Introduction to GLM-4.5 and GLM-4.5-Air
The artificial intelligence (AI) landscape is undergoing transformative changes, and one of the most notable developments in 2025 is Zhipu AI’s release of the GLM-4.5 series. Comprising two models, GLM-4.5 and GLM-4.5-Air, these systems aim to redefine open-source agentic AI by integrating hybrid reasoning capabilities. Designed to seamlessly connect reasoning, coding, and intelligent agent functionalities, they cater to both high-demand applications and mainstream user accessibility.
Model Architecture and Parameters
Understanding the architecture of these models is crucial for appreciating their capabilities.
- GLM-4.5: With a staggering 355 billion total parameters (32 billion active), it stands as one of the largest open-source models, noted for its exceptional benchmark performance.
- GLM-4.5-Air: A more compact version featuring 106 billion total parameters (12 billion active), this model is optimized for efficiency and compatibility with consumer hardware.
GLM-4.5 employs a Mixture of Experts (MoE) architecture, allowing for versatile applications across various AI tasks. This means users can engage with powerful tools without the need for top-tier hardware, making advanced AI accessible to a broader audience.
Hybrid Reasoning: A Dual Approach
One of the standout features of the GLM-4.5 series is the hybrid reasoning approach. This design comprises two distinct modes:
Thinking Mode
This mode enables complex reasoning, tool utilization, and multi-turn planning, making it ideal for sophisticated tasks requiring in-depth cognitive processing.
Non-Thinking Mode
In contrast, the non-thinking mode provides quick, stateless responses, perfect for conversational applications and immediate interactions.
This dual functionality ensures that users benefit from both advanced reasoning capabilities and quick-response times, enhancing their interaction with AI systems.
Performance Benchmarks
Performance metrics reveal much about the efficiency of AI models. In testing across 12 industry-standard benchmarks, GLM-4.5 achieved an average score of 63.2, placing it third overall and second globally among open-source models. Furthermore, GLM-4.5-Air secured a competitive score of 59.8, establishing a strong position among models with approximately 100 billion parameters.
These models also excelled in specific tasks such as tool-calling, achieving a success rate of 90.6%, surpassing competitors like Claude 3.5 Sonnet and Kimi K2. Their strong performance in Chinese-language tasks and coding further highlights their versatility across diverse applications.
Agentic Capabilities and Architecture
The core design philosophy of GLM-4.5 emphasizes agent-native functionalities. Some of its key features include:
- Multi-step task decomposition and planning.
- Integration with external APIs for enhanced tool use.
- Complex data visualization capabilities.
- Native support for perception-action cycles.
These attributes enable the implementation of agentic applications that were previously limited to more rigid frameworks or closed-source systems.
Efficiency, Speed, and Cost
Performance is not just about capabilities; it also involves speed and cost-effectiveness. The introduction of Speculative Decoding and Multi-Token Prediction (MTP) in GLM-4.5 allows for inference speeds that are 2.5 to 8 times faster than previous models, achieving generation rates exceeding 100 tokens per second.
In terms of hardware requirements, GLM-4.5-Air’s 12 billion active parameters can run on consumer-grade GPUs, making it accessible for local deployments. Additionally, the pricing structure for API calls starts as low as $0.11 per million input tokens, making advanced AI economically viable for developers.
Open-Source Access and Ecosystem
Another significant aspect of the GLM-4.5 series is its commitment to open-source principles, as demonstrated by its MIT license. This enables unrestricted commercial use, secondary development, and a robust ecosystem for integration and fine-tuning. The models are compatible with major frameworks, including transformers and vLLM, and detailed resources are available on platforms like GitHub and Hugging Face, encouraging broader collaboration and innovation in the AI community.
Key Technical Innovations
GLM-4.5 introduces several groundbreaking innovations:
- Multi-Token Prediction layer for accelerating inference on various hardware platforms.
- A unified architecture that combines reasoning, coding, and multimodal capabilities.
- Support for extensive input and output context windows, with training on a massive dataset of 15 trillion tokens.
- Immediate compatibility with research and production tools, facilitating easier adaptation for new use cases.
Conclusion
In summary, the launch of the GLM-4.5 and GLM-4.5-Air models marks a significant advancement in open-source, agentic AI technology. With their hybrid reasoning capabilities, impressive performance metrics, and commitment to accessibility, these models are poised to empower the next generation of intelligent agents and developer applications. They set a new benchmark for performance, accessibility, and cognitive capabilities in the AI realm.
FAQs
- What are the primary differences between GLM-4.5 and GLM-4.5-Air? GLM-4.5 has more parameters and is designed for high-demand applications, while GLM-4.5-Air is optimized for efficiency and mainstream hardware.
- How does the hybrid reasoning approach benefit users? It allows for both complex reasoning tasks and quick responses, catering to a wide range of applications.
- What are the performance benchmarks of these models? GLM-4.5 scored an average of 63.2 in industry tests, while GLM-4.5-Air scored 59.8, showcasing their competitive edge.
- Is GLM-4.5 available for commercial use? Yes, the models are released under an MIT license, allowing unrestricted commercial use and secondary development.
- What innovations does GLM-4.5 introduce? Key innovations include Multi-Token Prediction for faster inference and a unified architecture for diverse AI tasks.
