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How to Choose the Best Open-Source AI Model—A Comprehensive Review and Comparison of GLM 4.5

#glm #llm

🎯 Key Takeaways (TL;DR)

  • GLM 4.5 is one of the most notable open-source AI models of 2025, featuring hybrid reasoning and highly efficient coding capabilities.
  • Supports both "thinking mode" and "non-thinking mode," excelling at complex reasoning, tool use, and especially code generation and agent applications.
  • Community feedback is overwhelmingly positive; real-world tests show GLM 4.5 performs exceptionally in bioscience knowledge and complex code repair, making it ideal for users seeking high performance and multi-scenario adaptability.

Table of Contents

  1. What is GLM 4.5?
  2. Core Features and Technical Highlights of GLM 4.5
  3. GLM 4.5 vs. Leading Models: Comparative Analysis
  4. How to Use GLM 4.5 Efficiently?
  5. Community Testing & User Feedback
  6. 🤔 Frequently Asked Questions
  7. Conclusion & Actionable Recommendations

What is GLM 4.5?

GLM 4.5 is the latest generation open-source large language model released by the Zhipu AI team, based on a Mixture-of-Experts (MoE) architecture and specifically designed for AI agent scenarios. The flagship 355B parameter version and the lightweight 106B parameter GLM-4.5-Air both support multilingual, reasoning, coding, tool use, and more, meeting the needs of complex tasks.

💡 Pro Tip
GLM 4.5 supports both "thinking mode" (for complex reasoning and toolchain usage) and "non-thinking mode" (for fast responses), making it flexible for various scenarios.

Core Features and Technical Highlights of GLM 4.5

Architecture

  • Mixture-of-Experts (MoE) Design: 355B total parameters, 32B active (Air version: 106B/12B); deeper architecture enhances reasoning.
  • Grouped-Query Attention + Partial RoPE: Improves long-context stability.
  • Sigmoid MoE Gating + Lossless Routing: Efficient resource allocation.
  • QK-Norm, Multi-Token Prediction: Faster and more stable reasoning, improved multi-step prediction and decoding.
  • Muon Optimizer: Supports large batch training and faster convergence.

Training Data & Workflow

  • Pretrained on 22T tokens (15T general + 7T code/reasoning)
  • Large-scale Reinforcement Learning (RL) covers real-world agent flows and multi-domain knowledge

Mode Switching

  • Thinking Mode: For complex reasoning, tool use, and advanced tasks
  • Non-Thinking Mode: For daily Q&A and quick responses

GLM 4.5 vs. Leading Models: Comparative Analysis

Model Parameter Size Main Strengths Coding Reasoning Tool Use Community Feedback
GLM 4.5 355B/106B Hybrid reasoning + top coding ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐ ⭐⭐⭐⭐⭐ Very positive
Qwen3 200B+ Strong general capabilities ⭐⭐⭐⭐ ⭐⭐⭐⭐⭐ ⭐⭐⭐ Balanced
Kimi-K2 100B+ Balanced code & reasoning ⭐⭐⭐⭐ ⭐⭐⭐⭐ ⭐⭐⭐⭐ Highly regarded
Llama 4 Scout 70B+ Lightweight local deployment ⭐⭐⭐ ⭐⭐⭐ ⭐⭐ Easy to use

✅ Best Practice
For agent development, code generation, and complex reasoning, GLM 4.5 is the top open-source choice. For general Q&A or lightweight deployment, Qwen3 or Llama 4 Scout may be better options.

How to Use GLM 4.5 Efficiently?

Deployment & Usage

  1. Download from HuggingFace: Supports safetensors format; compatible with mainstream inference frameworks (e.g., transformers, vLLM, SGLang).
  2. Online API: Available via Z.ai or Zhipu AI platforms for instant experience.
  3. Local Deployment: The Air version is suitable for high-end local hardware and supports hybrid RAM/VRAM inference.

Deployment Workflow Diagram 

graph TD
    A[Choose Model Version] --> B[Download Model Weights]
    B --> C[Configure Inference Environment]
    C --> D[Call API or Run Locally]
    D --> E[Integrate into Application]
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💡 Pro Tip
GLM 4.5-Air is suitable for local deployment with 64GB+ RAM. Q4 quantization can further lower hardware requirements.

Community Testing & User Feedback

Experience Highlights

  • Multiple community members report that GLM 4.5 outperforms Qwen3 and Kimi-K2 in bioscience, complex project-level code repair, and tool use/multi-step reasoning tasks.
  • Users note that GLM 4.5's "thinking mode" is both fast and surprisingly advanced in its reasoning.
  • Some suggest further fine-tuning for writing and creative tasks to improve versatility.

Community Opinion Comparison

Feedback Type Key Points
Performance "GLM 4.5 achieves a 90.6% tool use success rate, excels at code repair."
Applicability "Ideal for agent development and complex coding; room for improvement in general Q&A and creative writing."
Deployment "Air version Q4 quantization runs on 64GB RAM, faster than peers."
Future Trends "100B+ MoE models are the new trend for local AI deployment."

✅ Best Practice
Select the right model version and quantization based on your main use case and leverage community feedback for local testing.

🤔 Frequently Asked Questions

Q: What are the best use cases for GLM 4.5?

A: Ideal for agent development, complex code generation, toolchain integration, multi-step reasoning, and advanced Q&A—especially where strong reasoning and coding are required.

Q: How to switch between thinking and non-thinking modes?

A: Specify the mode via API or inference parameters. Some community tools support one-click switching; refer to the official docs or community tutorials.

Q: Hardware requirements for local deployment of GLM 4.5-Air?

A: Recommended 64GB+ RAM; Q4 quantization reduces to about 57GB, suitable for high-end consumer or professional workstations.

Q: Advantages of GLM 4.5 over Qwen3 and Kimi-K2?

A: GLM 4.5 excels in complex reasoning and tool use scenarios, with outstanding community-reported performance in code repair and agent tasks.

Conclusion & Actionable Recommendations

With its hybrid reasoning architecture and efficient coding capabilities, GLM 4.5 stands out as a leader in the 2025 open-source AI model landscape. Whether you're building agents, automating toolchains, or generating complex code, GLM 4.5 delivers robust support. Developers are encouraged to trial both the full and Air versions, engage with the community, and stay updated on model iterations and best practices.

💡 Pro Tip
Join discussions on Reddit and HuggingFace to get firsthand feedback and optimization tips, accelerating your model deployment.

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