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Hydrological models are used for various purposes, including flow forecasting, flood forecasting and short and long term water management. However, models suffer from uncertainties from different sources, such as parameterisation, input data errors and process descriptions. The choice of a hydrological model depends on the purpose of its utilisation and data availability. The objectives of this study are, firstly, to investigate the consequences (uncertainty) of using simple versus a complex hydrological model to predict discharge and to quantify the uncertainty of results owing to differences between hydrological models, and secondly, to investigate the effects of using simple versus complex hydrological models in climate change studies. The complexity of each hydrological model is defined based on input data requirements, number of parameters and the level of description of the hydrological processes in the model. Model responses are compared using five hydrological models, ranging from conceptual-lumped to physically-based fully-distributed (HYMOD, HBV, HydroMAD, TopNet WaSiM-ETH) employing data from the Waiokura catchment, in the North Island of New Zealand. Climate change scenarios for three emission scenarios (B1, A1B and A1F1) have been assessed and using the models for three different time periods: “current” (1980–1999), “2040 condition” (2030–2049) and “2090 condition” (2080–2099). It was found that different models need different amounts of input data and time for the set up and the calibration and validation. The analysis of the climate change scenarios shows that the resulting discharges differ significantly between the selected models. This uncertainty indicates a need to carefully choose the model for a given application. Based on the results from the different scenarios, we conclude that a simple to moderately complex model is probably sufficient for most climate change studies; for more realistic results, using a multi-model ensemble is preferable, as it will reduce uncertainty due to model structure and complexity.