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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-4.5',
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-4.5",
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
Zhipu AI's GLM-4.5 stands out as a highly versatile text-to-text large language model, expertly engineered for a wide array of natural language processing tasks. Its impressive 128,000-token context window empowers it to comprehend and generate exceptionally long-form text with remarkable coherence and deep contextual understanding, making it ideal for complex applications.
Technical Specifications
Performance Benchmarks
- 🚀 Context Window: 128,000 tokens – enabling deep comprehension and generation of extensive documents.
- ✅ Optimization: Tailored for a broad spectrum of text-to-text applications, including intricate document analysis, concise summarization, and sophisticated content generation.
Performance Metrics & Ranking
GLM-4.5 aims to integrate diverse capabilities, bridging performance gaps. Across 12 key benchmarks (3 agentic tasks, 7 reasoning tasks, 2 coding tasks), GLM-4.5 secures an impressive overall third-place ranking. Its lighter variant, GLM-4.5 Air, achieves sixth place, demonstrating strong competitive results against leading models from OpenAI, Anthropic, Google DeepMind, xAI, Alibaba, Moonshot, and DeepSeek.
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Key Capabilities
- ✍️ Advanced Text Generation: Produces fluent, contextually relevant, and highly accurate long-form text outputs, from articles to reports.
- 🧠 Comprehensive Understanding: Possesses a robust grasp of semantics, enabling nuanced text manipulations like summarization, sophisticated paraphrasing, and engaging dialogue.
API Pricing
- 📥 Input: $0.63
- 📤 Output: $2.31
Optimal Use Cases
- 📖 Long-Form Content Creation: Ideal for generating books, detailed reports, and in-depth articles that demand deep consistency across numerous tokens.
- 🔬 Complex Document Analysis: Highly effective for processing and understanding legal texts, scientific papers, and intricate business documents.
- 💬 Conversational AI: Powers advanced chatbots capable of maintaining extended context and generating highly relevant, multi-turn replies.
- 💡 Text Summarization & Transformation: Excellent for generating precise abstracts, sophisticated paraphrased content, and synthesizing multi-turn dialogues.
Integration & Code Samples
Developers can seamlessly integrate GLM-4.5 into their applications. While specific code snippets are typically provided in dedicated API documentation, GLM-4.5 supports standard API interactions, similar to other leading models, enabling straightforward implementation for various use cases.
GLM-4.5 vs. Leading Models: A Comparison
GLM-4.5 demonstrates strong competitive performance when benchmarked against other industry giants:
- 🆚 Vs. Claude 4 Sonnet: GLM-4.5 exhibits comparable performance in agentic coding and reasoning tasks. While Claude Sonnet 4 excels in coding success and state-of-the-art reasoning, GLM-4.5 offers significant potential for optimization.
- 🆚 Vs. OpenAI GPT-4.5: GLM-4.5 maintains overall competitiveness with top models like GPT-4.5 in reasoning and agent benchmarks. Although GPT-4.5 often leads in raw task accuracy on specific professional benchmarks (e.g., MMLU, AIME), GLM-4.5 holds its own.
- 🆚 Vs. Qwen3-Coder & Kimi K2: GLM-4.5 shows superior coding and agentic capabilities, achieving an 80.8% success rate against Qwen3-Coder and winning 53.9% of tasks against Kimi K2, positioning it strongly for complex programming scenarios.
- 🆚 Vs. Gemini 2.5 Pro: GLM-4.5 performs well on reasoning and coding benchmarks. While Gemini 2.5 Pro shows strength in certain areas, GLM-4.5 effectively balances its large context window with robust agentic tools.
Limitations
⚠️ Computational Resources: The full GLM-4.5 model demands significant computational resources and GPU memory. This can be a limiting factor for organizations with restricted infrastructure. The more resource-friendly GLM-4.5 Air variant offers a solution, though with slightly reduced capabilities due to fewer active parameters.
Frequently Asked Questions (FAQ)
Q: What makes GLM-4.5's architecture unique?
A: GLM-4.5 features groundbreaking architectural innovations including a hybrid attention mechanism for efficiency and context retention, enhanced multi-scale reasoning pathways, and a novel knowledge distillation framework. It also uses improved sparse activation patterns and advanced positional encoding for superior handling of long-form content.
Q: How does GLM-4.5 handle multimodal understanding?
A: The model employs a revolutionary cross-modal pretraining approach called Unified Semantic Embedding. This method learns text, code, and visual representations in a shared latent space, incorporating progressive modality integration and sophisticated alignment techniques across massive-scale cross-modal corpora.
Q: What are GLM-4.5's specialized enterprise capabilities?
A: GLM-4.5 offers enterprise-grade capabilities through domain-adaptive reasoning engines. These include specialized modules for financial analysis, legal document processing, medical terminology comprehension, and technical documentation synthesis, along with business logic understanding to navigate workflows contextually.
Q: Is GLM-4.5 effective for low-resource languages?
A: Yes, it excels in low-resource language scenarios by leveraging advanced transfer learning, meta-learning frameworks for rapid adaptation, cross-lingual embedding spaces, and sophisticated data augmentation. A language-agnostic representation layer further ensures knowledge transfer while preserving cultural nuances.
Q: What security and alignment features does GLM-4.5 offer?
A: GLM-4.5 integrates a comprehensive security framework with real-time content moderation, advanced prompt injection detection, differential privacy guarantees, and verifiable output watermarking. Its alignment system uses multi-layered constitutional AI principles with dynamic reward modeling for ethical adherence across diverse values.
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