Do you have code to share and look at? All we can do is point to real-world code and benchmarks. For this specific case, see LoopVectorization.jl results (https://juliasimd.github.io/LoopVectorization.jl/latest/exam...), and the corresponding effects on stiff ODE solver benchmarks against C and Fortran packages (https://benchmarks.sciml.ai/html/StiffODE/Hires.html).

I'd don't know what you mean by "performs like" but it's definitely significantly closer in runtime speed after the first run to compiled languages than interpreted (in particular Python/Matlab).

Anyways, my point was more in response to this from the person I responded to: "my instinct is to look for opportunities to vectorize by rewriting loops into matrix notation on paper and then expressing them as array calculations". In some languages (Julia in particular) that is slower than the equivalent loop based code.