A new Mixture of Experts Neural Network (ME-NN) approach is described and proposed for modeling of nonlinear RF power amplifiers (PAs). The proposed ME-NN is compared with various piece-wise polynomial models and the time-delay neural network (TDNN) regarding their ability to scale in terms of modeling accuracy and parameter count. To this end, measurements with GaN Doherty PA at 1.8 GHz and a load modulated balanced (LMBA) PA operating at 2.1 GHz with strong nonlinear behavior and dynamics are employed, assessing the potential benefits of ME-NN over the existing models. Implementation-related advantages of the proposed ME-NN over TDNNs at increasing network sizes are furthermore discussed. The measurement results show that the ME-NN approach offers increased modeling accuracy, particularly in the LMBA PA case, compared to the existing reference methods.