Compressed Sensing theory is often applied to accelerate the acquisition of multi-contrast MR images. When highly undersampled, CS-MRI suffers from non-negligible reconstruction error. Here we propose an unrolled iterative deep-learning model to further utilize the group sparsity property for multi-contrast MRI reconstruction at high acceleration factor, named Joint-ISTA-Net, to reduce reconstruction error and aliasing. Our method adds a joint-shrinkage-thresholding model into ISTA-Net to generate a better reconstruction for multi-contrast image pairs. Experiments show the effectiveness of the proposed strategy.