Model Releases
Zhipu AI Unveils GLM-4.5 Air: The Efficient 106B MoE Powerhouse
Zhipu AI releases GLM-4.5 Air, a lightweight 106B MoE variant optimized for efficient inference on H20 GPUs with open-source weights.
July 28, 2025
Model ReleaseGLM-4.5 Air
Introduction
In the rapidly evolving landscape of large language models, Zhipu AI has once again pushed the boundaries of efficiency and capability with the release of GLM-4.5 Air. Announced on July 28, 2025, this model represents a strategic shift towards democratizing high-performance AI through optimized inference costs and open-source accessibility. While the flagship GLM-4.5 boasts massive parameters, the Air variant targets developers who need the intelligence of a 100B+ model without the prohibitive hardware requirements.
This release is particularly significant for startups and enterprises looking to deploy agentic workflows without the overhead of massive clusters. By leveraging a Mixture of Experts (MoE) architecture, GLM-4.5 Air delivers competitive performance while maintaining a lean footprint. It is designed to run efficiently on standard hardware configurations, such as 8x H20 GPUs, making it a viable choice for production environments where cost-per-token is a critical metric.
- Release Date: July 28, 2025
- Provider: Zhipu AI
- License: MIT (Open Source)
- Target Hardware: 8x H20 GPUs
Key Features & Architecture
The architecture of GLM-4.5 Air is built on a 106B parameter MoE structure, which allows the model to activate only a subset of parameters per token, significantly reducing computational load during inference. This design choice is critical for maintaining low latency while processing complex reasoning tasks. Unlike dense models, the MoE approach ensures that the model remains lightweight without sacrificing the depth of knowledge required for advanced coding and agent tasks.
Developers will appreciate the hybrid reasoning capabilities embedded in the model. It supports both thinking modes for complex problem-solving and non-thinking modes for immediate responses. The model is fully open-source, allowing for fine-tuning on proprietary datasets and deployment on private infrastructure. This flexibility is a key differentiator in the current market where data privacy is paramount.
- Parameters: 106B MoE
- Context Window: 128k tokens
- Max Output: 32k tokens
- Architecture: Mixture of Experts
Performance & Benchmarks
Performance-wise, GLM-4.5 Air has been rigorously tested across industry-standard benchmarks. According to LLM-Stats data, the model achieved a score of 59.8 across 12 benchmarks, ranking 6th globally in its category. This score is competitive with other open-source models of similar size, proving that the 106B parameter count is sufficient for high-quality generation. The model excels in coding benchmarks, matching the capabilities of the larger GLM-4.5 flagship in many synthetic tasks.