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.
GLM-4.7 is the latest flagship AI model from Zhipu AI, representing a significant leap in open-weight intelligence. This massive 358-billion parameter Mixture-of-Experts (MoE) model is specifically engineered for advanced reasoning, coding automation, and complex agentic workflows. It introduces a dedicated Deep Thinking mode that enables multi-step planning and error recovery, allowing the model to solve high-stakes software engineering tasks with unprecedented reliability.
The model distinguishes itself through exceptional technical performance, achieving a state-of-the-art 73.8% score on SWE-bench Verified and an 84.9 on LiveCodeBench v6. With its 200,000-token context window and massive 131,072-token output capacity, GLM-4.7 is optimized for generating entire applications and conducting deep research across vast datasets.
As an open-weight release under the MIT license, it offers a powerful and flexible alternative to proprietary APIs, supporting both cloud-based integration and local hosting. Its multimodal capabilities extend to advanced UI design and document analysis, making it a versatile powerhouse for modern AI-driven development.
Use Cases for GLM-4.7
Discover the different ways you can use GLM-4.7 to achieve great results.
Agentic Software Engineering
Resolving complex GitHub issues and implementing full-stack features autonomously across entire repositories.
High-Fidelity Vibe Coding
Rapidly generating modern, production-ready web interfaces using Tailwind CSS and interactive Framer Motion components.
Multilingual Technical Support
Providing advanced coding assistance and logical problem solving across 10+ international programming environments.
Deep Academic Research
Analyzing massive document sets to extract multi-hop, verifiable information using the BrowseComp search framework.
Automated Presentation Design
Creating structured, visually balanced slides with accurate layouts and typography from single-sentence prompts.
Terminal-Based Automation
Executing complex system administration and DevOps tasks directly within a terminal sandbox with 41% benchmark accuracy.
Strengths
Limitations
API Quick Start
zai/glm-4.7
import { ZhipuAI } from "zhipuai";
const client = new ZhipuAI({ apiKey: "YOUR_API_KEY" });
async function main() {
const response = await client.chat.completions.create({
model: "glm-4.7",
messages: [{ role: "user", content: "Build a real-time collaborative whiteboard using Next.js." }],
stream: true,
extra_body: { "thinking": true }
});
for await (const chunk of response) {
process.stdout.write(chunk.choices[0].delta.content || "");
}
}
main();Install the SDK and start making API calls in minutes.
What People Are Saying About GLM-4.7
See what the community thinks about GLM-4.7
"GLM 4.7 CRUSHES OPEN SOURCE RECORDS! ... hit 42.8% on Humanity's Last Exam"
"GLM-4.7... scores 73.8% on SWE-Bench at $0.6/M tokens... The AI race is becoming truly multipolar."
"GLM 4.7 brings clear gains... in multilingual agentic coding and terminal-based tasks"
"This model is crushing it on many 2025 coding benchmarks"
"GLM 4.7 wins for speed and stability, while Minimax M2.1 dominates in multi-agent coding"
"Zhipu is really showing what open weights can do against the big labs in the US."
Videos About GLM-4.7
Watch tutorials, reviews, and discussions about GLM-4.7
“GLM 4.7 is a model that delivers major improvements in code quality, complex reasoning, and tool usage”
“Scored a 73.8 percentage on SWE-bench verified, which is absolutely incredible for an open-source model”
“It even surpasses Claude Sonnet 4.5 and GPT 5.1 in tool use benchmarks”
“The mixture of experts approach here is very refined, leading to higher efficiency despite the size”
“It is essentially the first open-weight model to provide a viable alternative to Claude 3.5 for heavy coding”
“It is the best open model yet by a long shot”
“It produces cleaner, more modern web pages and generates better looking slides”
“Reasons, but the thinking traces are not available in the coding plan API”
“Vibe coding results are near-perfect, even with complex Tailwind animations”
“The 200k context handles long repos with very little needle loss compared to previous GLM versions”
“Important upgrade is thinking before acting, which helps the model handle complex tasks reliably”
“Highlight vibe coding, where GLM 4.7 improves UI quality”
“API pricing to be around the same $3, making it a very cost-effective option”
“The multimodal performance allows it to convert Figma designs to code with high accuracy”
“Local deployment is possible if you have a massive workstation, but the API is remarkably fast”
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Pro Tips for GLM-4.7
Expert tips to help you get the most out of GLM-4.7 and achieve better results.
Enable Deep Thinking
For complex logical tasks, explicitly trigger the thinking mode via API parameters to enable multi-step planning.
Leverage Preserved Thinking
Maintain long conversation histories to utilize the model's ability to retain reasoning traces across multiple turns.
Local Quantization
Use Unsloth-optimized 2-bit or 4-bit GGUF versions to run this high-parameter model on consumer-grade hardware.
Date Injection
Manually include the current date in the system prompt to avoid temporal hallucinations and improve scheduling accuracy.
Testimonials
What Our Users Say
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Frequently Asked Questions About GLM-4.7
Find answers to common questions about GLM-4.7