// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "z-ai/glm-4.6"
}).then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms", {
model: "z-ai/glm-4.6"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://api.puter.com/puterai/openai/v1/",
api_key="YOUR_PUTER_AUTH_TOKEN",
)
response = client.chat.completions.create(
model="z-ai/glm-4.6",
messages=[
{"role": "user", "content": "Explain quantum computing in simple terms"}
],
)
print(response.choices[0].message.content)
curl https://api.puter.com/puterai/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_PUTER_AUTH_TOKEN" \
-d '{
"model": "z-ai/glm-4.6",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
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.
Context Window 203K
tokens
Max Output 131K
tokens
Input Cost $0.22
per million tokens
Output Cost $1
per million tokens
Input text
modalities
Tool Use Yes
Release Date Sep 30, 2025
Model Playground
Try GLM 4.6 instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How GLM 4.6 performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 63.2% |
| Humanity's Last Exam Cross-domain reasoning | 5.5% |
| LiveCodeBench Recent coding problems | 56.1% |
| AIME 2025 Advanced math exam | 44.3% |
| IFBench Instruction following | 36.7% |
| LCR Long-context reasoning | 26.3% |
| Terminal-Bench Hard Agentic terminal tasks | 28.8% |
| τ²-Bench Tool use / agents | 76.9% |
Scores sourced from Artificial Analysis.
Find other Z.AI models →
GLM 5.3 Flash
GLM 5.3 Flash is a mixture-of-experts model from Z.ai (Zhipu AI), the first natively multimodal release in the GLM-5 family, accepting text and image input. It has 320 billion total parameters with 18 billion active per token, and a hybrid sparse-and-linear attention architecture that Z.ai says cuts attention compute about 3x and KV cache size about 4.4x compared with GLM-5.3. Through this API it offers a 1,000,000 token context window and up to 131,100 output tokens, priced at $0.08 per million input tokens and $0.25 per million output tokens. It supports function calling, and thinking is always on and cannot be disabled. GLM 5.3 Flash beats GLM-5.2 on Z.ai's reported benchmarks (DeepSWE v1.1, 63.4 vs 46.2; AutomationBench, 48.8 vs 26.2) and approaches Claude Opus 4.8 on coding and agentic tasks. Model weights are released under the MIT license.
ChatGLM 5.3
GLM 5.3 is Z.ai's latest open-weight foundation model, a post-training refresh of GLM-5.2 released August 14, 2026. It keeps the same 744B-parameter Mixture-of-Experts architecture (40B active) and 1M-token context window; all reported gains come from expanded reinforcement-learning post-training rather than a new base model. On Z.ai's internal Code Bench, GLM 5.3 scores 50% higher than GLM-5.2. It also improves on Terminal-Bench 3.0 (4.6 to 28.3) and DeepSWE v1.1 (46.2 to 66.9). Cybersecurity ability grew alongside coding: CyberGym rose from 77.2% to 84.5%, and ExploitBench more than doubled, from 24.4% to 54.4%. The model supports three reasoning-effort levels (low, high, max) via the API, and thinking can no longer be disabled. It is best suited to long-horizon coding agents, repository-scale engineering tasks, and vulnerability research.
ChatGLM 5.2 Fast
GLM 5.2 Fast is the high-speed serving tier of Z.ai's GLM-5.2, running the same model weights on inference infrastructure tuned for higher throughput. Output quality matches the standard GLM-5.2 endpoint; serving speed and price are the differences. It keeps the full 1M-token context window and 128K max output, along with tool calling, structured output, streaming, optional thinking mode with adjustable reasoning effort, and implicit prompt caching. Providers report roughly 2x the throughput of their standard GLM-5.2 endpoints, with peaks measured at 446 tokens per second. Because the weights are identical, it inherits GLM-5.2's coding results, including 62.1 on SWE-bench Pro. The tradeoff is a higher per-token price than standard GLM-5.2. A fit for agent loops that chain many model calls, real-time coding assistants, and other latency-sensitive workloads.
Frequently Asked Questions
You can access GLM 4.6 by Z.AI through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add GLM 4.6 to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.
| Price per 1M tokens | |
|---|---|
| Input | $0.22 |
| Output | $1 |
GLM 4.6 was created by Z.AI and released on Sep 30, 2025.
GLM 4.6 supports a context window of 203K tokens. For reference, that is roughly equivalent to 406 pages of text.
GLM 4.6 can generate up to 131K tokens in a single response.
GLM 4.6 accepts the following input types: text. It produces: text.
Yes, GLM 4.6 supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
GLM 4.6 scores 16.7 on the Artificial Analysis Intelligence Index, outperforming 61% of tracked models. On math, it scores 44.3 (outperforms 44% of models).
Yes — the GLM 4.6 API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Get started with Puter.js
Add GLM 4.6 to your app without worrying about API keys or setup.
Read the Docs View Tutorials