The New Standard in Open-Source AI: Introducing GLM-4.7
The landscape of generative AI is shifting. While the industry anticipates the next moves from established giants, Z.AI has officially released its latest flagship model: GLM-4.7.
Positioned as a direct competitor to top-tier proprietary models like GPT-5.1, GLM-4.7 sets a new benchmark for open-source performance. If you have been relying on Kimi or DeepSeek, it is time to evaluate the next generation of LLMs.
Engineering Excellence: What’s New in GLM-4.7?
Z.AI has focused on two critical pillars to transform GLM-4.7 into a production-ready powerhouse:
1. Advanced Programming & UI Aesthetics
GLM-4.7 features a significant leap in coding proficiency. Beyond mere syntax, the model demonstrates a sophisticated understanding of front-end aesthetics and modern design principles. Developers can expect cleaner code generation, better architectural suggestions, and more visually cohesive UI outputs.
2. Stable Multi-Step Reasoning
One of the primary challenges for AI agents is maintaining logic across complex, multi-stage tasks. GLM-4.7 introduces enhanced reasoning and execution stability, allowing it to handle intricate agent workflows without losing context or deviating from the objective. This results in a more natural conversational flow and reliable task automation.
Performance Metrics & Specialized Gnoppix API Pricing
For enterprises and developers scaling AI applications, efficiency is paramount. Through the Gnoppix API, GLM-4.7 offers industry-leading throughput and a massive context window at a highly competitive price point.
Key Specifications:
- Context Window: 200k tokens (Ideal for massive datasets and long-form codebases)
- Throughput: 33.72 tps (Optimized for real-time responsiveness)
API Pricing (USD):
- Input: $0.40 per 1M tokens
- Output: $1.75 per 1M tokens
Test prompts :
. Agent / Tool Use (All Tools) Prompts
GLM-4 is highlighted for its ability to autonomously decide when to search the web, interpret code, or use other tools.
The "Trip Planning" Prompt:
"帮我规划一个去北京的三天行程，并查看这三天的天气预报。"
(Plan a 3-day trip to Beijing for me and check the weather forecast for these three days.)
Model Action: The model identifies the need for current data, triggers a Web Search tool to get the weather and attraction info, and then synthesizes an itinerary.
The "Data Analysis" Prompt:
"分析一下这份2023年销售数据的表格，并画出柱状图。"
(Analyze this 2023 sales data table and draw a bar chart.)
Model Action: The model utilizes the Code Interpreter to write Python code (pandas/matplotlib), processes the data (either provided or uploaded), and executes the code to generate the chart image.
- Long Context Prompts
Demonstrating the 128k / 1M context window capabilities.
The "Long Document QA" Prompt:
"请阅读上传的小说《红楼梦》全文，并总结林黛玉进贾府时见到了哪些人物，以及她的心理活动。"
(Please read the full text of the novel "Dream of the Red Chamber" uploaded here, and summarize whom Lin Daiyu saw when she entered the Jia Mansion, and her psychological activities.)
Demonstration: The model accurately retrieves specific details from the beginning, middle, or end of a massive text block without "hallucinating" or losing context.
- Coding Prompts
Demonstrating GLM-4’s improvement in code generation (HumanEval scores).
The "Game Development" Prompt:
"请用Python写一个贪吃蛇游戏，要求有图形界面。"
(Please write a Snake game in Python with a graphical user interface.)
Model Output: Provides a complete, runnable Python script using a library like pygame or tkinter.
The "Algorithm" Prompt:
"如何实现一个快速排序算法？请给出Python代码示例。"
(How to implement a Quick Sort algorithm? Please provide a Python code example.)
- Roleplay & Creative Writing Prompts
Demonstrating instruction following and stylistic adaptation.
The "Persona" Prompt:
"你现在是一个资深的健身教练，请为一名想要减脂的上班族制定一个一周的训练计划。"
(You are now a senior fitness coach. Please create a one-week workout plan for an office worker who wants to lose fat.)
The "Creative Writing" Prompt:
"以‘雨后的街道’为题，写一首现代诗。"
(Write a modern poem titled 'The Street After Rain'.)
- Logic & Reasoning Prompts
Comparing performance against GPT-4.
The "Math Word Problem" Prompt:
"一个水池有两个进水管和一个出水管。单开A管6小时注满，单开B管8小时注满，单开C管5小时放完。三管同开，几小时注满水池？"
(A pool has two inlet pipes and one outlet pipe. Pipe A fills it in 6 hours, Pipe B in 8 hours, and Pipe C drains it in 5 hours. If all three are open, how long does it take to fill the pool?)
Experience the Future of Open-Source AI
GLM-4.7 proves that high-level intelligence and open accessibility are no longer mutually exclusive. Whether you are building complex autonomous agents or seeking a more refined conversational AI, GLM-4.7 delivers the precision and speed required for modern innovation.
- Try it now: chat.z.ai
- Read the full technical breakdown: Z.AI Blog - GLM-4.7