For decades, the productivity of tropical montane cloud forests (TMCF) has been assumed to be lower than in tropical lowland forests due to nutrient limitation, lower temperatures, and frequent cloud immersion, although actual estimates of gross primary productivity (GPP) are very scarce. Here, we present the results of a process-based modeling estimate of GPP, using a soil–plant–atmosphere model, of a high elevation Peruvian TMCF. The model was parameterized with field-measured physiological and structural vegetation variables, and driven with meteorological data from the site. Modeled transpiration corroborated well with measured sap flow, and simulated GPP added up to 16.2 ± SE 1.6 Mg C ha−1 y−1. Dry season GPP was significantly lower than wet season GPP, although this difference was 17% and not caused by drought stress. The strongest environmental controls on simulated GPP were variation of photosynthetic active radiation and air temperature (T air). Their relative importance likely varies with elevation and the local prevalence of cloud cover. Photosynthetic parameters (V cmax and J max) and leaf area index were the most important non-environmental controls on GPP. We additionally compared the modeled results with a recent estimate of GPP of the same Peruvian TMCF derived by the summing of ecosystem respiration and net productivity terms, which added up to 26 Mg C ha−1 y−1. Despite the uncertainties in modeling GPP we conclude that at this altitude GPP is, conservatively estimated, 30–40% lower than in lowland rainforest and this difference is driven mostly by cooler temperatures than changes in other parameters.