Despite this explosion of DW‐MRI models, a broad comparison on a common dataset and within a common evaluation framework is lacking, so little is understood about which models are more plausible representations or explanations of the signal. Panagiotaki et al.18 established a taxonomy of diffusion compartment models and compared 47 of them using data from the fixed corpus callosum of a rat acquired on a pre‐clinical system. Later, Ferizi et al.39 performed a similar experiment using data from a live human subject, while Ferizi et al.41, 42 explored a different class of models, which aim to capture fibre dispersion. Rokem et al.43 compared two classes of models using cross‐validation and test–retest accuracy. All these studies18, 43, 44 aim to evaluate variations with specific classes of models with all other variables of the parameter estimation pipeline (i.e. noise model, fitting routine, etc.) fixed. While this provides fundamental insight into which compartments are important in compartment models, questions remain about the broader landscape of models; in particular, which classes of models explain the signal best and how strongly performance depends on the choice of parameter‐estimation procedure.