In this paper, we extend the U-Net model and propose a novel hybrid pyramid U-Net (HPU-Net) model which explores the global context information combined different region based context. Global context information combination is effective for producing good quality results in tumor segmentation tasks, and HPU-Net provides a better framework for pixel-level prediction. Because of the continuous downsampling of FCN the resolution of the feature map gradually decreases and direct upsampling during restoration of resolution will introduce noise and make the segmentation inaccurate. A novel and efficient multimodal tumor segmentation (including internal tumor) model based on U-Net is proposed to perform end-to-end training and testing. Our model includes a downsampling path and a symmetrical upsampling path, concatenating the features at the symmetrical block of upsampling and downsampling path. In the process of upsampling, we extract multiple scale features from every block, and add them pixel-wise after recovering them to origin resolution. Integrating the multi-scale information, semantic and location information before softmax layer, it helps the model complete the segmentation efficiently. The model was evaluated on two datasets BRATS2015 and BRATS2017, and outperformed state-of-the-art methods with better segmentation results.