How do people choose between a smaller reward available sooner and a larger reward available later? Past research has evaluated models of intertemporal choice by measuring goodness of fit or identifying which decision‐making anomalies they can accommodate. An alternative criterion for model quality, which is partly antithetical to these standard criteria, is predictive accuracy. We used cross‐validation to examine how well 10 models of intertemporal choice could predict behaviour in a 100‐trial binary‐decision task. Many models achieved the apparent ceiling of 85% accuracy, even with smaller training sets. When noise was added to the training set, however, a simple logistic‐regression model we call the difference model performed particularly well. In many situations, between‐model differences in predictive accuracy may be small, contrary to long‐standing controversy over the modelling question in research on intertemporal choice, but the simplicity and robustness of the difference model recommend it to future use.