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GLM-4.5 Breakthrough: How This Open-Source AI Model Outperforms Competitors in Coding & Reasoning

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GLM-4.5: A Breakthrough in Open-Source AI Language Models


Figure 1: GLM-4.5’s average performance across Agentic, Reasoning, and Coding (ARC) benchmarks

1. What is GLM-4.5?

GLM-4.5 is a new generation of open-source large language model (LLM) developed by Zhipu AI and Tsinghua University. Unlike conventional language models, it employs a 「Mixture-of-Experts (MoE) architecture」, maintaining high parameter scale (355 billion total parameters) while achieving efficient computation through dynamic activation (only 32 billion parameters actively participate in calculations).

Key Features:

  • 「Multi-modal reasoning」: Supports both “thinking mode” and “direct response” modes
  • 「Domain excellence」: Outstanding performance in agentic tasks, complex reasoning, and code generation
  • 「Open-source friendly」: Available in two versions – full 355B parameter model and compact 106B GLM-4.5-Air

2. Architecture Design: Why MoE?

2.1 What is MoE Architecture?

MoE (Mixture-of-Experts) is a specialized neural network structure that divides the model into multiple “expert modules.” Each input token dynamically selects the most relevant experts for processing, similar to a hospital triage system – different symptoms are directed to different departments.

「Parameter Comparison Table」

Model Total Parameters Activated Parameters Dense Layers MoE Layers
GLM-4.5 355B 32B 3 89
GLM-4.5-Air 106B 12B 1 45
DeepSeek-V3 671B 37B 3 58
Kimi K2 1043B 32B 1 60

Data Source: Paper Table 1

2.2 Architectural Innovations

  1. 「Depth-First Design」
    Compared to similar models (e.g., DeepSeek-V3), GLM-4.5 prioritizes increasing model depth over width. Experiments show 「deeper architectures perform better in mathematical reasoning tasks」.

  2. 「Attention Mechanism Optimization」

    • Uses Grouped-Query Attention (GQA)
    • Increases attention heads to 96 (with 5120 hidden dimension)
    • Implements QK-Norm technology to stabilize attention logic ranges

  3. 「Multi-Token Prediction Layer (MTP)」
    Adds a specialized layer at the model end to support speculative decoding during inference, 「improving generation speed by over 30%」.

3. Training Process: How to Build a Top-Tier Model?

3.1 Pre-training: Data Determines the Ceiling

「Training Data Composition」

  • Web pages (English/Chinese)
  • Multilingual texts (filtered by quality classifiers)
  • Code repositories (GitHub and other platforms)
  • Mathematics/science literature

「Data Processing Highlights」

  • Web deduplication: Combines MinHash and semantic deduplication (SemDedup) techniques
  • Code enhancement: Uses Fill-In-the-Middle objective function
  • Quality stratification: Assigns weights to different data sources, prioritizing high-frequency knowledge

3.2 Mid-Training: Specializing Capabilities

Stage 1: Repository-Level Code Training

  • Trains on concatenated code files from the same repository
  • Includes issues, pull requests (PRs), and commit histories
  • Extends sequence length to 32K tokens

Stage 2: Synthetic Reasoning Data

  • Generates mathematical/science competition problem solutions
  • Constructs coding competition problems and solutions

Stage 3: Long Context & Agent Training

  • Extends sequence length to 128K tokens
  • Introduces large-scale agent trajectory data


Figure 3: Complete pre-training and mid-training process for GLM-4.5

3.3 Post-Training: Expert Model Iteration

Adopts a two-stage strategy:

  1. 「Expert Training」
    Trains three specialized models:

    • Reasoning Expert
    • Agent Expert
    • General Chat Expert

  2. 「Unified Training」
    Integrates expert capabilities through self-distillation, resulting in a final model capable of:

    • Deep thinking mode (for complex tasks)
    • Fast response mode (for casual conversation)

4. Performance: How Does It Actually Perform?

4.1 Agentic Tasks

「Key Metrics」

Benchmark GLM-4.5 Comparison Model (o3)
TAU-Retail 79.7% 70.4%
BFCL V3 77.8% 72.4%
BrowseComp 26.4% 49.7%

Data Source: Paper Table 3

「Real-World Application Example」
In TAU-Bench retail scenario testing, the model can:

  • Understand multi-turn conversations
  • Query APIs for product inventory
  • Handle complex operations like order modifications

4.2 Reasoning Capabilities

「Math & Science Task Performance」

Benchmark Accuracy Comparison Model (o3)
AIME 24 91.0% 90.3%
MATH-500 98.2% 99.2%
GPQA 79.1% 82.7%

Data Source: Paper Table 4

「Technical Highlights」

  • 91% accuracy on AIME 2024 math competition problems
  • Solves 14.4% of “Humanity’s Last Exam” (HLE) questions
  • Scores 72.9% on LiveCodeBench coding tasks

4.3 Code Generation

「SWE-bench Verified Results」

Model Accuracy Comparison Model (Claude Sonnet 4)
GLM-4.5 64.2% 70.4%

Data Source: Paper Table 5

「Practical Capability Demonstration」

  • 64.2% success rate in fixing real GitHub issues
  • Scores 37.5% on Terminal-Bench terminal tasks

5. Typical Application Scenarios

5.1 Code Development Assistant

「Supported Functions」

  • Automatically generates Python/JavaScript code
  • Fixes issues in existing codebases
  • Understands project documentation and provides suggestions

「Real-World Case」
In CC-Bench testing:

  • 40.4% task win rate vs Claude Sonnet 4
  • 90.6% tool call success rate (industry leading)

5.2 Intelligent Customer Service System

「Core Advantages」

  • Supports complex multi-turn conversations
  • Understands implicit user intentions
  • Calls external APIs to process order inquiries

5.3 Educational Assistance

「Applicable Scenarios」

  • Automatic math problem solving
  • Code assignment grading
  • Scientific concept explanations

6. How to Access and Use?

6.1 Download Links

6.2 Hardware Requirements

Model Version VRAM Requirement Recommended GPU
GLM-4.5 80GB+ NVIDIA H100/A100
GLM-4.5-Air 24GB+ NVIDIA A10/A6000

7. Frequently Asked Questions (FAQ)

Q1: Who is GLM-4.5 suitable for?

Suitable for developers needing to build intelligent agent systems, automated coding tools, or complex dialogue robots. Particularly ideal for:

  • Enterprise intelligent customer service development
  • Code assistance tool construction
  • Educational AI applications

Q2: How to call the model API?

Official Python SDK example:

from zhipuai import ZhipuAI

client = ZhipuAI(api_key="your_api_key")
response = client.chat.completions.create(
    model="glm-4.5",
    messages=[{"role": "user", "content": "Write a Python sorting algorithm"}]
)
print(response.choices[0].message.content)

Q3: What advantages does it have compared to other models?

  • 「Parameter efficiency」: Achieves performance comparable to models with tens of billions more parameters
  • 「Long context handling」: Natively supports 128K tokens context
  • 「Multilingual support」: Strong mixed Chinese/English processing capabilities

Q4: Are there fine-tuning guidelines?

The official repository provides:

  • LoRA fine-tuning example code
  • RLHF training framework
  • Multi-GPU training configuration templates

8. Technical Evolution Directions

The paper mentions future focus areas:

  1. 「Multi-modal capabilities」: Support for image/video understanding
  2. 「Inference speed optimization」: Through speculative decoding technology
  3. 「Enhanced safety」: Better value alignment

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