Essentially the same difference between vectors and arrays. A vector is an element of a vector space. A vector can be represented as a coordinate array in a basis, but it is not the coord array. Consider the vector 1 + x + x^2 you might use the canonical basis for polynomials {1, x, xx, xxx, ...} and then the coordinate vector is [1 1 1]. The coordinate and the basis represent the polynomial. But the polynomial isn't a list of numbers.

A matrix is the coefficient list of a linear transformation, but it is not the transformation. The derivative operator is a linear transformation. It's representation as a matrix is just coordinates. But the operator is clearly more than that.

A tensor can be written down and you can compute with the components, but the tensor itself is the basis independent transformation. Not a list of components.

I didn't mean to imply you couldn't have an n-dimensional array in many programming languages. Just that it's usually not what's done when designing programs. I think many would prefer to make some kind of object to encapsulate the data type (an RGB struct for my example) instead of making arrays of arrays of arrays of (double/float/uint8).

For the literal definition between a mathematical 'tensor' and a (e.g.) PyTorch 'tensor' (which, from reading the other comments here, are not the same thing), I leave that to other commenters. I'm just someone learning PyTorch and sharing my knowledge.

The practical engineering difference between a PyTorch tensor and Python array is that PyTorch comes with tons of math & utility functions that support n-dimensions and are differentiable, whereas the builtin Python arrays and math utilities don't do much, if any, of that.