**[Alibaba Launches More Efficient Qwen3-Next AI Model]** Golden Finance reports that Alibaba's Tongyi Qianwen has unveiled the next-generation foundational model architecture, Qwen3-Next, and open-sourced the Qwen3-Next-80B-A3B series models based on this architecture. Compared to the MoE model structure of Qwen3, the new architecture introduces the following key improvements: hybrid attention mechanisms, high-sparsity MoE structure, a series of optimizations for training stability, and a multi-token prediction mechanism to enhance inference efficiency. Using the Qwen3-Next model architecture, Alibaba has trained the Qwen3-Next-80B-A3B-Base model, which features 80 billion parameters while activating only 3 billion parameters. This Base model achieves performance comparable to or slightly better than the Qwen3-32B dense model, while its training cost (GPU hours) is less than one-tenth of Qwen3-32B. Additionally, its inference throughput for contexts exceeding 32k is more than ten times that of Qwen3-32B, delivering exceptional cost-effectiveness in both training and inference.