Z.AI: GLM 4 32B 0414 128K
z-ai/glm-4-32b-0414-128k
Access GLM 4 32B 0414 128K from Z.AI using Puter.js AI API.
Get Started// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "z-ai/glm-4-32b-0414-128k"
}).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-4-32b-0414-128k"
}).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-4-32b-0414-128k",
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-4-32b-0414-128k",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
GLM-4-32B-0414-128K is a 32B-parameter dense language model from Z.ai with an extended 128K-token context window. Pre-trained on 15 trillion tokens of high-quality data — including substantial reasoning-focused synthetic data — it was further refined with rejection sampling and reinforcement learning for instruction following, code generation, and function calling.
It supports bilingual Chinese-English usage and is optimized for tasks like tool use, search-grounded Q&A, and structured output generation. Performance is competitive with models in the GPT and DeepSeek V3/R1 class at a fraction of the parameter count.
A strong choice for cost-sensitive workloads that need long-context reasoning, multi-file code editing, or reliable JSON output without stepping up to the larger MoE models in the GLM family.
Context Window 128K
tokens
Max Output 16K
tokens
Input Cost $0.1
per million tokens
Output Cost $0.1
per million tokens
Input text
modalities
Tool Use Yes
Release Date Apr 14, 2025
Model Playground
Try GLM 4 32B 0414 128K instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
More AI Models From Z.AI
GLM 5.1
GLM-5.1 is a frontier-class reasoning model from Z.ai (formerly Zhipu AI), built as a post-training refinement of GLM-5 with a focus on coding and agentic tasks. It uses a 744B-parameter Mixture-of-Experts architecture with 40B active parameters per token and supports a 200K context window. GLM-5.1 scored 58.4 on SWE-Bench Pro, surpassing GPT-5.4 (57.7) and Claude Opus 4.6 (57.3), and reached 95.3 on AIME 2026. It excels at long-horizon agentic workflows, multi-step tool use, and complex software engineering tasks. The model is text-only — no image or audio input.
ChatGLM 5 Turbo
GLM-5 Turbo is a foundation model by Z.ai optimized for fast inference and agent-driven workflows, excelling at tool invocation, complex instruction decomposition, and long-chain task execution in OpenClaw scenarios. It is built on top of the GLM-5 architecture (744B parameters, 40B active) with DeepSeek Sparse Attention for reduced deployment cost and up to 205K token context. GLM-5 Turbo supports reasoning/thinking mode and is designed for real-world multi-step agentic tasks including scheduled, persistent, and high-throughput operations.
ChatGLM 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.
Frequently Asked Questions
You can access GLM 4 32B 0414 128K 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 32B 0414 128K 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 | $0.1 |
| Output | $0.1 |
GLM 4 32B 0414 128K was created by Z.AI and released on Apr 14, 2025.
GLM 4 32B 0414 128K supports a context window of 128K tokens. For reference, that is roughly equivalent to 256 pages of text.
GLM 4 32B 0414 128K can generate up to 16K tokens in a single response.
GLM 4 32B 0414 128K accepts the following input types: text. It produces: text.
Yes, GLM 4 32B 0414 128K supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Yes — the GLM 4 32B 0414 128K 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 32B 0414 128K to your app without worrying about API keys or setup.
Read the Docs View Tutorials