Z.AI: GLM 4 32B 0414 128K
z-ai/glm-4-32b-0414-128k
Try GLM 4 32B 0414 128K for free in your browser, and add it to your app for free with Puter.js AI API.
Try it free Add to your app// 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>
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 96K
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
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This playground uses the Puter.js AI API — no API keys or setup required.
More AI Models From Z.AI
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GLM 5.3 FlashX Legal is a closed-source variant of Z.ai's GLM-5.3-FlashX tuned for legal work, including legal research, contract review, and document drafting. It accepts text, image, and video input and returns text. It has a 1,048,576-token context window, which fits long contracts and case files in a single request, and up to 131,072 output tokens. It supports function calling. Pricing is $0.37 per million input tokens and $1.25 per million output tokens, the same as GLM-5.3-FlashX. On Vals AI's Legal Research Bench it scores 41.83, according to DataLearner. Z.ai has not published other benchmark results for it. It suits developers building contract analysis tools, legal research assistants, and drafting workflows that process long documents.
ChatGLM 5.3 Prime
GLM 5.3 Prime is a high-speed serving tier built on Z.ai's GLM-5.3, offered through third-party inference platforms rather than as an official Z.ai product listing. It runs the same 744-billion-parameter Mixture-of-Experts weights (40 billion active) as standard GLM-5.3, on infrastructure tuned for higher throughput. Vendor reports put its output throughput at 1.5 to 2 times the standard GLM-5.3 endpoint, at roughly double the per-token price, a pattern also seen with GLM-5.2's own "Fast" tier. No independent benchmark of the Prime endpoint exists, but since the weights are unchanged, it should inherit GLM-5.3's reported gains over GLM-5.2, including a 50% jump on Z.ai's Code Bench and gains on Terminal-Bench 3.0 and DeepSWE v1.1. It supports a 1,000,000-token context window, 128,000 max output tokens, three reasoning-effort levels, and function calling. Best suited for agent loops and coding assistants that chain many model calls and are latency-sensitive, where the throughput gain offsets cost.
ChatGLM 5.3 FlashX
GLM 5.3 FlashX is Z.ai's high-speed serving tier for GLM-5.3-Flash, using the same 320-billion-parameter Mixture-of-Experts weights (18 billion active) rather than a separate model. Z.ai runs it on faster inference infrastructure, reporting speeds up to 200 tokens per second. Because the weights are unchanged, it inherits GLM-5.3-Flash's benchmark results, including 63.4 on DeepSWE v1.1 and 48.8 on AutomationBench, both ahead of GLM-5.2. It keeps the same 1,000,000-token context window, 128,000-token max output, and function calling support. Pricing is $0.37 per million input tokens and $1.25 per million output tokens, about 2.5 times GLM-5.3-Flash's rate. It fits agent loops and coding assistants that call the model repeatedly and need lower per-call latency, where the added throughput offsets the higher per-token price.
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.
GLM 4 32B 0414 128K is free to try with a Puter account. Every account includes a free AI allowance, and you can chat with it in the playground on this page. You can upgrade your account anytime for a larger allowance.
GLM 4 32B 0414 128K is free to integrate using the Puter.js AI API. With the User-Pays Model, you can add AI to your app for $0, since users cover their own AI usage through their Puter account.
| 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 96K 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.
Add GLM 4 32B 0414 128K to your app for free
Developers can integrate GLM 4 32B 0414 128K for free using the Puter.js AI API.
With the User-Pays Model, each user covers their own AI usage instead of the developer.