Math programming is inherently hard and there will likely always be instances where one solver is faster than another. Often, problems are solved quickly just because some heuristic was "lucky".
Also, the size of a problem alone is not a reliable measure for its difficulty. There are tiny instances that are still unsolved while we can solve instances with millions of constraints in a very short amount of time.
When you're looking for the best performance, you should analyze the solver's behavior by inspecting the log file and then try to adjust parameters accordingly. If you have the opportunity to test out different solvers you should just go for it to have even more options available. You should be careful about recommendations for either of the established, state-of-the-art solvers - especially without hands-on computational experiments.
You also need to consider the difficulty of the modeling environment/language and how much time you might need to finish the modeling part.
To answer your question concerning Gurobi's Python interface: this is a very performant and popular tool for all kinds of applications and is most likely not going to impact the overall solving time. In the majority of cases, the actual solving time is still the dominant factor while the model construction time is negligible.
