Big O Algorithm Complexity Comparator
Compare operation counts for common Big O complexity classes at a given input size n.
Results
What is it?
For informational/educational purposes only. Big O notation describes how an algorithm's runtime or space requirements grow as the input size increases. Comparing complexity classes helps you choose the most efficient algorithm for your use case.
How to use
Enter an input size n (e.g., 1,000 items in an array). The calculator shows the approximate number of operations each major complexity class would require at that size, making the differences stark and intuitive.
Example scenario
For n = 1,000: O(1) = 1 op, O(log n) = ~10 ops, O(n) = 1,000 ops, O(n log n) = 9,966 ops, O(n�) = 1,000,000 ops. Switching from O(n�) to O(n log n) is a 100� improvement at this scale.
Pro tip
Big O ignores constant factors and lower-order terms. For small n, an O(n�) algorithm with small constants may outperform an O(n log n) one. Always benchmark with realistic data. Space complexity matters as much as time complexity in memory-constrained environments.