Z.AI: GLM 5.3 Prime
z-ai/glm-5.3-prime
Try GLM 5.3 Prime 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-5.3-prime"
}).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-5.3-prime"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
Model Card
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.
Context Window 1M
tokens
Max Output 131K
tokens
Input Cost $2.8
per million tokens
Output Cost $8.8
per million tokens
Input text
modalities
Tool Use Yes
Release Date Sep 23, 2026
Try GLM 5.3 Prime for free
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This playground uses the Puter.js AI API — no API keys or setup required.
More AI Models From Z.AI
GLM 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.
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 5.3 Prime 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 5.3 Prime 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 5.3 Prime 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 | $2.8 |
| Output | $8.8 |
GLM 5.3 Prime was created by Z.AI and released on Sep 23, 2026.
GLM 5.3 Prime supports a context window of 1M tokens. For reference, that is roughly equivalent to 2,000 pages of text.
GLM 5.3 Prime can generate up to 131K tokens in a single response.
GLM 5.3 Prime accepts the following input types: text. It produces: text.
Yes, GLM 5.3 Prime 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 5.3 Prime 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 5.3 Prime to your app for free
Developers can integrate GLM 5.3 Prime for free using the Puter.js AI API.
With the User-Pays Model, each user covers their own AI usage instead of the developer.