Labs / Computer Science
Big-O Growth Grapher
Every algorithm has a growth rate. Drag the marker left and right to pick an input size n, and read off how many operations each complexity class needs. Toggle curves on and off, or flip the y-axis to a log scale to see slow and fast growth side by side.
n 8n² 642ⁿ 2562ⁿ÷n² ×4
What to try
- At what value of n does O(n²) finally overtake O(n log n) — and does it ever look back?
- Switch to a log y-axis. Why do these curving lines suddenly become nearly straight, and what does a steeper line mean?
- Turn off everything except O(2ⁿ). How far can you push n before the count leaves the top of the chart entirely?
- Compare O(1) and O(log n) at n = 256. Is "logarithmic" really that far from "constant"?
- If a faster computer does 10× more operations per second, which curve does that speed-up help the least?