AI Code Detector

What is an AI code detector?

An AI code detector is a tool that estimates whether a piece of source code was written by a person or generated by an AI coding assistant. Since tools like ChatGPT, Claude, Gemini, GitHub Copilot and Cursor became part of everyday programming, a large share of new code starts life as an AI suggestion. That is often fine, and often very useful. But there are situations where you need to know where code came from: a programming assignment that is meant to test a student's own understanding, a take-home interview task, or a pull request from a contributor whose work you are reviewing.

This free AI code detector gives you a quick, readable answer: a verdict (likely AI-generated, likely human-written, or mixed), an AI probability from 0 to 100%, and a few short reasons explaining what the analysis noticed.

How this AI code detector works

When you click Detect, your snippet is sent to a large language model (Google Gemini) together with instructions to act as a careful reviewer. Instead of looking for a hidden watermark, which most AI tools do not add to code, the model looks at the same stylistic signals an experienced engineer would notice:

• Comment style. AI code often has tidy, evenly spaced comments that explain the obvious, such as "# Initialize the list". Human comments tend to be sparse, uneven, or focused on the strange parts.
• Naming uniformity. Assistants favor consistent, descriptive, textbook names everywhere. People mix styles, abbreviate, and leave a tmp2 behind.
• Boilerplate patterns. Defensive checks for every input, generic error messages, and a full if __name__ == "__main__" demo block are common in generated code.
• Formatting and structure. Perfectly consistent formatting with no dead code, no TODOs and no leftover debugging can point toward AI.
• Idiosyncrasies. Typos, unusual shortcuts, project-specific hacks and personal habits usually point toward a human author.

The model weighs these signals and returns a probability plus the strongest reasons. We ask it to stay calibrated, so short or generic snippets get moderate scores rather than confident guesses.

How accurate is it? An honest answer

No AI code detector is 100% accurate, and you should be suspicious of any tool that claims to be. Code is far more constrained than prose: there are only so many reasonable ways to reverse a string or call an API, so a clean human solution and an AI solution can look almost identical. Accuracy is best on longer, complete functions or files with comments and some personality. It drops on very short snippets, on heavily standardized code (for example, code formatted by a linter and written to a strict style guide), and on AI output that a person has edited.

False positives happen too. Experienced developers who write clean, well-commented code can sometimes look "too perfect". For that reason, use the result as one signal alongside other evidence: a conversation with the author, the commit history, or a short follow-up question about how the code works.

Which AI tools can it flag?

The detector looks for general patterns, not for one product, so it can flag code typical of all the major assistants: ChatGPT, Claude, Gemini, GitHub Copilot and Cursor, as well as other tools built on similar models. It estimates whether the code looks AI-written; it does not reliably say which tool produced it, because these assistants increasingly share the same habits.

Who uses an AI code detector?

• Teachers and teaching assistants checking programming homework, where the goal is to see whether a student can solve the problem on their own.
• Interviewers and recruiters reviewing take-home tests and coding challenges, as a prompt for follow-up questions rather than an automatic rejection.
• Code reviewers and maintainers who want to know when a pull request may need extra scrutiny for subtle bugs, licensing questions, or code the author may not fully understand.
• Developers who are simply curious how "AI-like" their own style looks.

Tips for the best results

Paste a complete function or a meaningful section of a file (up to 8,000 characters), keep the original comments, and avoid running it through a formatter first. If you are checking a large project, test several independent pieces and look at the overall pattern rather than a single score.

Frequently asked questions