GLM-4.7
GLM-4.7 by Zhipu AI is a flagship 358B MoE model featuring a 200K context window, elite 73.8% SWE-bench performance, and native Deep Thinking for agentic...
zhipuGLMDecember 22, 2025About GLM-4.7
Learn about GLM-4.7's capabilities, features, and how it can help you achieve better results.
Model Overview
GLM-4.7 is a flagship large language model developed by Zhipu AI. It utilizes a Mixture-of-Experts (MoE) architecture with 358 billion total parameters. The model is specifically designed to handle complex agentic tasks and long-context reasoning through its unique Preserved Thinking and Interleaved Thinking capabilities. These features allow the model to maintain stable logic and intermediate reasoning states across multi-turn sessions, addressing the context decay common in autonomous workflows.
Performance and Architecture
The model offers an expansive 200,000-token context window combined with a massive 131,072-token output capacity. This makes it suitable for generating entire applications or analyzing extensive documentation in a single pass. Released under the MIT license as an open-weight model, it provides high-performance coding and reasoning at a fraction of the cost of proprietary alternatives.
Integration and Use
It is fully compatible with the OpenAI API format, simplifying integration into existing software ecosystems. Developers use it for high-stakes software engineering tasks, where it achieves a 73.8% score on SWE-bench Verified. Its ability to process and analyze high volumes of technical documentation between English and Chinese with native-level linguistic nuance makes it a versatile tool for international development teams.
Use Cases
Discover the different ways you can use GLM-4.7 to achieve great results.
Autonomous Software Engineering
Utilizing the 73.8% SWE-bench capability to autonomously debug, refactor, and implement new features across complex repositories.
High-Capacity Document Synthesis
Leveraging the 131k output limit to generate comprehensive technical manuals or entire book chapters from large datasets.
Long-Horizon Agentic Workflows
Deploying agents that use Preserved Thinking to maintain consistency and logic over hundreds of sequential tasks without losing context.
Bilingual Business Intelligence
Processing and analyzing high-volumes of technical documentation between English and Chinese with native-level linguistic nuance.
Automated UI/UX Code Generation
Generating complete React or Next.js front-end architectures with advanced animations and production-ready styling in a single shot.
Competition-Level Mathematical Solving
Solving complex Olympiad-level math problems and symbolic logic puzzles using the dedicated reasoning-heavy thinking mode.
Strengths
Limitations
API Quick Start
zai/glm-4.7
import OpenAI from 'openai';
const client = new OpenAI({
apiKey: 'YOUR_ZAI_API_KEY',
baseURL: 'https://api.z.ai/api/paas/v4/',
});
async function main() {
const response = await client.chat.completions.create({
model: 'glm-4.7',
messages: [{ role: 'user', content: 'Design a scalable React architecture.' }],
thinking: { type: 'enabled' }
});
console.log(response.choices[0].message.content);
}
main();Install the SDK and start making API calls in minutes.
Community Feedback
See what the community thinks about GLM-4.7
“GLM-4.7 handles large codebases reliably with its 128k context. It's been surprisingly useful for subagent tasks to save on primary API costs.”
“Zhipu AI's GLM-4.7 matches proprietary frontier models like GPT-5.1 High in coding. The Preserved Thinking feature is a huge win for autonomous agents.”
“GLM-4.7 continues to be the most intelligent open weights model in the Intelligence Index v4.0, placing ahead of DeepSeek V3.2.”
“Chinese models are closing the gap fast in coding utility. This 73% SWE-bench score is no joke for an open weight release.”
“The reasoning speed is actually quite decent for a model of this size. It handles the complex logic much better than previous iterations.”
“GLM-4.7 lands #6 on the AI Index, surpassing Kimi K2. Discover why this $2 model is replacing GPT-5.2 in coding workflows.”
Related Videos
Watch tutorials, reviews, and discussions about GLM-4.7
“The context length here is 200k and the maximum output tokens is 128k which is quite beefy actually.”
“All right, that is really quite impressive. None of them put in a special feature with that level of complexity.”
“The reasoning speed is actually quite decent for a model of this size.”
“It handles the complex logic much better than previous iterations.”
“This model is a significant step up in terms of logical consistency.”
“The GLM model actually implemented a better architecture by placing all the mock data in one file.”
“This one is definitely a huge leap. Those benchmarks are justified by the testing I've done.”
“It understood the context of the entire project without me needing to remind it.”
“The coding capability is arguably on par with the best models out there.”
“You are getting high-end reasoning at a fraction of the cost.”
“It scored a 73.8 percentage on Swaybench verified, which is absolutely incredible for an open-source model.”
“You can actually see that it functions and actually works. Whereas the Gemini 3 Pro generation doesn't work at all.”
“The speed of generation for this level of intelligence is remarkable.”
“It is clearly designed for developers who need reliable code output.”
“Zhipu AI has really outdone themselves with the MoE architecture tuning here.”
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Pro Tips
Expert tips to help you get the most out of GLM-4.7 and achieve better results.
Enable Thinking Mode for Logic
Set the thinking parameter to enabled for coding or math tasks to utilize the model's internal reasoning traces and improve accuracy.
Use OpenAI-Compatible SDKs
Integrate GLM-4.7 into existing workflows by using the OpenAI SDK and changing the base URL to the Z.ai endpoint.
Maximize the 131K Output
When generating long-form content, provide a detailed outline first to help the model maintain structural coherence over the massive token limit.
Optimize System Prompts for Agents
Define the Preserved Thinking requirements in the system message to ensure the model reuses reasoning states across multi-turn sessions.
Testimonials
What Our Users Say
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Frequently Asked Questions
Find answers to common questions about GLM-4.7