GPUs and other accelerators are available on many different devices, while GPGPU has been massively adopted by the HPC research community. Although a plethora of libraries and applications providing GPU support are available, the need of implementing new algorithms from scratch, or adapting sequential programs to accelerators, will always exist. Writing CUDA or OpenCL codes, although an easier task than using their predecessors, is not trivial. Obtaining performance is even harder, as it requires deep understanding of the underlying architecture. Some efforts have been directed toward the automatic code generation for GPU devices, with different results. In particular, several directive-oriented programming models, taking advantage of the OpenMP success, have been created. Although future OpenMP releases will integrate accelerators into the standard, tools are needed in the meantime. In this work, we present a comparison between three directive-based programming models: hiCUDA, PGI Accelerator and OpenACC, using for the last our novel accULL implementation. With this comparison, we aim to showcase the evolution of the directive-based programming models and how users can guide tools toward better performance results.