Work out big o growth comparison instantly with clear inputs, formula shown and shareable results.
Big-O describes how work scales with input size. Comparing two classes at a concrete n shows the practical gap: the ordering 1 < log n < n < n log n < n² < n³ < 2ⁿ holds for all sufficiently large n.
Growth ordering
O(1) ⊂ O(log n) ⊂ O(n) ⊂ O(n log n) ⊂ O(n²) ⊂ O(n³) ⊂ O(2ⁿ)
About 9966 against 1000000 — the quadratic algorithm does roughly 100 times more work.
Big-O ignores them, but in practice a large constant can make an asymptotically better algorithm slower for small inputs.