In this letter, to better supplement the advantages of features at different levels and improve the feature extraction ability of the network, a novel multilevel feature interaction transformer network (MFITN) is proposed for pansharpening, aiming to fuse multispectral (MS) and panchromatic (PAN) images. In MFITN, a multilevel feature interaction transformer encoding module is designed to extract and correct global multilevel features by considering the modality difference between source images. These features are then fused using the proposed multilevel feature mixing (MFM) operation, which enables features to fuse interactively to obtain richer information. Furthermore, the global features are fed into a convolution neural network (CNN)-based local decoding module to better reconstruct high-spatial-resolution multispectral (HRMS) images. Additionally, based on the spatial consistency between MS and PAN images, a band compression loss is defined to improve the fidelity of fused images. Numerous simulated and real experiments demonstrate that the proposed method has optimal performance compared to state-of-the-art methods. Specifically, the proposed method improves the SAM metric by 7.89% and 6.41% compared to the second-best comparison approach on Pléiades and WorldView-3, respectively.