This study aims to explore the ability of a multi-criteria decision making with analytical hierarchy process (MCDM-AHP) model to emulate the results of a cost benefit analysis (CBA) model in the context of offshore wind farm siting within the Swedish exclusive economic zone (EEZ). The research question addressed is whether the MCDM-AHP analysis produces similar results to the CBA analysis. In addition to this, the strengths and weaknesses of each model is explored. The MCDM-AHP model employs the spatial criteria in a more basic manner compared to the CBA model, simplifying the evaluation process while still explaining 89.5% of the variation in the CBA model and defining similar areas as suitable. Thus, it can be concluded that the MCDM-AHP model adequately emulates the CBA model within the context of offshore wind farm siting within the Swedish EEZ. However, it is crucial to note that the two models produce outputs on different scales. While the CBA model provides levelized cost of energy (LCOE) values that can be thresholded for investment viability comparisons, the suitability score generated by the MCDM-AHP model remains a relative and arbitrary score within the model. Both models entail uncertainties, limiting their usage beyond making general assumptions or identifying areas of interest. The findings reveal that the CBA model demonstrates greater robustness when confronted with changes in spatial input parameters compared to the MCDM-AHP model. This discrepancy is attributed to the iterative computation process and consideration of flat cost inputs in the CBA model, whereas the MCDM-AHP model represents a linear combination of various spatial parameters. However, the calculated LCOE values in the CBA model are highly sensitive to changes in modeling assumptions regarding external parameters, resulting in significant linear variations. The LCOE values obtained from the CBA model baseline case fall within a range of 52.1 - 98.9 EUR/MWh, which aligns with similar studies, validating the CBA model. Nonetheless, caution should be exercised when considering these results as an accurate representation of the real world due to inherent uncertainties in cost inputs and the LCOE measure. The strengths of the MCDM-AHP model lie in its robustness when the order of relative importance remains stable for key spatial evaluators. It is sensitive to significant changes in water depth and wind speed, which heavily influence its output. The model's simplicity allows for a quick overview of the problem, but it requires assumptions that introduce uncertainties. Validation of the MCDM-AHP model using existing and planned offshore wind farms within the Swedish EEZ was possible but limited by the arbitrary scale and limited validation areas. The comparison between the two models could be enhanced with more comprehensive spatial and economic data for an in-depth CBA model, which could serve as a ground truth for the MCDM-AHP model. Nevertheless, the comparison made in this study considers the CBA model to be closer to the truth, acknowledging the underlying assumptions that should be considered during evaluation. In conclusion, within the context of offshore wind farm siting, the MCDM-AHP model produces outputs that are similar to the CBA model.