It's hard to think of concrete benefits on the spot when the biggest benefit is the somewhat nebulous "feel" of the language. But generally: array languages typ

It's hard to think of concrete benefits on the spot when the biggest benefit is the somewhat nebulous "feel" of the language. But generally: array languages typically make use of composition of primitives rather than larger, more complicated built in functions. For example in k, there is no 'sort' primitive. Instead of sort, you have grade (<). In APL this is ⍋. In numpy this is np.argsort This means to sort an array you do x@<x, where @ is apply (indexing - for more info on k's indexing read https://beyondloom.com/blog/slicedice.html https://beyondloom.com/blog/slicedice.html) At first this is a bit counterintuitive. But then you realise, x@<x is sort. y@<x is sort by. <<x is 'rank'. There are all these 'recipes' that are based on <, and some of them you may not discover using numpy, as there would be another function that did it for you. You don't typically solve things by composing simple elements, as that would be far too verbose, instead using complicated built in functions, often with many parameters to describe the desired behaviour. I'm sure other people will have other opinions on what the benefits are, but that was just one example off the top of my head.