Z.AI: GLM 4 32B 0414 128K
z-ai/glm-4-32b-0414-128k
Access GLM 4 32B 0414 128K from Z.AI using the 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>
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
Model Playground
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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, 131,072-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.
ChatGLM 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.
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 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.