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If simplex noise covers a larger range, why not figure out the relationship between input->output range (or standard deviation), and apply that as a normalisation to the inputs? I assume the relationship is predictable, and tuning the inputs wouldn't increase computational complexity. The differing ranges make it hard to compare and hard to see the different artifacts.

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Author of the post here. I agree. It's not a big deal. I just wanted to compare the two and document my findings. As I said in the post, Simplex isn't a drop in replacement for Perlin, but it's easy enough to tweak the values to match if that's what you need to do. Most applications of Perlin noise (from what I can see) are more on the creative side of things, and in most cases, someone is just fiddling with some values until something "looks right" anyway.

Indeed, the comparisons seems meaningless to me. Having play a little with Perlin noise I just see samples a different scales/ranges and not anything fundamentally different.

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