Big-O Time Complexity Calculator
Operations vs. n for common Big-O classes: O(1), O(log n), O(n), O(n log n), O(n²), O(2ⁿ).
Result
How to use this calculator
- Enter input size n.
- Pick complexity class.
- Read estimated operations + wall-clock time.
About this calculator
Big-O notation describes algorithm growth rate as input size increases. O(1) — constant (hash lookup). O(log n) — binary search. O(n) — linear scan. O(n log n) — quicksort/mergesort, optimal comparison sort. O(n²) — bubble/selection sort, naive nested loop. O(2ⁿ) — naive recursive Fibonacci, brute-force subset. O(n!) — TSP brute force, factors quickly become impossible. Modern CPUs do ~10⁹ simple ops/sec; an O(n²) algorithm hits a wall at n ≈ 100,000.
Frequently asked
Why log base 2?+
Big-O hides constants?+
Best vs. worst case?+
When does O(2ⁿ) become impossible?+
Is O(n!) ever practical?+
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