This letter develops a framework for differentiating sparse optimal control inputs with respect to cost parameters. The difficulty lies in the non-smoothness induced by a sparsity-enhancing regularizer. To avoid this, we identify the optimal inputs as a unique zero point of a function using the proximal technique. This enables us to characterize the differentiability and employ the implicit function theorem. We also demonstrate the effectiveness of our approach using a numerical example of inverse optimal control.