The compatibility between various GPU programming models and vendors is complex, largely due to the growing number of choices involving GPU platforms, programming models, and languages. As recently analyzed by Herten , while OpenMP is natively supported across the three major platforms-AMD, Intel, and Nvidia-and works with both C++ and Fortran, other popular models like OpenACC have limited support on Intel GPUs. This underscores the importance of thoroughly assessing a programming model's level of support on a chosen platform before beginning code development. Additionally, standardized benchmarks and evaluation tools are needed to effectively compare the performance and capabilities of different programming models. The lack of standardization in GPU programming has also led to a variety of community-driven, higher-level models that aim to abstract away vendor-specific details and provide a more portable programming experience. Examples of such models include Kokkos , RAJA , Alpaka , and, to a lesser extent, hipSYCL which is limited to AMD and Intel. These abstraction models often utilize vendornative infrastructure in the background, enabling developers to write code that can be deployed on multiple platforms without significant code changes. However, the support and standardization of these higher-level models can vary significantly, and relying on community-driven efforts for critical software infrastructures can introduce uncertainties and complexities in the long run. Comparing the performance of various GPU programming models on the LUMI supercomputer reveals the potential of community-driven models like Kokkos in achieving portable performance but also highlights the need for further development and community support to ensure their long-term viability.