Z.AI API
Access Z.AI instantly with Puter.js, and add AI to any app in a few lines of code without backend or API keys.
// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain AI like I'm five!", {
model: "z-ai/glm-4-32b"
}).then(response => {
console.log(response);
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain AI like I'm five!", {
model: "z-ai/glm-4-32b"
}).then(response => {
console.log(response);
});
</script>
</body>
</html>
List of Z.AI Models
Z.AI: GLM 5
z-ai/glm-5
GLM-5 is Zhipu AI's (Z.ai) fifth-generation flagship open-weight foundation model with 744B total parameters (40B active) in a Mixture of Experts architecture, designed for agentic engineering, complex systems coding, and long-horizon agent tasks. It achieves state-of-the-art performance among open-weight models on coding and agentic benchmarks like SWE-bench Verified and Terminal Bench 2.0, approaching Claude Opus 4.5-level capability.
ChatZ.AI: GLM 4.7 Flash
z-ai/glm-4.7-flash
GLM 4.7 Flash is designed for speed and efficiency while maintaining strong performance. It features a 200K token context window, making it suitable for processing long documents and generating extended responses.
ChatZ.AI: GLM 4.6V
z-ai/glm-4.6v
GLM-4.6V is a 106B vision-language model featuring native multimodal Function Calling—the first to directly pass images as tool inputs. It supports 128K context for processing 150+ page documents or 1-hour videos in a single pass.
ChatZ.AI: GLM 4.7
z-ai/glm-4.7
GLM-4.7 is Zhipu AI's latest ~400B flagship released December 2025, optimized for coding with 200K context and 128K output. It scores 73.8% on SWE-bench and 95.7% on AIME 2025.
ChatZ.AI: GLM 4.6
z-ai/glm-4.6
GLM-4.6 is Zhipu AI's 355B-parameter (32B active) flagship text model with 200K context, excelling at coding, agentic workflows, and search tasks. It's 15% more token-efficient than GLM-4.5 and ranks as the #1 domestic model in China.
ChatZ.AI: GLM 4.5V
z-ai/glm-4.5v
GLM-4.5V is a 106B-parameter vision-language model achieving SOTA on 42 multimodal benchmarks, capable of image/video reasoning, GUI agent tasks, document parsing, and visual grounding. It features a thinking mode toggle and 64K multimodal context under MIT license.
ChatZ.AI: GLM 4.5
z-ai/glm-4.5
GLM-4.5 is Zhipu AI's flagship 355B-parameter open-source model (32B active) designed for agentic AI applications with dual thinking/non-thinking modes. It excels at reasoning, coding, and tool use, ranking 3rd globally among all models on combined benchmarks under MIT license.
ChatZ.AI: GLM 4.5 Air
z-ai/glm-4.5-air
GLM-4.5-Air is a compact 106B-parameter variant (12B active) of GLM-4.5, offering competitive agentic performance with significantly lower resource requirements. It supports the same dual reasoning modes and 128K context window as its larger sibling.
ChatZ.AI: GLM 4 32B
z-ai/glm-4-32b
GLM-4-32B is a 32-billion parameter bilingual (Chinese-English) foundation model by Zhipu AI, pre-trained on 15TB of reasoning-focused data. It delivers performance comparable to GPT-4o on code generation, function calling, and Q&A tasks while remaining deployable on accessible hardware.
Frequently Asked Questions
The Z.AI API gives you access to models for AI chat. Through Puter.js, you can start using Z.AI models instantly with zero setup or configuration.
Puter.js supports a variety of Z.AI models, including Z.AI: GLM 5, Z.AI: GLM 4.7 Flash, Z.AI: GLM 4.6V, and more. Find all AI models supported by Puter.js in the AI model list.
With the User-Pays model, users cover their own AI costs through their Puter account. This means you can build apps without worrying about infrastructure expenses.
Puter.js is a JavaScript library that provides access to AI, storage, and other cloud services directly from a single API. It handles authentication, infrastructure, and scaling so you can focus on building your app.
Yes — the Z.AI API through Puter.js works with any JavaScript framework, Node.js, or plain HTML. Just include the library and start building. See the documentation for more details.