Transformer-based models have demonstrated state-of-the-art results in the field of image super-resolution. However, we observe that such methods sometimes suffer from overly smooth structural reconstruction and blurred details, indicating that the potential of Transformers has not yet been fully exploited in existing networks. To leverage more prior information, this paper proposes a novel Global Cross Attention Transformer (GCAT) algorithm. This algorithm introduces external prior information by incorporating a cross-attention mechanism alongside the original self-attention mechanism. Furthermore, to better establish the model, we apply cross-attention across all Transformer modules to enhance the model capability for complex mapping. Extensive experiments demonstrate the efficacy of the proposed architecture, with the overall approach exceeding the performance of current state-of-the-art methods.