, screenshot the result, feed it back to the model for corrections. This workflow leverages both vision and coding capabilities.
- Configuration: The compact parameter size means faster inference, but the most complex visual reasoning tasks may benefit from the full GLM-4.6V (106B). Benchmark on your specific use cases.
- Zero Data Retention: AI Gateway does not currently support Zero Data Retention for this model. See the documentation for models that support ZDR.
- Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
When to Use GLM 5V Turbo
Best For
- Design-to-code generation: Screenshots and mockups convert into responsive React components, HTML, and CSS
- Visual debugging: The model examines rendered output, identifies layout issues, and generates fixes
- GUI automation: Real screen environments navigated autonomously for testing and interaction workflows
- Agentic visual coding pipelines: Image understanding combined with autonomous code planning and iteration
- High-volume visual processing: The compact parameter size keeps inference fast and cost-effective
Consider Alternatives When
- Maximum visual reasoning depth: GLM-4.6V (106B) provides the largest vision-language model in the lineup without speed constraints
- Text-only workloads: GLM-5-Turbo offers the same generation's speed without vision overhead
- Simple captioning or classification: A lighter vision model may be more cost-effective for basic image tasks
- Deepest text reasoning: The full GLM-5 text-only variant provides multiple thinking modes without vision input
Conclusion
GLM 5V Turbo bridges vision and code generation in a fast, compact package. For teams building design-to-code pipelines, visual debugging loops, or autonomous GUI agents, it delivers the GLM-5 generation's agentic capabilities with multimodal input at a practical speed and cost profile.
Frequently Asked Questions
What can GLM 5V Turbo do with screenshots?
It converts screenshots and design mockups into responsive code, identifies visual bugs in rendered output, and navigates GUI environments by reading screen elements and performing actions.
How does GLM 5V Turbo compare to GLM-4.6V?
GLM 5V Turbo is a newer, compact vision model focused on coding and GUI tasks. GLM-4.6V is a larger 106B parameter model with broader vision-language capabilities including native multimodal function calling and interleaved image-text generation.
Does GLM 5V Turbo support design-to-code generation?
Yes. It's specifically built for this workflow. Provide a screenshot or design mockup and specify the target framework. The model generates matching responsive components.
What is the context window for GLM 5V Turbo?
200K tokens.
How do I authenticate with GLM 5V Turbo through AI Gateway?
AI Gateway provides a unified API key. No separate Z.ai account is needed. Use the
glm-5v-turbomodel identifier to route requests. BYOK is also supported.Can GLM 5V Turbo operate GUIs autonomously?
Yes. It reads screen elements, interprets visual context, and performs navigation actions in real GUI environments. This makes it useful for automated testing and UI interaction workflows.
What is the pricing for GLM 5V Turbo?
Check the pricing panel on this page for today's numbers. AI Gateway tracks rates across every provider that serves GLM 5V Turbo.
