Compare two complexity classes to find the input size where the better one wins.
Big-O hides constant factors, which is why a theoretically worse algorithm often wins on small input. Insertion sort beating quicksort below about a dozen elements is the classic example, and real sort implementations exploit exactly that. Choosing an algorithm requires knowing the expected input size, not just the asymptotic class.
Algorithm Complexity
Operations = constant factor × growth function of n, compared across the two algorithms
Operations = constant factor × growth function of n, compared across the two algorithms Big-O hides constant factors, which is why a theoretically worse algorithm often wins on small input. Insertion sort beating quicksort below about a dozen elements is the classic example, and real sort implementations exploit exactly that.
Choosing an algorithm requires knowing the expected input size, not just the asymptotic class.
This calculator takes 5 inputs: Input size n, Algorithm A complexity, Algorithm B complexity, Constant factor of A, Constant factor of B. The pre-filled defaults are a realistic starting point — replace them with figures from your own environment for a result you can act on.