Thank you for the quick answer.
My use case is as follows:
I have a model that needs to act on variable input sizes. Therefore benchmark mode can not infer a single best option.
I know that the vast majority of cases lie within a small range of input sizes. So I could run a benchmark on the mode of the distribution and thereby get the fasted algorithm for most of the cases.
I would then manually fix the algorithm choice before going to variable-sized inputs.