This paper addresses the question of how well we should expect a land surface model to perform. A statistically-based artificial neural network is used as a de facto land surface model and its results used to benchmark the performance of a traditional physically-based land surface model. This provides us with a measure of land surface model performance relative to the information contained in the meteorological forcing about the surface fluxes. Further, it is a benchmark that is independent of the measure of model performance. The technique is used to benchmark three models at three observational sites, with results showing that for the most part, the models under-utilise the information available to them. This suggests that there are considerable opportunities for model improvement.