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const { OpenAI } = require('openai');
const api = new OpenAI({
baseURL: 'https://api.ai.cc/v1',
apiKey: '',
});
const main = async () => {
const result = await api.chat.completions.create({
model: 'zhipu/glm-4.6',
messages: [
{
role: 'system',
content: 'You are an AI assistant who knows everything.',
},
{
role: 'user',
content: 'Tell me, why is the sky blue?'
}
],
});
const message = result.choices[0].message.content;
console.log(`Assistant: ${message}`);
};
main();
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.ai.cc/v1",
api_key="",
)
response = client.chat.completions.create(
model="zhipu/glm-4.6",
messages=[
{
"role": "system",
"content": "You are an AI assistant who knows everything.",
},
{
"role": "user",
"content": "Tell me, why is the sky blue?"
},
],
)
message = response.choices[0].message.content
print(f"Assistant: {message}")
-
AI Playground
Test all API models in the sandbox environment before you integrate.
We provide more than 300 models to integrate into your app.
Product Detail
✨ GLM-4.6 API Overview
GLM-4.6 is an advanced large language model developed by Zhipu AI (now Z.ai). It features a state-of-the-art 355 billion parameter Mixture of Experts (MoE) architecture. Optimized for a broad range of tasks including complex reasoning, coding, writing, and multi-turn dialogue, GLM-4.6 offers an extended context window of 200,000 tokens. This model demonstrates industry-leading performance, particularly in programming and agentic tasks, making it a top choice for developers and enterprises seeking efficiency and versatility.
⚙️ Technical Specifications
- Model Architecture: 355B parameter Mixture of Experts (MoE)
- Input Modality: Text
- Output Modality: Text
- Context Window Size: 200,000 tokens (expanded from 128,000 in GLM-4.5)
- Maximum Output Tokens: 128,000 tokens
- Efficiency: Approximately 30% more efficient token consumption than previous versions
- Supported Programming Languages: Python, JavaScript, Java (for coding tasks)
📈 Performance Benchmarks
GLM-4.6 has been rigorously evaluated across authoritative benchmarks, demonstrating competitive or superior results against leading models:
- Real-world Coding Tests: Outperforms similar domestic models in 74 coding scenarios, showing better code correctness and performance.
- Comparative Efficiency: Consumes approximately 30% fewer tokens for equivalent output, reducing costs and resource needs.
- Benchmark Results: Comparable to Claude Sonnet 4 and 4.6 on multi-domain NLP benchmarks like AIME, GPQA, LCB v6, and SWE-Bench Verified.
- Reasoning and Agent Tasks: Strong performance in decision-making and tool-assisted tasks, often matching or exceeding competitors in benchmark tests.
- Contextual Understanding: Expanded context allows superior performance on tasks requiring deep document analysis and complex instructions.
Image: GLM-4.6 Performance Benchmarks
💡 Key Features and Capabilities
- Extended Context Handling: With a massive 200K token window, GLM-4.6 can perform detailed long-form text comprehension, multi-step problem solving, and maintain coherent, prolonged dialogues.
- Superior Coding Performance: Outperforms GLM-4.5 and many domestic competitors in 74 practical coding tests within the Claude Code environment. Excels in front-end development, code organization, and autonomous planning.
- Advanced Reasoning and Decision Making: Enhanced tool usage capabilities during inference enable better autonomous agent frameworks and search-based task execution.
- Natural Language Generation: Produces text with improved alignment to human stylistic preferences, excelling in role-playing, content creation (novels, scripts, ads), and multi-turn conversations.
Image: GLM-4.6 Key Features and Capabilities
💰 GLM-4.6 API Pricing
- Input: $0.63
- Output: $2.31
- Cached: $0.1155
🚀 Use Cases for GLM-4.6
- Long-context document analysis and summarization
- Complex multi-step reasoning and problem solving
- Real-world programming and code generation in multiple languages
- Natural language content creation including creative writing and scripts
- Chatbots with sustained, coherent multi-turn conversations
- Agentic systems with tool use and autonomous decision making
- High-volume industrial-scale applications requiring token-efficient models
💻 Code Sample
<!-- Code Sample Placeholder -->
This section would typically feature an interactive code snippet for API integration, e.g.:
import openai
from openai import OpenAI
# For Zhipu AI (Z.ai) GLM-4.6
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://api.z.ai/v1", # Example base URL
)
completion = client.chat.completions.create(
model="zhipu/glm-4.6",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Explain large language models in simple terms."}
]
)
print(completion.choices[0].message.content)⚖️ Comparison with Other Models
Vs. GLM-4.5: GLM-4.6 offers noticeable improvements in code generation accuracy and maintains a consistent edge in handling ultra-long context inputs, while retaining strong agentic task performance close to GLM-4.5.
Vs. OpenAI GPT-4.5: GLM-4.6 narrows the gap in reasoning and multi-step task accuracy, leveraging its much larger context window; however, GPT-4.5 still leads in raw task precision on some standardized benchmarks.
Vs. Claude 4 Sonnet: While Claude 4 Sonnet excels in coding and multi-agent efficiency, GLM-4.6 matches or surpasses it in agentic reasoning and long-document comprehension, making it stronger for extended-context applications.
Vs. Gemini 2.5 Pro: GLM-4.6 balances advanced reasoning and coding capabilities with enhanced long-form document understanding, whereas Gemini 2.5 Pro is more focused on optimizing individual coding and reasoning benchmarks.
🔗 API Integration
Accessible via AI/ML API. For detailed documentation, please refer to: GLM-4.6 API Documentation.
❓ Frequently Asked Questions (FAQ)
Q1: What is GLM-4.6 and what significant improvements does it offer?
GLM-4.6 is the latest iteration in Zhipu AI's General Language Model series, featuring substantial enhancements in reasoning capabilities, multilingual performance, and specialized domain knowledge. Key improvements include advanced mathematical and logical reasoning, better code generation and understanding, enhanced multilingual support with superior Chinese language capabilities, and improved efficiency in processing complex, multi-step tasks.
Q2: How does GLM-4.6 compare to other leading language models like GPT-4 and Claude?
GLM-4.6 demonstrates competitive performance with top-tier models, particularly excelling in Chinese language tasks, mathematical reasoning, and coding applications. While it may have different strengths than GPT-4 in creative writing or Claude in safety alignment, it often matches or exceeds comparable models in technical domains and Asian language understanding. Its efficient architecture also provides cost advantages for many enterprise applications.
Q3: What are the key technical innovations in GLM-4.6's architecture?
GLM-4.6 introduces several architectural innovations: enhanced attention mechanisms for better long-context handling, improved training techniques for mathematical and logical reasoning, optimized tokenization for multilingual efficiency, advanced fine-tuning approaches for specialized domains, and better parameter efficiency that delivers strong performance without requiring extreme scale. These innovations contribute to its balanced performance across diverse tasks.
Q4: What practical applications is GLM-4.6 particularly well-suited for?
GLM-4.6 excels in: enterprise applications in Asian markets requiring strong Chinese language support, technical documentation and code generation, mathematical and scientific analysis, educational tools for STEM subjects, business intelligence and data analysis, and multilingual customer service automation. Its balanced performance makes it versatile for both creative and analytical tasks across various industries.
Q5: How does GLM-4.6 address multilingual and cross-cultural applications?
GLM-4.6 features sophisticated multilingual capabilities with particular strength in Chinese and other Asian languages, including understanding of cultural context, idioms, and regional variations. The model demonstrates strong cross-lingual transfer learning, enabling effective performance even when training data is unevenly distributed across languages. This makes it especially valuable for global businesses operating in Asian markets or requiring robust multilingual support.
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