We propose a new proxy measure for Neural Architecture Search (NAS) focusing on the flatness of loss surface. One step forward to the existing NAS studies utilizing the validation-set accuracy or angle which measures convergence speed during training, we claim that the flatness of the loss surface can be a promising proxy for predicting the generalization capability of neural network architectures.