Super resolution (SR) technique is an important means for image resolution improvement, which has been widely used in remote sensing, medicine image processing, target recognition and tracking etc. In recent years, the deep learning techniques also have been applied in the SR domain successfully. However, researchers pay most of their attentions on the quality of the output images, but ignore the training or reconstruction efficiency. In this paper, we found that for images with different texture features, the most appropriate models are usually different. Based on this observation, a multi-model super resolution framework (MMSR) is proposed, which can choose a suitable network model for each image for training. Experimental results with the DIV_2K image set indicate that, efficiency can be improved 66.7% without the loss of image quality. Moreover, MMSR exhibits good scalability.