Single Image Super-Resolution reconstructs high-fidelity images from low-resolution inputs under tight computational budgets, essential in fields like medical diagnostics, remote sensing, surveillance, and aerospace imaging. To ease manual network design, Neural Architecture Search is a key AutoML tool, though its effectiveness hinges on often costly and imperfect performance evaluations. This paper compares two efficient predictors: (i) SynFlow, a zero-cost method, and (ii) an XGBoost regressor, a model-based approach. Both are integrated into NSGA-III, a multi-objective evolutionary algorithm, to explore Super-Resolution architectures optimizing three objectives: maximizing PSNR while minimizing parameter count and floating-point operations. We analyze each method’s impact on final architecture quality. SynFlow offers faster and more reliable estimations compared to XGBoost, which is slower and less accurate. However, XGBoost enables broader approximations of the Pareto front, making it suitable for more comprehensive trade-off exploration. Therefore, we recommend using SynFlow in scenarios where quick and resource-constrained searches are required, while XGBoost is more appropriate when the benefits of a wider trade-off analysis outweigh the additional computational cost.