Inside Zhipu AI: What Makes GLM 4.5 & 4.5 Air a Game-Changer in LLMs?
Zhipu AI, now rebranded as “Z.ai” with its latest milestone — the launch of GLM 4.5.
Showed at World AI Conference in Shanghai on 29 July 2025, GLM-4.5 is Zhipu’s most advanced model to date, designed for agent-native intelligence, multi-modal reasoning, and cost-effective deployment. It also introduces a lightweight version, GLM-4.5 Air, built for high performance on low-power setups.
But what makes this launch truly disruptive is its open-source licensing and dual-mode reasoning. While challenging not just Chinese competitors like DeepSeek, but even GPT‑4o and Claude 3 on the global stage.
What Is GLM-4.5?
GLM-4.5 is the latest large language model developed by Zhipu AI, a leading Chinese AI company with backing from institutions like Tsinghua University. While earlier GLM models gained attention for their bilingual (English–Chinese) proficiency, GLM-4.5 breaks new ground with:
This model family is designed to power intelligent agents, autonomous decision-making systems, and high-speed inference tasks across diverse environments—from cloud to edge devices.
GLM-4.5 Air was specifically designed to make AI more accessible. It can run efficiently on just eight NVIDIA H20 GPUs, slashing infrastructure requirements in half compared to rivals like DeepSeek V2.
Key Features Of GLM 4.5 Large Language Model
1. Dual Reasoning Modes
Thinking Mode with high-complexity reasoning, coding, agentic workflows. Non-Thinking Mode with ultra-fast response generation with minimal resource use (~200 tokens/sec).
2. Agentic AI Use Cases
It is tested that GLM 4.5 goes beyond conversation limit and support function calling, tool usage, multi-step task planning, and reasoning pipelines.
3. Free & Open Source
Both versions are released under MIT and Apache 2.0 licenses, boasting it sourceability for general use while being transparent, modifiable, and free to use commercially.
Performance Benchmark Of GLM 4.5 And GLM 4.5 Air
Studying the benchmark comparison chart, it showing how different large language models are perform in the areas of agentic, coding, and reasoning tasks. It highlights Zhipu AI’s new models — GLM-4.5 and GLM-4.5 Air and how they stack up against competitors like GPT-4, Claude 4, Grok, and DeepSeek.
Overall, the model GLM-4.5 ranks 3rd overall, and GLM-4.5 Air ranks 6th, showing impressive performance.
Pricing Comparison: GLM 4.5 vs Deepseek vs Claude 3 vs GPT 4o
With such low-budget pricing, it opens serious possibilities for startups, educators, and IT companies to deploy models at scale without burning cloud credits.
How To Access New GLM 4.5 Model?
Access GLM 4.5 model via Hugging Face platform. At GitHub, you can access source code, training methods, and tools. At official website (z.ai) developers explore model API, agent SDKs, and Sandbox environment.
What Developers Can Build Using GLM 4.5?
Real-world applications developers can build using GLM‑4.5 and GLM‑4.5 Air are as followed.
1. Autonomous AI Agents
2. Advanced Reasoning Workflows
3. Content Generation Tools
4. RAG Systems
5. Developer Tools
What Are The Use Cases Of GLM 4.5?
Healthcare industries can take major benefit for GLM 4.5 for instance AI-powered symptom checker, health advise, and summarization. In Education, AI tutors for math, science, and language learning. In finance, it may act as finance advisor via fine-tune datasets.
Does GLM‑4.5 Have a Support Community?
Yes, Zhipu AI’s GLM 4.5 have a developer and open-source community to share learning and updates. Additional resources such as code examples, fine-tuning scripts, model weight, and instructions for local deployment can be found at official platform.
Conclusion
The crown of best open source model was previously at Kimi K2, then Qwen3, and now it goes to GLM 4.5 with super contrary achievement. For developers, researchers, and enterprises watching the AI cost curve, GLM-4.5 offers power at a fraction of the price.
The real questions is how would your organization use GLM 4.5 LLM? Share your thoughts in the comment. Thanks for reading 🙂
This post was originally published at The Next Tech → Read here