Abstract: As the core operator of Transformers, Softmax Attention exhibits excellent global modeling capabilities. However, its quadratic complexity limits its applicability to vision tasks. In contrast, Linear Attention shares a similar formulation with Softmax Attention while achieving linear complexity, enabling efficient global information modeling. Nevertheless, Linear Attention suffers from a significant performance degradation compared to standard Softmax Attention. In this paper, we analyze the underlying causes of this issue based on the formulation of Linear Attention. We find that, unlike Softmax Attention, Linear Attention entirely disregards the magnitude information of the Query($Q$ or $\phi(Q)$). The absence of magnitude information prevents the attention score distribution from dynamically adapting as the Query scales. As a result, despite its structural similarity to Softmax Attention, Linear Attention exhibits a significantly different attention score distribution. Based on this observation, we propose **Magnitude-Aware Linear Attention** (MALA), which modifies the computation of Linear Attention to fully incorporate the Query’s magnitude. This adjustment allows MALA to generate an attention score distribution that closely resembles Softmax Attention while exhibiting a more well-balanced structure. As a result, MALA surpasses Softmax Attention in performance while maintaining only linear complexity. We build Magnitude-Aware Vision Transformer (MAViT) based on MALA, achieving **84.7%** accuracy on ImageNet-1K with only **27M** parameters and **4.6G** flops, without using any additional data or labels. It also exhibits excellent inference efficiency. This result highlights the strong potential of MALA.