Z.AI: GLM 4.7 Flash
z-ai/glm-4.7-flash
Access GLM 4.7 Flash 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-4.7-flash"
}).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-flash"
}).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-4.7-flash",
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-4.7-flash",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
GLM 4.7 Flash is designed for speed and efficiency while maintaining strong performance. It features a 200K token context window, making it suitable for processing long documents and generating extended responses.
Context Window 200K
tokens
Max Output 128K
tokens
Input Cost $0
per million tokens
Output Cost $0
per million tokens
Input text
modalities
Tool Use Yes
Release Date Jan 19, 2026
Output Speed 86
tokens / sec
Latency 0.95s
time to first token
Model Playground
Try GLM 4.7 Flash instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How GLM 4.7 Flash performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 58.1% |
| Humanity's Last Exam Cross-domain reasoning | 7.1% |
| SciCode Scientific programming | 33.7% |
| IFBench Instruction following | 60.8% |
| LCR Long-context reasoning | 35.0% |
| Terminal-Bench Hard Agentic terminal tasks | 22.0% |
| τ²-Bench Tool use / agents | 98.8% |
Scores sourced from Artificial Analysis.
Find other Z.AI models →
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 4.7 Flash 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 4.7 Flash 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 |
| Output | $0 |
GLM 4.7 Flash was created by Z.AI and released on Jan 19, 2026.
GLM 4.7 Flash supports a context window of 200K tokens. For reference, that is roughly equivalent to 400 pages of text.
GLM 4.7 Flash can generate up to 128K tokens in a single response.
GLM 4.7 Flash accepts the following input types: text. It produces: text.
Yes, GLM 4.7 Flash supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
GLM 4.7 Flash scores 30.1 on the Artificial Analysis Intelligence Index, outperforming 68% of tracked models. On coding, it scores 25.9 (outperforms 63% of models).
Yes — the GLM 4.7 Flash 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 4.7 Flash to your app without worrying about API keys or setup.
Read the Docs View Tutorials