GLM-4.5: Unified Breakthrough in Reasoning, Coding, and Agentic Abilities

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July 28, 2025 · Research
Keywords: Large Language Models, AI Agents, Code Generation, Reasoning Capabilities, GLM-4.5

Why We Need Generalist AI Models?

Current AI development faces a critical challenge: specialized models excel in narrow domains but lack comprehensive abilities. For example:

• Some models solve complex math problems but struggle with code generation
• Others handle tool interactions but fail at deep logical reasoning
• Most require switching between specialized models for different tasks

GLM-4.5’s mission: Unify reasoning, coding, and agentic capabilities within a single model to meet growing demands of complex AI applications.

Core Features at a Glance

Performance Benchmarks: Triple Capability Validation

(1) Agentic Abilities: Tool Usage & Web Interaction

Competitive results across three industry tests:

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Real-world application:
When asked “Latest breakthroughs in quantum computing for 2025”, GLM-4.5 autonomously uses browsing tools to retrieve recent papers and extracts key findings.

(2) Reasoning Capabilities: Math/Science/Logic Mastery

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Note: Volatile benchmarks like AIME24 use 32-sample averaging for stability.

(3) Coding Prowess: Full-Stack Development

Real-world development validation:

Full-stack demonstration:
Command: “Create user management system with login UI and database” generates:

• React frontend components
• Flask backend APIs
• MySQL schema
  Complete code: GitHub Repository

Technical Innovations: The Unification Framework

Architectural Breakthrough: Depth-Optimized MoE Design

Training Strategy: Four-Stage Capability Fusion

graph LR
A[General Pretraining] --> B[Code/Reasoning Specialization]
B --> C[Domain Fine-Tuning]
C --> D[Reinforcement Learning]

1. General Pretraining: 15T token universal corpus
2. Specialized Enhancement: 7T token code/reasoning corpus
3. Domain Tuning: Medium-scale domain datasets (including instructions)
4. RL Optimization: Reinforcement learning via slime framework

Reinforcement Learning Engine: slime’s Triple Innovation

Practical Applications: From Slides to Full-Stack Development

Application 1: Intelligent Presentation Creation

Workflow:

1. User request: “Create 5-slide presentation about quantum computing”
2. GLM-4.5 autonomously:

   • Searches web for updated content
   • Designs visual layouts
   • Outputs playable HTML slides
     

Application 2: End-to-End Web Development

Case Study:

1. User provides basic template
2. Iterative development via conversation:

   • “Add user comment feature”
   • “Integrate payment gateway”
3. Outputs production-ready code
   Live Demo

Application 3: Toolchain Integration

Seamless compatibility with development ecosystems:

# Using GLM-4.5 with Claude Code
from claude_code import Agent
agent = Agent(model="glm-4.5")
agent.execute_task("Analyze sales.csv and generate visual report")

Supported frameworks:

• Claude Code
• Roo Code
• CodeGeex

Getting Started with GLM-4.5

Option 1: Online Access (Zero Setup)

1. Visit Z.ai Platform
2. Select “GLM-4.5” model
3. Directly test capabilities:

   • Generate interactive games
   • Create physics simulations
   • Design marketing posters

Option 2: API Integration (Developers)

# OpenAI-compatible endpoint
curl https://api.z.ai/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -d '{
    "model": "glm-4.5",
    "messages": [{"role": "user", "content": "Extract insights from this PDF..."}]
  }'

Full API Documentation

Option 3: Local Deployment (High Performance)

# vLLM deployment example
pip install vllm
python -m vllm.entrypoint \
  --model zai-org/glm-4.5-base \
  --tensor-parallel-size 4

Complete Deployment Guide

Frequently Asked Questions (FAQ)

Q1: How does GLM-4.5 differ from GPT-4.1?

Key distinction: Capability unification

• GLM-4.5: Single model handles reasoning/coding/tool use
• GPT-4.1: Requires specialized models (e.g., Codex for programming)

Q2: Which version suits smaller projects – GLM-4.5 or Air?

Selection guide:

Q3: How to troubleshoot failed tool calls?

Debugging steps:

1. Activate Thinking Mode (not Non-Thinking Mode)
2. Verify function descriptions follow OpenAPI specs
3. Include examples: Provide tool-call demonstrations in prompts

Q4: Is commercial use permitted?

Licensing:

• Z.ai API usage: Follow platform commercial policy
• Local deployment: Apache 2.0 license for open weights

Conclusion: Toward General-Purpose AI

GLM-4.5 achieves unification through:

1. Depth-optimized MoE architecture: Balances efficiency and reasoning
2. Four-stage training: Integrates general and specialized capabilities
3. slime RL framework: Solves long-horizon task challenges

First single model to simultaneously deliver:

• Reasoning: Competition-level math (AIME24: 91.0)
• Coding: Full-stack development (90.6% tool success)
• Agents: Complex web interaction (BrowseComp beats Claude 4 Opus)

Experience now: https://chat.z.ai
Source code: GitHub Repository