Z.AI: GLM 4.7 FlashX
z-ai/glm-4.7-flashx
Try GLM 4.7 FlashX 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-4.7-flashx"
}).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-4.7-flashx"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
Model Card
GLM-4.7-FlashX is the fastest inference tier in Z.ai's GLM-4.7 generation, offering the lowest latency in the lineup. It shares the 200K-token context window and core improvements of the 4.7 generation — stronger coding, tool usage, multi-step reasoning, and natural conversational tone — while trading peak capability for maximum speed.
The full GLM-4.7 model scores 73.8% on SWE-bench Verified, 84.9% on LiveCodeBench, and 95.7% on AIME 2025. FlashX inherits the same foundational training but is the right pick when response time matters more than squeezing out every point of accuracy.
Targets high-throughput coding assistance, real-time agent orchestration, and latency-sensitive chat where the standard GLM-4.7 or GLM-4.7-Flash would be too slow for the concurrency requirements.
Context Window 203K
tokens
Max Output 131K
tokens
Input Cost $0.07
per million tokens
Output Cost $0.4
per million tokens
Input text
modalities
Tool Use Yes
Release Date Jan 19, 2026
Try GLM 4.7 FlashX for free
Try GLM 4.7 FlashX 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 5.3 FlashX Legal
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 4.7 FlashX 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 4.7 FlashX 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 4.7 FlashX 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 | $0.07 |
| Output | $0.4 |
GLM 4.7 FlashX was created by Z.AI and released on Jan 19, 2026.
GLM 4.7 FlashX supports a context window of 203K tokens. For reference, that is roughly equivalent to 406 pages of text.
GLM 4.7 FlashX can generate up to 131K tokens in a single response.
GLM 4.7 FlashX accepts the following input types: text. It produces: text.
Yes, GLM 4.7 FlashX 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 4.7 FlashX 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 4.7 FlashX to your app for free
Developers can integrate GLM 4.7 FlashX for free using the Puter.js AI API.
With the User-Pays Model, each user covers their own AI usage instead of the developer.