Accurate solar resource assessment is critical for the development of solar energy projects, especially in regions with complex climatic and geographic conditions. This study evaluates the performance of various satellite-based and reanalysis models in estimating global horizontal irradiance (GHI) in Northwestern Argentina, focusing on two locations characterized by different environmental conditions: La Quiaca and Salta. Five satellitebased models (CAMS Heliosat-4, NREL NSRDB, GOES DSR, LSA-SAF MDSSFTD, and GOES G-CIM) and two reanalysis datasets (MERRA-2 and ERA-5) were analysed and compared with high-quality ground-based measurements recorded between 2020 and 2023. The results show that the G-CIM and NSRDB models provide the most accurate irradiance estimates, effectively minimising errors even in challenging environments with extreme altitude or variable terrain reflectivity. At the 10-minute time scale in Salta, the G-CIM model yields a root mean squared deviation (RMSD) of 23.4% and a mean bias of 4.8%, whereas the NSRDB model records an RMSD of 26.6% and a mean bias of –4.2%. In La Quiaca, both models achieve RMSD values below 20% and mean biases under 1%. At the 60-minute scale, in Salta, G-CIM and NSRDB exhibit RMSDs of 20.7% and 19.7%, with corresponding mean biases of 5.4% and –3.6%, respectively, while in La Quiaca they maintain mean biases below 1% and RMSDs of 13.2% for G-CIM and 12.6% for NSRDB. Conversely, the MERRA-2 and ERA-5 reanalysis models showed higher uncertainties, particularly in areas with significant microclimatic variations. The study highlights the importance of using locally validated satellite data for accurate solar resource assessment and emphasises the need for site-specific adjustments when applying global irradiance models. These findings contribute to improved planning and decision-making for solar energy projects in Northwest Argentina and provide valuable insights for researchers, policy makers and industry professionals.