Temperature-dependent crystal viscoplasticity models are ideal for modeling large-grained, directionally solidified Ni-base superalloys but are computationally expensive. This work explores the use of reduced-order models that are potentially more efficient with similar predictive capability of capturing temperature and orientation dependence. First, a transversely isotropic viscoplasticity model is calibrated to a directionally solidified Ni-base superalloy using the response predicted by a crystal viscoplasticity model. The unified macroscale model is capable of capturing isothermal and thermomechanical responses in addition to secondary creep behavior over the temperature range of 20–1050 °C. A second approach is an extreme reduced-order microstructure-sensitive constitutive model that uses an artificial neural network to provide a set of parameters that depend on orientation, temperature, and strain rate to give a first-order approximation of the material response using a simple constitutive model. This simple relationship is then used in a Neuber-type fatigue notch analysis to predict the local response.