BigO time complexity of 3 nested for loops

Question

I'm debating with a friend whether a particular function I wrote is $O(N^3)$ or $O(N \times M \times X)$

I believe it is the latter since all 3 variables differ in size. $N = 100, M = 50, X = 10000$

for i in range(len(N)):
  for j in range(len(M)):
    for p in range(len(X)):
      if statement:
        count += 1
        list.append(count)

The outer loop executes $N$ times, the inner loop executes $M$ times, and the most inner loop executes $X$. Hence giving $N \times M \times X$. His theory is that because $X$ is so much greater than the other two variables it makes it $O(N^3)$

Answer 1

Obviously you can't say it's $O(N^3)$ because X might grow a lot faster than N. But you can't even say it's O (N x M x X), because you don't know how often the "list.append(count)" is executed and what the time complexity of that operation is.

Answer 2

As you have mentioned that $ N, M, X $ are variables, the time complexity would be $ O(N*M*X) $.
A possible counter argument for the complexity not equal to $ O(N^3) $ is, what if the rate of increase of $ X $ is greater than rate of increase of $ N $ ?