GLM-5V-Turbo: Zhipu's Multimodal Vision-Coding Foundation Model
GLM-5V-Turbo was released on April 2, 2026 as Zhipu's multimodal vision-coding foundation model. It combines a CogViT vision encoder with Multi-Token Prediction (MTP) and supports image, video, text, and file input across a 200K context window with up to 128K tokens of output. The model was trained with joint reinforcement learning across 30+ tasks, giving it strong performance on visual understanding, document-grounded writing, and code-generation workflows.
- GLM-5V-Turbo supports image, video, text, and file input with a 200K context window and 128K max output.
- Uses CogViT vision encoder and Multi-Token Prediction (MTP) for efficient multimodal reasoning.
- Trained with joint RL across 30+ tasks for strong visual understanding and code generation.
Multimodal architecture overview
GLM-5V-Turbo is built on a multimodal architecture that integrates the CogViT vision encoder with Zhipu's language model backbone. Multi-Token Prediction (MTP) allows the model to generate multiple tokens per forward pass, which improves throughput for long-form output without sacrificing quality.
The model accepts image, video, text, and file inputs within a single 200K-token context window, and can produce up to 128K tokens of output. This makes it suitable for tasks that require grounding text generation in visual or document context, such as screenshot-to-code, document analysis, or visual debugging.
- Simultaneous file + video + image understanding in a single request is not currently supported.
- The model works best when the primary modality is clearly specified in the prompt.
| Modality | Input | Output | Notes |
|---|---|---|---|
| Text | Yes | Yes | Full language understanding and generation |
| Image | Yes | N/A | Processed through CogViT vision encoder |
| Video | Yes | N/A | Frame-level processing within context window |
| File / Document | Yes | N/A | PDF, code files, and structured documents |
| Code | Yes | Yes | Full code generation and editing capabilities |
Benchmark performance for vision tasks
GLM-5V-Turbo posts competitive results on multimodal and vision-language benchmarks. The joint RL training across 30+ tasks gives it a broad skill surface that generalizes well to real-world coding and document workflows.
| Benchmark | GLM-5V-Turbo | Notes |
|---|---|---|
| DocVQA | Strong | Document visual question answering |
| ChartQA | Strong | Chart and graph understanding |
| TextVQA | Strong | Text recognition in natural images |
| MathVista | Competitive | Mathematical visual reasoning |
| RealWorldQA | Competitive | Real-world image understanding |
Accuracy on combined vision+coding tasks for the current generation of multimodal models.
Official Z.AI evaluation.
Official OpenAI evaluation.
Official Anthropic evaluation.
Source: Official GLM-5V-Turbo docs.
Official skill capabilities
GLM-5V-Turbo exposes a set of built-in skills designed for practical enterprise and developer workflows. Each skill is tuned through the joint RL training process and can be invoked directly through the API.
| Skill | Description | Use case |
|---|---|---|
| Image Captioning | Generates detailed natural-language descriptions of images | Accessibility, cataloging, content generation |
| Visual Grounding | Identifies and localizes specific objects or regions in an image | UI testing, visual debugging, annotation |
| Document-Grounded Writing | Generates text grounded in uploaded document content | Report drafting, summarization, compliance |
| Resume Screening | Extracts structured information from resume documents | HR automation, candidate ranking |
| Prompt Generation | Creates optimized prompts based on visual or text input | Workflow automation, prompt engineering assistance |
Integration and API access
GLM-5V-Turbo is available through the same Zhipu API and DevPack subscription as GLM-5.1. The multimodal inputs are passed as structured content blocks within the standard chat completion format, making it straightforward to integrate into existing workflows that already use GLM models.
- Available through the BigModel API at docs.bigmodel.cn.
- Supported in the DevPack subscription alongside GLM-5.1.
- Multimodal inputs use standard structured content blocks.
- Does not support simultaneous file + video + image understanding in a single request.
Test GLM-5V-Turbo on your own visual coding tasks
The easiest way to evaluate GLM-5V-Turbo is through the Zhipu API or DevPack. Start with a screenshot-to-code or document-grounded writing task to see the multimodal capabilities in action.
Sources and official links
Frequently asked questions
Can GLM-5V-Turbo process images and video at the same time?
No. GLM-5V-Turbo supports image, video, text, and file input individually, but does not currently support simultaneous file + video + image understanding in a single request.
How does GLM-5V-Turbo differ from GLM-5?
GLM-5 is a text-only MoE language model. GLM-5V-Turbo adds the CogViT vision encoder and multimodal capabilities, supporting image, video, and file input. It also has a larger 200K context window and 128K max output compared to GLM-5's 128K context.
What is CogViT?
CogViT is Zhipu's vision encoder, designed to process visual inputs (images, video frames) and convert them into representations that the language model backbone can reason over. It is the core component that enables GLM-5V-Turbo's multimodal capabilities.