Neighborhood competition is a critical driver of individual tree growth, and aboveground biomass (AGB) accumulation, which together play key roles in forest dynamics and carbon storage. Therefore, accurate biomass estimation is essential for understanding ecosystem functioning and informing forest management strategies to mitigate climate change. However, integrating neighborhood competition into biomass estimation models, particularly for young mixed forest stands, remains unexplored. In this study, we examined how incorporating neighborhood competition improves biomass prediction accuracy and how the influence of neighborhood competition differs between Scots pine (Pinus sylvestris L.) and Pyrenean oak (Quercus pyrenaica Willd.), as well as the relative contributions of intra- and interspecific competition to AGB. Our findings revealed that including neighborhood competition alongside tree size variables (DBH and total tree height) significantly improved the predictive accuracy of AGB models for Scots pine. This addition reduced the root mean square error (RMSE) by 14% and improved the model efficiency factor (MEF) by 15%. Furthermore, intraspecific competition in Scots pine slightly reduced AGB, whereas interspecific competition had a significant negative effect on AGB. In contrast, DBH alone was the best predictor of AGB for Pyrenean oak, as neighborhood competition did not improve model performance. Also, intra- and interspecific competition in Pyrenean oak had positive but nonsignificant effects on AGB. These findings highlight the important role of competition in biomass models and suggest species-specific approaches in competition dynamics to inform sustainable forest management and climate change adaptation strategies.