Nested cross-validation generalization error for multiple models
In general you are right and in this answer it has been done as far as I see. The models are compared to each other while the best tuning of them is found, both inside the loop. It looks fine.

About your point, yes. But the point in Machine learning is that at some point we need to stop/limit our attempts as the number of algorithms which can do the task are very large. We usually try to evaluate different families of algorithms and then narrow the search from there but at the end we can never claim that the best answer we found is necessarily the best possible answer. In another POV, this is the main idea behind many research papers in ML. They just creatively find/modify an algorithm and show that it works better than previously applied algorithm through a benchmark dataset.