Time complexity calculator

Paste code and get an AI-assisted breakdown of runtime growth, loop nesting, recursion, and Big O behavior.

Useful if you are searching for a time complexity calc, Big O calculator, runtime complexity analyzer, or code complexity calculator.

The Lab

your_code

def bubble_sort(arr):
    n = len(arr)
    for i in range(n):
        for j in range(0, n-i-1):
            if arr[j] > arr[j+1]:
                arr[j], arr[j+1] = arr[j+1], arr[j]
    return arr

def bubble_sort(arr):
    n = len(arr)
    for i in range(n):
        for j in range(0, n-i-1):
            if arr[j] > arr[j+1]:
                arr[j], arr[j+1] = arr[j+1], arr[j]
    return arr

Push it. I dare you.

The Story

"Your code is a blank page. Let's write the story."

How This Time Complexity Calculator Works

Paste code, inspect loops and recursion, and turn runtime growth into a readable Big O explanation.

Paste Code

Use Python, JavaScript, Java, C, or C++ snippets that you want to estimate quickly.

Trace Loops And Recursion

The calculator checks iteration depth, repeated work, divide-and-conquer patterns, and built-in operations.

Get Big O

Read the estimated runtime class, then follow the explanation to see why the growth rate fits.

PythonJavaScriptJavaCC++

This page is designed to match tool intent. If someone searches for a Big O calculator, time complexity calc, or runtime complexity analyzer, the goal is to answer that need directly on this URL.

How To Calculate Time Complexity Read The Big O Guide

Common Big O Classes

These are the runtime growth patterns a time complexity calculator usually maps to when it sees loops, recursion, and divide-and-conquer structure.

O(1)

Constant

Array access, hash lookup

O(log n)

Logarithmic

Binary searching

O(n)

Linear

Single loop, linear search

O(n log n)

Linearithmic

Merge sort, quick sort

O(n²)

Quadratic

Nested loops, bubble sort

O(2ⁿ)

Exponential

Recursive Fibonacci

The Free Time Complexity Calculator

Estimate runtime growth, compare Big O classes, and understand why the code scales the way it does.

Time Complexity Analyzer

Instantly analyze O(N), O(log N), O(N²) and more patterns in your code. Get Big O notation with AI explanations.

Space Complexity Calculator

Calculate auxiliary space and memory usage of your algorithms. Analyze O notation with AI-powered insights.

AI-Powered Insights

Get natural language explanations of complexity patterns. Understand algorithm efficiency in plain English.

Master Big O Notation

Master algorithm complexity with 16+ interactive tutorials. From linear search to dynamic programming.

Why Choose TimeComplexityAI?

Feature	TimeComplexityAI	Others
AI-Powered Explanations		---
Space Complexity Labs		---
16+ Deep-Dive Tutorials		---
Step-by-Step Breakdowns		---
Free & Clean UI		---

Learn The Patterns

Go beyond the calculator. These deep-dive guides connect theory to the lines of code you write every day.

Tutorials

Crawlable guides on binary search, merge sort, graphs, and dynamic programming.

Browse Guides

Math Lab

Use the step-by-step analyzer to understand where each complexity term comes from.

Enter Lab

The Blog

Read focused explainers on Big O notation, Bubble Sort, and Merge Sort theory.

Read Articles

Time Complexity Calculator FAQ

Answers to common questions about Big O, runtime growth, and using this time complexity calculator.

What does this time complexity calculator analyze?

It estimates how runtime grows with input size by looking at loops, nested loops, recursion, and common operations in your code.

Is this also a Big O calculator?

Yes. If you searched for a Big O calculator or time complexity calc, this page is designed for that exact use case.

Can I use this time complexity calc for Python, JavaScript, Java, C, and C++?

Yes. The interface is built around those languages so you can estimate runtime complexity across common interview and coursework code.