Benchmarking algorithms is a crucial task to understand them and to make recommendations for which algorithms to use in practice. However, one has to keep in mind that we typically compare only algorithm implementations and that care must be taken when making general statements about an algorithm while implementation details and parameter settings might have a strong impact on the performance. In this paper, we investigate those impacts of initialization, internal parameter setting, and algorithm implementation over different languages for the well-known BFGS algorithm. We must conclude that even in the default setting, the BFGS algorithms in Python's scipy library and in Matlab's fminunc differ widely—with the latter even changing significantly over time.