Accounting for the variability of processes and climate conditions between catchments and within catchments remains a challenge in hydrological modelling. To address this issue, various approaches were developed over the past decades. Among them, multi-model approaches provide a way to quantify and reduce the uncertainty linked to the choice of model structure, and semi-distributed approaches propose a good compromise to account for spatial variability of the processes by dividing the catchment in sub-catchments while maintaining a limited level of complexity. However, these two approaches were barely applied together. The aim of this work is to answer the following question: can we improve the efficiency of hydrological models by implementing a multi-model approach within a semi-distributed framework? In this work, the benchmark considered is a lumped model with a fixed structure. To this end, a large set of 147 catchments in France was assembled, with precipitation, evapotranspiration and flow data at an hourly time step over the 1998-2018 period. The semi-distribution set-up was kept simple by considering a single intermediate catchment between a downstream station and one or more upstream catchments. The multi-model approach was implemented with two versions of the GR model (namely GR4H and GR5H). Within a semi-distributed framework, the two models were either used individually, i.e. applied on all sub-catchments (called GR4H-SD and GR5H-SD respectively), or combined using a simple and a weighted mean. The first step of this work was to check whether past conclusions published in the scientific literature, obtained with lumped multi-models, were the same in a semi-distributed framework. In other words, does the multi-model approach generate better performance than individual models in a semi-distributed context? Another possible combination of the semi-distributed and the multi-model approaches would be to make different choices of model structures or combinations on each sub-catchment. Intuitively, it makes sense to propagate the flow simulated by the best model from upstream to downstream. The second analysis therefore focuses on the following question: is the best upstream model always the most useful downstream? The results and the operational implications of this work will be analyzed in the case of the Rhône basin.