Marine forecasts are essential for safe navigation, efficient offshore operations, coastal management, and research, especially in areas with a such harsh conditions as the Arctic Ocean. They require accurate predictions of ocean currents, wind-driven waves, and other oceanic parameters. However, physics-based numerical models, while precise, are computationally demanding. Consequently, data-driven methods, which are less resource-intensive, may offer a more efficient solution for sea state forecasting. This paper presents an analysis and comparison of three data-driven models: our newly developed convLSTM-based MariNet, FourCastNet and the PhydNet, a physics-informed model for video prediction. Using metrics such as RMSE, Bias and Correlation, we demonstrate the areas where our model surpasses the performance of the prominent prediction models. Our model achieves improved accuracy in forecasting ocean dynamics compared to FourCastNet and PhyDNet. We also find that our model requires significantly less training data, computing power, and consequently provides less carbon emmisions. The results suggest that data-driven models should be further explored as a complement to physics-based models for operational marine forecasting. They have the potential to enhance prediction accuracy and efficiency, enabling more responsive and cost-effective forecasting systems.