Model predictive control (MPC) is an industry standard control technique that iteratively solves an open-loop optimization problem to guide a system towards a desired state or trajectory. Consequently, an accurate forward model of system dynamics is critical for the efficacy of MPC and much recent work has been aimed at the use of neural networks to act as data-driven surrogate models to enable MPC [1]. Perhaps the most common network architecture applied to this task is the recurrent neural network (RNN) due to its natural interpretation as a dynamical system. In this work, we assess the ability of RNN variants to both learn the dynamics of exemplar control systems and serve as surrogate models for MPC. We find that echo state networks (ESN) have a variety of benefits over competing architectures, namely reductions in computational complexity, longer valid prediction times, and reductions in cost of the MPC objective function. The combination of these advantages allowed ESN based MPC to successfully control the lift generated by fluid flow past a cylinder, while assuming access to a fraction of the training data available in previous work [2]. No other considered architecture was successful at this task. Similarly, ESN based MPC consistently controlled the chaotic Lorenz system to a fixed point in a region of phase space that was not explored by the training data. These results support the conclusion that ESNs are better at extrapolating to unseen control and measurement inputs than the other architectures. The relative ease of training ESNs also provides promising future directions for online updates of the surrogate model. [1] F. Bonassi, M. Farina, J. Xie, and R. Scattolini, “On Recurrent Neural Networks for learning-based control: Recent results and ideas for future developments,” Journal of Process Control, vol. 114, pp92-104, June 2022. [2] K. Bieker, S. Peitz, S. L. Brunton, J. N Kutz, and M. Dellnitz, “Deep model predictive flow control with limited sensor data and online learning,” Theoretical and Computational Fluid Dynamics, vol. 34, pp. 577-591, Aug. 2020.