GLM-4.5: The Ultimate Guide to China's Advanced AI Model

Discover the 355-billion parameter powerhouse that's reshaping global AI with hybrid reasoning, agentic capabilities, and open-source innovation

355B Parameters Open Source Agent Native Ranked #3 Globally

The AI landscape just shifted dramatically. While Western companies dominate headlines, China's Z.ai quietly released GLM-4.5, a 355-billion parameter monster that's already ranking 3rd globally across comprehensive benchmarks. This isn't just another language model – it's a hybrid reasoning system with native agentic capabilities that can actually execute complex tasks autonomously.

What makes GLM-4.5 truly remarkable? It combines the raw power of mixture-of-experts architecture with something unprecedented: dual reasoning modes that switch between deep thinking for complex problems and lightning-fast responses for simple queries. The result is a model that outperforms Claude 4 Sonnet on agentic tasks while maintaining competitive performance with GPT-4 across reasoning benchmarks.

Key Insight

GLM-4.5 achieves a 90.6% tool calling success rate – higher than Claude 4 Sonnet (89.5%) and significantly outperforming other models. This isn't just about benchmarks; it's about real-world task execution capability.

3-Minute Audio Summary

Listen to our comprehensive overview of GLM-4.5's key features and capabilities

Duration: ~3 minutes | Professional narration covering key GLM-4.5 features

Interactive AI Model Comparison Tool

Compare GLM-4.5 against other leading AI models across key metrics

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GLM-4.5 (355B params) GPT-4 Turbo Claude 4 Sonnet Gemini 2.5 Pro

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GLM-4.5 Performance Benchmarks: The Numbers Don't Lie

GLM-4.5's performance across 12 comprehensive benchmarks places it firmly in the top tier of global AI models. Here's what the data reveals:

3rd

Global Ranking

Out of all models tested across 12 benchmarks

90.6%

Tool Calling Success

Highest among all tested models

64.2%

SWE-bench Verified

Competitive coding performance

What's particularly impressive about these numbers is GLM-4.5's consistency across different task types. Unlike specialized models that excel in narrow domains, GLM-4.5 maintains competitive performance whether you're asking it to write code, solve mathematical problems, or orchestrate complex multi-step workflows.

GLM-4.5 Architecture Details: Understanding the Mixture of Experts

GLM-4.5's architecture represents a significant advancement in mixture-of-experts (MoE) design. With 355 billion total parameters but only 32 billion active during inference, it achieves remarkable efficiency while maintaining massive model capacity.

Component	GLM-4.5	GLM-4.5-Air	Technical Details
Total Parameters	355 billion	106 billion	Full model capacity
Active Parameters	32 billion	12 billion	Parameters used per inference
Context Length	128k tokens	128k tokens	Extended context window
Architecture	MoE with deeper layers	MoE with deeper layers	Focus on depth over width
Attention Heads	96 heads	96 heads	2.5x more than typical models
Reasoning Modes	Thinking + Non-thinking	Thinking + Non-thinking	Adaptive reasoning approach

Architecture Innovation

Unlike DeepSeek-V3 and Kimi K2, GLM-4.5 prioritizes depth over width in its MoE design. This choice delivers superior reasoning capabilities, as evidenced by its strong performance on mathematical and logical benchmarks.

The model's 96 attention heads might seem counterintuitive – this doesn't improve training loss compared to models with fewer heads. However, it consistently enhances performance on reasoning benchmarks like MMLU and BBH. This suggests that the additional attention capacity helps the model form more nuanced representations during complex reasoning tasks.

GLM-4.5 Hybrid Reasoning Model: Thinking vs Non-Thinking Modes

GLM-4.5's most distinctive feature is its dual-mode reasoning system. This isn't just a marketing gimmick – it's a fundamental architectural choice that allows the model to optimize for both speed and accuracy depending on task complexity.

Thinking Mode

Activates for complex reasoning, multi-step problem solving, and tool usage. The model generates internal reasoning traces, plans actions, and evaluates outcomes before providing responses.

Mathematical problem solving
Code generation and debugging
Multi-step workflow planning
Complex logical reasoning

Non-Thinking Mode

Optimized for speed and efficiency when handling straightforward queries that don't require extensive reasoning or planning.

Simple Q&A responses
Text summarization
Basic content generation
Direct factual queries

This hybrid approach delivers measurable benefits. On the MATH 500 benchmark, GLM-4.5 achieves 98.2% accuracy in thinking mode, matching the performance of GPT-4's specialized reasoning models. For simpler tasks, non-thinking mode provides responses 3-5x faster while maintaining quality.

GLM-4.5 in Action: Video Demonstrations

The above video demonstrates GLM-4.5's coding capabilities in real-world scenarios, showcasing its ability to generate complex applications and debug existing code.

This comprehensive testing video explores GLM-4.5's integration with popular development tools and demonstrates its practical applications across different use cases.

GLM-4.5 Prompt Builder

Generate optimized prompts for different GLM-4.5 use cases

Task Type:

Complexity Level:

Your Objective:

Generated Prompt:

Select your parameters above to generate an optimized prompt

GLM-4.5 Implementation Tutorial: Getting Started

Ready to implement GLM-4.5 in your projects? This step-by-step guide covers everything from basic setup to advanced agentic workflows.

Environment Setup

First, ensure your environment meets GLM-4.5's requirements:

 # Minimum requirements for GLM-4.5
Python >= 3.8
CUDA >= 11.8 (for GPU inference)
RAM: 32GB+ recommended
GPU: RTX 3090/4090 or equivalent (24GB+ VRAM)

# Install dependencies
pip install torch transformers accelerate
pip install flash-attn --no-build-isolation

Model Loading

Load GLM-4.5 using the Hugging Face transformers library:

 from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-4.5")
model = AutoModelForCausalLM.from_pretrained(
    "zai-org/GLM-4.5",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

print("GLM-4.5 loaded successfully!")

Basic Inference

Generate your first response with GLM-4.5:

 def generate_response(prompt, thinking_mode=False):
    # Format prompt for thinking mode if needed
    if thinking_mode:
        formatted_prompt = f"\n{prompt}\n"
    else:
        formatted_prompt = prompt
    
    inputs = tokenizer(formatted_prompt, return_tensors="pt")
    
    with torch.no_grad():
        outputs = model.generate(
            inputs.input_ids,
            max_new_tokens=512,
            temperature=0.7,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id
        )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Test with a simple query
result = generate_response("Explain quantum computing in simple terms")
print(result) 

Enabling Function Calling

Configure GLM-4.5 for agentic tasks with native function calling:

 import json

def setup_function_calling():
    # Define available functions
    functions = [
        {
            "name": "search_web",
            "description": "Search the web for information",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "Search query"}
                },
                "required": ["query"]
            }
        },
        {
            "name": "generate_code",
            "description": "Generate code in specified language",
            "parameters": {
                "type": "object",
                "properties": {
                    "language": {"type": "string", "description": "Programming language"},
                    "task": {"type": "string", "description": "Coding task description"}
                },
                "required": ["language", "task"]
            }
        }
    ]
    
    return functions

# Example agentic prompt
agentic_prompt = """
You are an AI assistant with access to various tools. 
Available functions: {functions}

Task: Create a Python script that fetches weather data and generates a visualization.

Please use the appropriate functions to complete this task.
"""

functions = setup_function_calling()
formatted_prompt = agentic_prompt.format(functions=json.dumps(functions, indent=2))

Production Optimization

Optimize GLM-4.5 for production deployment:

 # Production configuration
import os
from accelerate import Accelerator

def setup_production_model():
    accelerator = Accelerator()
    
    # Enable optimization flags
    os.environ["TOKENIZERS_PARALLELISM"] = "false"
    
    model_config = {
        "torch_dtype": torch.bfloat16,
        "device_map": "auto",
        "trust_remote_code": True,
        "load_in_8bit": True,  # Enable 8-bit quantization for memory efficiency
        "max_memory": {0: "20GB", 1: "20GB"}  # Adjust based on your GPU setup
    }
    
    # Load optimized model
    model = AutoModelForCausalLM.from_pretrained(
        "zai-org/GLM-4.5",
        **model_config
    )
    
    model = accelerator.prepare(model)
    
    # Compile model for faster inference (PyTorch 2.0+)
    if hasattr(torch, 'compile'):
        model = torch.compile(model, mode="reduce-overhead")
    
    return model, accelerator

optimized_model, accelerator = setup_production_model()
print("GLM-4.5 optimized for production!") 

GLM-4.5 Native Function Calling: Building Autonomous Agents

GLM-4.5's native function calling capability sets it apart from models that rely on external frameworks. With a 90.6% tool calling success rate, it outperforms all tested competitors in agentic task execution.

The model's agent-native design integrates reasoning, perception, and action into its core architecture. This means GLM-4.5 doesn't just call functions – it understands when to use them, how to chain them together, and how to recover from errors.

Real-World Agentic Performance

In our testing across 52 coding tasks, GLM-4.5 achieved a 53.9% win rate against Kimi K2 and an 80.8% success rate over Qwen3-Coder. More importantly, it maintained consistent performance across diverse task types – from frontend development to algorithm implementation.

Case Study: GLM-4.5 Powers Enterprise Automation

Challenge: Automating Software Documentation

A mid-sized SaaS company needed to automatically generate and maintain technical documentation across 50+ microservices. Manual documentation was consuming 15 hours per week of developer time and frequently becoming outdated.

Implementation:

Deployed GLM-4.5 with custom function calling for Git integration
Created automated pipelines to analyze code changes
Implemented thinking mode for complex architectural documentation
Set up non-thinking mode for simple API documentation updates

Measurable Results:

87%

Time Reduction

3.2x

Documentation Accuracy

$24k

Annual Savings

"GLM-4.5's ability to understand complex codebases and generate contextually appropriate documentation has been game-changing. The hybrid reasoning modes mean we get detailed architectural overviews when needed, but quick updates for simple changes." - Sarah Chen, Engineering Manager

GLM-4.5 Coding Capabilities: Beyond Basic Code Generation

GLM-4.5's coding capabilities extend far beyond simple code generation. The model demonstrates sophisticated understanding of software architecture, debugging skills, and the ability to work with existing codebases.

Benchmark	GLM-4.5	GPT-4 Turbo	Claude 4 Sonnet	Performance Analysis
SWE-bench Verified	64.2%	48.6%	70.4%	Strong real-world debugging
Terminal-Bench	37.5%	30.3%	35.5%	Superior command-line interaction
LiveCodeBench	72.9%	-	63.6%	Excellent recent problem solving
Function Calling Success	90.6%	-	89.5%	Best-in-class tool integration

What sets GLM-4.5 apart in coding tasks is its ability to understand project context. Unlike models that generate isolated code snippets, GLM-4.5 can navigate existing codebases, understand architectural patterns, and make changes that maintain consistency across the entire project.

 # Example: GLM-4.5 generating a full-stack web application

# Frontend (React component with proper styling)
const TaskDashboard = () => {
  const [tasks, setTasks] = useState([]);
  const [loading, setLoading] = useState(true);
  
  useEffect(() => {
    fetchTasks();
  }, []);
  
  const fetchTasks = async () => {
    try {
      const response = await api.get('/tasks');
      setTasks(response.data);
    } catch (error) {
      console.error('Failed to fetch tasks:', error);
    } finally {
      setLoading(false);
    }
  };
  
  // Component continues with proper error handling and accessibility...
};

# Backend (Express.js with database integration)
app.get('/api/tasks', authenticateToken, async (req, res) => {
  try {
    const tasks = await Task.findAll({
      where: { userId: req.user.id },
      order: [['createdAt', 'DESC']]
    });
    res.json(tasks);
  } catch (error) {
    logger.error('Task fetch error:', error);
    res.status(500).json({ error: 'Internal server error' });
  }
});

GLM-4.5 Deployment Checklist

Component	Requirement	Recommended
Hardware	24GB+ VRAM GPU	RTX 4090 or A100
Memory	32GB RAM	64GB+ RAM
Storage	100GB+ SSD	1TB NVMe SSD
Python Environment	Python 3.8+	Python 3.10+
CUDA Support	CUDA 11.8+	CUDA 12.1+
Dependencies	torch, transformers	flash-attn, accelerate
Model Weights	Downloaded from HuggingFace	Local model cache
API Setup	Z.ai API key (optional)	Rate limiting configured

Quick Deployment Commands:

 # Clone the official repository
git clone https://github.com/zai-org/GLM-4.5.git
cd GLM-4.5

# Install requirements
pip install -r requirements.txt

# Download model weights
python download_model.py --model glm-4.5

# Run inference server
python serve.py --model glm-4.5 --port 8000 --gpu-memory-utilization 0.9 

GLM-4.5 vs GPT-4: A Comprehensive Comparison

How does GLM-4.5 stack up against the gold standard? Our comprehensive analysis reveals some surprising results across key performance metrics.

GLM-4.5 Advantages

Open Source: Full model weights available for customization and local deployment
Superior Tool Calling: 90.6% success rate vs GPT-4's estimated 85%
Adaptive Reasoning: Dual-mode system optimizes for both speed and accuracy
Cost Effective: Significantly lower inference costs for equivalent performance

Areas for Improvement

Language Coverage: GPT-4 supports more languages with higher quality
Multimodal: Limited vision capabilities compared to GPT-4V
Maturity: Newer model with less extensive real-world testing
Ecosystem: Smaller developer community and fewer integrations

The Verdict

GLM-4.5 doesn't aim to be a GPT-4 replacement – it's positioning itself as a specialized alternative for developers who need open-source flexibility, superior agentic capabilities, and competitive performance at a fraction of the cost. For many use cases, particularly those involving automation and tool integration, GLM-4.5 actually outperforms GPT-4.

GLM-4.5 Open-Source Features: True AI Sovereignty

Unlike many "open" models with restrictive licenses, GLM-4.5 provides genuine open-source access with Apache/MIT licensing for commercial use. This represents a significant shift toward AI sovereignty.

Full Model Access

Complete model weights available on HuggingFace and ModelScope. No API restrictions or usage limits.

100% Model Availability

Fine-Tuning Support

Full support for custom fine-tuning with provided training scripts and documentation.

95% Feature Coverage

Commercial License

Apache/MIT licensing allows commercial deployment without royalties or restrictions.

100% Commercial Freedom

This open approach has practical implications beyond ideology. Organizations can modify GLM-4.5 for specific domains, integrate it into proprietary systems, and maintain full control over their AI infrastructure. For many enterprises, this represents the difference between AI dependency and AI sovereignty.

Frequently Asked Questions

How does GLM-4.5's 355 billion parameter architecture actually work?

GLM-4.5 uses a mixture-of-experts (MoE) architecture where only 32 billion of the 355 billion parameters are active during any single inference. The model routes inputs to specific "expert" networks based on the type of task, allowing for massive capacity while maintaining reasonable computational costs. This design enables the model to have specialized knowledge domains while keeping inference speed practical.

What makes GLM-4.5's thinking mode different from other reasoning approaches?

GLM-4.5's thinking mode is built into the model architecture rather than being a post-processing technique. When activated, the model generates explicit reasoning traces, evaluates multiple solution paths, and can backtrack when it detects errors. This isn't just longer responses – it's a fundamentally different inference process that trades speed for accuracy on complex tasks. The model dynamically switches between thinking and non-thinking modes based on task complexity.

How does GLM-4.5's function calling compare to GPT-4's tool usage?

GLM-4.5 achieves a 90.6% tool calling success rate compared to GPT-4's estimated 85%. More importantly, GLM-4.5's function calling is "native" – built into the model architecture rather than relying on external frameworks. This means better error handling, more reliable function sequencing, and the ability to recover from failed tool calls. The model understands not just how to call functions, but when and why to use them.

Can GLM-4.5 run on consumer hardware, and what are the real requirements?

GLM-4.5 can run on high-end consumer hardware, but with caveats. You need at least 24GB of VRAM (RTX 4090 or better) for full precision inference. With quantization to 8-bit, you can run it on 20GB VRAM, though with some performance loss. For practical use, 32GB+ system RAM is essential. GLM-4.5-Air (106B parameters) is more consumer-friendly, running effectively on RTX 3090s with proper optimization.

What are the licensing terms for commercial GLM-4.5 usage?

GLM-4.5 is released under Apache 2.0 license, which permits commercial use, modification, and distribution without royalties. This is genuine open-source licensing – you can fine-tune the model, integrate it into commercial products, and even create derivative works. The only requirement is maintaining copyright notices. This contrasts with many "open" models that have restrictive commercial terms.

How does GLM-4.5 handle different programming languages and coding tasks?

GLM-4.5 demonstrates strong multilingual coding capabilities, with particular strength in Python, JavaScript, TypeScript, and Java. On SWE-bench Verified, it achieves 64.2% success rate, indicating strong real-world debugging skills. The model excels at understanding existing codebases, maintaining architectural consistency, and generating production-ready code with proper error handling and documentation. It can work with modern frameworks like React, Vue, Django, and Express.js effectively.

What's the actual cost difference between GLM-4.5 and commercial alternatives?

Running GLM-4.5 locally eliminates per-token costs entirely after initial hardware investment. For cloud deployment, Z.ai's API pricing is significantly lower than OpenAI's GPT-4 rates. However, the bigger cost advantage comes from not needing multiple specialized models – GLM-4.5's unified capabilities for reasoning, coding, and agentic tasks can replace several specialized APIs, providing compound savings for complex workflows.

How does GLM-4.5 perform on mathematical and scientific reasoning tasks?

GLM-4.5 achieves 98.2% accuracy on MATH 500 benchmark and 91.0% on AIME24, placing it among the top reasoning models globally. In thinking mode, it generates step-by-step solutions with explicit reasoning traces. For scientific tasks (SciCode benchmark), it scores 41.7%, demonstrating strong capabilities in computational science problems. The model particularly excels at problems requiring multi-step reasoning and tool usage.

What development tools and frameworks integrate well with GLM-4.5?

GLM-4.5 integrates seamlessly with popular development frameworks including Claude Code, Roo Code, and CodeGeex for coding assistance. It works well with LangChain for agentic workflows, supports OpenAI-compatible API endpoints for easy migration, and has native support for function calling without external frameworks. The model also integrates with VS Code extensions, Jupyter notebooks, and CI/CD pipelines through its API.

The GLM-4.5 Revolution: What This Means for AI Development

GLM-4.5 represents more than just another large language model – it's a paradigm shift toward open, accessible, and truly capable AI systems. With its combination of massive scale, hybrid reasoning, and native agentic capabilities, GLM-4.5 challenges the assumption that cutting-edge AI must remain locked behind proprietary APIs.

The model's 90.6% tool calling success rate and competitive performance across reasoning benchmarks demonstrate that open-source AI can match or exceed proprietary alternatives. For developers and organizations seeking AI sovereignty, GLM-4.5 offers a genuine alternative to dependency on external APIs.

Key Takeaways and Next Steps:

Evaluate GLM-4.5 for your specific use case – Try the model through Z.ai's platform before committing to local deployment
Consider hardware requirements carefully – Budget for adequate GPU memory and system resources for optimal performance
Explore agentic applications – GLM-4.5's native function calling opens possibilities for sophisticated automation workflows
Plan for fine-tuning – Take advantage of the open-source nature to customize the model for your domain
Monitor the ecosystem – GLM-4.5 is part of a broader trend toward open, capable AI models that could reshape the industry

GLM-4.5 proves that innovation in AI doesn't require billion-dollar budgets or exclusive access to computational resources. As the model continues to evolve and the open-source ecosystem grows around it, we're witnessing the democratization of truly advanced AI capabilities. The question isn't whether GLM-4.5 will impact AI development – it's how quickly organizations will adapt to this new paradigm of open, accessible, and powerful AI systems.

Explore More Resources

Official Documentation

Benchmarks and Analysis

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