In this work, a cutting-edge full implementation of task-based programming paradigms applied to a complete particle-in-cell code is shown. The core of the implementation has been based on the algorithm of the particle-in-cell code Smilei (https://smileipic.github.io/Smilei/), although a complete code has been built up from scratch. The task-based model has been implemented in the particle-in-cell code throughout the backend OpenMP and the library Eventify, being the last one currently developed at the Jülich Supercomputing Centre. Four physical studies were selected as benchmarks: thermal plasma, plasma beam diffusion, a laser colliding with a plasma beam sphere, and thermal plasma with an imbalanced artificial operator. Besides, different parametric studies were designed to measure the scalability of the implementation at different numbers of cores and different physical conditions. To measure the performance scalability under different architectures, the current parametric studies were performed in an Intel cascade lake-based machine and an AMD EPYC-based machine. Results show a significant gain in performance in the task-based implementation compared to the classical omp for implementation when a high load-imbalance benchmark is presented.