Why this is O(1)
Why O(1). Two array accesses by index plus a comparison. None of them depend on the length of words — they're all single pointer-arithmetic operations on the underlying buffer.
Common trip-up. People sometimes count .length as a traversal. It's not — JS arrays store length as a property, looked up in constant time.
How to recognize constant complexity in the wild
O(1) means the runtime doesn't grow with the input at all. Dictionary lookups, array indexing, single-line arithmetic — the input could be ten or ten million and the work is the same.
The eight rungs of the ladder
Big-O classifies the asymptotic growth of a function, not the wall-clock time. The ladder Bugdle uses has eight rungs ordered from cheapest to most ruinous: O(1), O(log n), O(n), O(n log n), O(n²), O(n³), O(2ⁿ), O(n!). The puzzle's answer sits at rung 1 of 8. The four-guess budget plus higher/lower hints means the puzzle is solvable in at most ⌈log₂(8)⌉ = 3 perfectly-read guesses; the fourth attempt absorbs misreads of the snippet.
Common confusables
Nested loops aren't automatically quadratic — the inner loop has to range over n, not a constant. A loop that always runs ten iterations is still O(1) work per outer iteration. Similarly, a recursive function isn't automatically exponential just because it calls itself twice — if the recursion has overlapping subproblems and you memoise, you collapse back to polynomial. Always ask: what does n really mean here, and how many distinct subproblems are there?
External reference: Big O notation — Wikipedia.