GLM-4.5-Air excels at the intersection of hardware cost-efficiency and high-quality, long-context reasoning capabilities, positioning itself as a highly practical and versatile solution for demanding real-world applications.
Zhipu AI’s GLM-4.5-Air is a highly efficient, cost-effective large language model built with 106 billion total parameters (12 billion active) using a Mixture-of-Experts (MoE) design. Tailored for a broad spectrum of text-to-text applications, it matches the full GLM-4.5’s 128,000-token context window, enabling comprehension and generation of very long-form text while dramatically reducing computational overhead.
GLM-4.5-Air is engineered for agentic applications, offering a 128,000-token context window and built-in function execution capabilities. On agentic benchmarks such as τ-bench and BFCL-v3, it attains results nearly equivalent to Claude 4 Sonnet. In specialized tests for web browsing (BrowseComp), which assess complex multi-step reasoning and tool use, GLM-4.5-Air achieves a 26.4% accuracy rate, outperforming Claude-4-Opus (18.8%) and approaching the leading o4-mini-high at 28.3%. These results underscore GLM-4.5-Air’s balanced performance in real-world, tool-driven tasks and agent scenarios.
Vs. Claude 4 Sonnet: GLM-4.5-Air offers a competitive balance of efficiency and performance but is slightly behind Claude 4 Sonnet in coding and agentic reasoning tasks. Claude 4 Sonnet supports a larger context window (200k tokens vs. 128k) and includes image input capabilities, making it more suitable for multimodal applications. However, GLM-4.5-Air is open-source, more cost-effective, and provides strong reliability across function calling and multi-turn reasoning.
Vs. GLM-4.5: GLM-4.5-Air achieves about 80-98% of the flagship GLM-4.5’s performance, with significantly fewer active parameters (12B vs. 32B) and reduced resource requirements. While it slightly trails in raw task accuracy, it maintains solid reasoning, coding, and agentic capabilities, making it better suited for deployment in hardware-constrained environments.
Vs. Qwen3-Coder: GLM-4.5-Air competes well with Qwen3-Coder in coding and tool use, delivering fast and accurate code generation for complex programming tasks. GLM-4.5-Air demonstrates dominant success rates and reliable tool calling mechanisms over Qwen3-Coder.
Vs. Gemini 2.5 Pro: GLM-4.5-Air holds close on practical reasoning and coding benchmarks against Gemini 2.5 Pro. While Gemini may excel slightly in some coding and reasoning tests, GLM-4.5-Air offers a favorable balance of large context window and agentic tooling optimized for efficient real-world deployments.
Accessible via AI/ML API. Documentation: available here.
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