Accurate prediction of wind farm wake interactions is important for energy yield assessment, as offshore wind farms increasingly operate in close proximity to one another. This work presents a systematic comparison of two neural network surrogate models for inter-farm wake deficit prediction: a convolutional neural network (CNN) based attention residual U-Net (ARU-Net) and a graph neural network (GNN) based graph neural operator (GNO). Both architectures are rained using multi-fidelity transfer learning. First, pre-trained on low-fidelity engineering model simulations, and second fine-tuned on high-fidelity Reynolds-averaged Navier-Stokes actuator wind farm (RANS-AWF) data. The models are evaluated on procedurally generated wind farm layouts spanning diverse farm sizes, turbine spacings, wind speeds, and ambient turbulence intensities. Both architectures achieve high prediction accuracy but exhibit complementary strengths: evaluated over the wake region (<em>&delta;</em>_w &ge; 10^&minus;3 m s^&minus;1) and averaged across both evaluation grids, the GNO achieves a lower RMSE (0.024 vs. 0.028 m s^&minus;1), while the ARU-Net attains a higher <em>F</em>₁ score (0.98 vs. 0.91), reflecting its superior wake boundary capture. Transfer learning substantially benefits the ARU-Net, while the GNO shows only marginal improvement.