GLM 4.5 vs Qwen 3: In-Depth Comparison of Models, Performance & Costs

GLM 4.5 and Qwen 3 are emerging as significant open-source large language models (LLMs) developed by Chinese labs, offering advanced capabilities at a lower cost compared to proprietary Western models. GLM 4.5 is tailored towards efficient tool-calling and agentic workflows, utilizing a Mixture-of-Experts (MoE) architecture with 355 billion total parameters but only activating 32 billion, making it ideal for constructing AI systems that require external function calls and documentation browsing. Meanwhile, Qwen 3, which activates 35 billion out of 480 billion parameters, excels in long-context reasoning and multilingual tasks, supporting 119 human languages and 358 programming languages, with a context window extending from 256,000 to 1 million tokens. Both models are under permissive licenses, facilitating local deployment and customization, and they epitomize a geopolitical shift as Chinese labs innovate with local hardware. While GLM 4.5 is more cost-effective and excels in tool-calling reliability, Qwen 3 offers unmatched context length and language support, though at a higher cost and hardware requirement. Clarifai provides a platform to streamline the deployment of these models, offering tools for compute orchestration, local processing, and multimodal applications.

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