Free tool · No sign-up · Runs in your browser
Big-O Complexity Visualiser
Pick your algorithm's time and space classes. The chart plots them against the reference curves (constant, logarithmic, linear, quadratic) on the same axes so the growth rate is obvious at a glance.
Reading the chart
Curves are plotted on a shared axis. Your time class is always included; the rest (O(1), O(log n), O(n), O(n²)) are reference lines so you can eyeball whether your algorithm scales gracefully (closer to the bottom curves) or painfully (closer to quadratic). For large inputs the gap between O(n) and O(n²) becomes the only thing that matters.
Why complexity matters more than the constant
A 100× constant-factor speedup loses to a single complexity-class jump for any non-trivial input. Going from O(n²) to O(n log n) (e.g. switching from bubble sort to merge sort) is the kind of change that turns a 10-second job into a 50-ms one once the input gets past a few thousand elements.
Want a per-line walkthrough?
The chat-version of this tool (@bigO inside EveChat) takes your actual code and walks through the dominant operation in each loop. Useful when you're not sure which class your implementation lands in. Open in EveChat
