Zhipu AI Revolutionizes Web Development with GLM-5V-Turbo

GLM-5V-Turbo: A Major Advancement for Zhipu AI

Zhipu AI has recently introduced GLM-5V-Turbo, a revolutionary multimodal model that promises to transform design mockups into executable front-end code. This model is capable of processing not only text but also images and videos, allowing it to generate code directly from these visual media.

The core of this innovation lies in a proprietary vision encoder, designed to seamlessly integrate perception, planning, and execution into a single workflow. Zhipu AI claims that GLM-5V-Turbo delivers exceptional performance in multimodal coding benchmarks and GUI agents, while maintaining its skills in text-only coding tasks.

A Model Designed to Bridge the Gap Between Vision and Code

GLM-5V-Turbo represents a significant advancement for Zhipu AI, being the company's first multimodal coding base model. Capable of processing images, videos, and text, this model is specifically designed to optimize agent workflows.

The primary goal of GLM-5V-Turbo is to reduce the gap between visual understanding and code generation. Unlike traditional models that focus solely on text, this one analyzes design mockups to produce executable code. Zhipu AI emphasizes that this model integrates perfectly with agents such as Claude Code and OpenClaw, covering the entire cycle from understanding the environment to executing tasks.

The context window management capability is impressive, able to handle up to 200,000 tokens, with a maximum output of 128,000 tokens. Among its features are a reflection mode, streaming output, function calls, and context caching.

Merging Vision and Code into a Single Model

Zhipu AI attributes the remarkable performance of GLM-5V-Turbo to improvements in four key areas: model architecture, training methods, data construction, and tools.

From the outset of training, the model learns to process images and text simultaneously, rather than adding an image recognition module to an existing language model. To achieve this, Zhipu AI developed a new vision encoder named CogViT. Additionally, the model predicts multiple tokens at once during inference, speeding up result production.

Reinforcement learning is used to optimize the model across more than 30 types of tasks, ranging from STEM to video, as well as GUI and coding agents, with the aim of enhancing perception, reasoning, and agent execution.

To address the lack of training data for agents, Zhipu AI has implemented a controllable and verifiable multi-level data system. Agentic meta-skills are integrated from the pre-training phase to improve action prediction and execution from the start.

A new multimodal toolchain extends the agent's capabilities from text interaction to visual interaction. Tools for box drawing, screenshots, and web page reading, including image understanding, complete the perception-planning-execution cycle.

Impressive Performance in Benchmarks

According to Zhipu AI, GLM-5V-Turbo stands out for its top-tier performance in multimodal coding and agent tasks. The model achieves excellent results in code generation from designs, visual code generation, multimodal search, and visual exploration. It shows strong scores on benchmarks like AndroidWorld and WebVoyager, which test an agent's ability to navigate real GUI environments.

GLM-5V-Turbo ranks at the top in most categories of multimodal coding and tool usage. Claude Opus 4.6 excels in certain benchmarks like Flame-VLM-Code and OSWorld.

In purely text-based coding tasks, GLM-5V-Turbo maintains its performance despite its additional visual capabilities, ranking well on the three main benchmarks CC-Bench-V2 (backend, frontend, repo exploration). It also performs well on PinchBench, ClawEval, and ZClawBench, which measure task execution quality. Independent evaluations are still pending.

In text-only coding and agent benchmarks, Claude Opus 4.6 leads, but GLM-5V-Turbo surpasses its own text model GLM-5-Turbo and Kimi K2.5 in several categories.

From Design Mockups to Complete Front-End Projects

GLM-5V-Turbo targets several specific use cases. The model can take design mockups or reference images and generate a complete and executable front-end project. It reconstructs the structure and functionality of wireframes, aiming for perfect visual consistency with high-resolution designs.

When paired with frameworks like Claude Code, the model manages autonomous exploration of GUIs: it autonomously searches for target websites, maps page transitions, collects visual assets and interaction details, and writes code based on what it finds. Zhipu AI describes this as an improvement from "recreating from a screenshot" to "recreating through autonomous exploration."

For debugging, the model captures screenshots of broken pages, automatically identifies rendering issues such as layout shifts, component overlaps, and color mismatches, and then generates correction code. With GLM-5V-Turbo integrated, OpenClaw can also understand website layouts, GUI elements, and diagrams, helping it tackle more complex tasks that combine perception, planning, and execution.

Zhipu AI offers official skills, including image captioning, visual anchoring, document-based writing, CV filtering, and prompt generation, all available on ClawHub. GLM-5V-Turbo is currently available only as an API through the Zhipu AI platform, priced at $1.20 per million input tokens and $4 per million output tokens, the same rate as the text-only GLM-5-Turbo and slightly above the base GLM-5 model. Zhipu AI has not yet announced any open model weights.

Foundations Laid by GLM-5-Turbo and GLM-5

Zhipu AI recently launched GLM-5-Turbo, a text-only model designed for the OpenClaw agent framework that enhances tool calls, instruction tracking, time-controlled tasks, and long task chain execution.

Simultaneously, Zhipu AI introduced ZClawBench, an end-to-end benchmark for agent tasks within the OpenClaw ecosystem. Results show that GLM-5-Turbo significantly outperforms its predecessor, GLM-5, and surpasses Claude Opus 4.6, Gemini 3.1 Pro, MiniMax M2.5, and Kimi K2.5 in several categories. The use of skills within the OpenClaw ecosystem has surged from 26% to 45% in a short time, indicating a growing momentum for modular agent systems, according to Zhipu AI.

Prior to this, Zhipu AI launched GLM-5 in mid-February: an open-source model with 744 billion parameters under MIT license that the company claims rivals Claude Opus 4.5 and GPT-5.2 in coding and agent tasks. GLM-5 achieved 77.8% on SWE-bench Verified, just behind Claude Opus 4.5 at 80.9%. The model also runs on Chinese chips from Huawei and others, as well as on Nvidia GPUs, a significant advantage given U.S. export restrictions.

Alibaba is taking a similar approach with Qwen3.5-Omni, an omnimodal model that processes text, images, audio, and video. Like GLM-5V-Turbo, it generates code from visual inputs but also accepts voice commands.