Estimate operation counts and runtime for a complexity class at a given input size.
Complexity classes describe how cost grows rather than absolute speed. Comparing runtime at n and at ten times n shows the practical difference: linear grows tenfold while quadratic grows a hundredfold. An algorithm that is fast at small input can be unusable at scale, which is why growth rate matters more than constant factors once data volumes grow.
Big O Time Complexity
Operations = f(n) for the complexity class; runtime = operations × time per operation
Operations = f(n) for the complexity class; runtime = operations × time per operation Complexity classes describe how cost grows rather than absolute speed. Comparing runtime at n and at ten times n shows the practical difference: linear grows tenfold while quadratic grows a hundredfold.
An algorithm that is fast at small input can be unusable at scale, which is why growth rate matters more than constant factors once data volumes grow.
This calculator takes 3 inputs: Input size n, Complexity class, Nanoseconds per operation. The pre-filled defaults are a realistic starting point — replace them with figures from your own environment for a result you can act on.