AI Detector for Code: Can You Tell If Copilot or Claude Wrote It?

AI now writes a large share of new code, from Copilot autocompletions to Claude refactors. That raises a question developers, reviewers, and CS instructors keep asking in 2026: is there a detector that can tell machine-generated code from human-written code? The short answer is yes, but the tools and the limits are different from text detection.

GPTZero Code Mode: the detector you already use, now for code

GPTZero extended its natural-language detector to source code, which matters because most teams already reach for it on prose. 2026 roundups put its code accuracy around 92% overall, covering six languages with a free tier and an academic focus, which makes it the easiest on-ramp if you are already in the GPTZero ecosystem.

The honest framing is that code mode is one signal, not a verdict. Like its text counterpart it highlights the passages it flags, so a reviewer can see which functions tripped the model rather than arguing with a single percentage. Treat that percentage as a prompt to look closer, not as proof.

Originality.ai Code Mode: detection inside the content pipeline

Originality.ai ships a Code Mode that scores AI-generated code the same way its text engine scores documents. Independent 2026 benchmark matrices put it lower on code than on prose, roughly 70 to 81% across models, with Claude near 79%, GPT-4 near 81%, and Copilot near 70%, at about 1.5 cents per check.

The useful part is context: because Originality.ai is built for a publishing pipeline, a team can run the same AI score on a blog post and on a code snippet in one dashboard. The tradeoff is the same as elsewhere in the category, its own benchmark claims are higher than what third parties measure, so verify against a second tool before acting.

Copyleaks CodeLeaks: the multilingual code module

Copyleaks bundles a code-specific module, CodeLeaks, inside the same dashboard as its text plagiarism tool. That single-pane coverage is the draw: a university or business that already runs Copyleaks on documents can add code detection without a second vendor, across more than 40 languages and with LMS integrations.

Pricing starts around $13.99 a month for the broader platform. For teams that need both text and code integrity in one place it is the pragmatic pick, though the code accuracy figures vendors quote should be read as upper bounds rather than guarantees, since purpose-built code detectors still sit well below the 90% plus numbers some marketing pages imply.

GLTR: see the predictability behind code

GLTR, the MIT and Harvard visualizer, applies the same idea to code that it applies to prose: it shows how predictable each token is against a model's top predictions. On code that predictability runs higher than on natural language, because programming languages are intentionally structured, so GLTR's code scores land only around 55 to 62% in benchmark tests.

That low number is not a flaw, it is the lesson. Code is by design more uniform than writing, so a model's 'most likely next token' is closer to what a human would also write. GLTR is best understood as a teaching instrument that shows why code attribution is hard, not as a tool to accuse a developer.

The free fallback, and why to read it with caution

ZeroGPT remains the most-searched free checker and needs no signup, which makes it the casual first stop for a quick scan. Applied to code, though, it inherits the weakness of every general text detector: the perplexity and burstiness signals that work on essays do not transfer to source code, where predictability is a feature, not a tell.

Expect higher false positives on boilerplate. Independent testing shows tutorial-style and CRUD code gets misflagged 20 to 28% of the time because humans and models converge on the same templated patterns. Use a free tool for a ballpark read, never for a ruling on someone's work.

For students: pair the score with process evidence

Code-only detection is the weakest instrument an educator has. A submission that trips GPTZero Code Mode might just be clean, conventionally formatted student work, and a cleverly edited AI draft can slip past every scorer. The signals that actually hold up are process signals: commit history with real false starts, a verbal walkthrough the author can defend, and in-class components on related material.

So use GPTZero Code Mode as one input among several, never as automatic proof. Require draft history, hold a short conversation about the approach, and reserve the score for pointing investigation in the right direction rather than settling the question.

Bottom line

Yes, you can flag AI-generated code, but purpose-built detectors like GPTZero Code Mode, Originality.ai Code Mode, and Copyleaks CodeLeaks do the job far better than general text checkers such as ZeroGPT. None are definitive, especially on short or boilerplate snippets, and for students process evidence beats any score. Use a code detector as one signal, never as proof.