Copyleaks AI Checker
Copyleaks is the detector aimed at publishers and agencies rather than classrooms, and the one with the widest language coverage in the category. It is also where the field's accuracy argument is at its most circular: Copyleaks reports up to 99.12%, and a study published by a competitor reports it misclassifying roughly one human document in twenty. This page sets out both numbers, who published each, and how the tool reads output from every major assistant. A free sentence-level check of our own is below.
What the Copyleaks AI checker is actually built for.
It is usually compared against GPTZero and Turnitin, and it is aimed at a different reader from either, which explains most of the differences.
- Publishing and enterprise before educationGPTZero went to teachers first and Turnitin arrived through universities. Copyleaks came at it from the content side: publishers checking commissioned work, agencies checking freelancers, enterprises checking what goes out under their name. That changes the default question from who wrote this essay to whether we can publish this, which is a different standard of proof.
- The widest language coverage in the categoryOver thirty languages, which is more than any of its main competitors offer and the single clearest reason to reach for it over GPTZero. Coverage is not the same as parity, and the section below is specific about the gap, but for anything outside English it is usually the more serious option.
- Built around text that mixes human and AI writingIts pitch is finding generated content even when carefully blended into human writing, which is the realistic case rather than the textbook one. Almost nothing that arrives at an editor is wholly one thing. Whether it delivers on that is exactly what the accuracy argument below is about.
- It grew out of plagiarism detectionCopyleaks was a plagiarism checker before AI detection existed, and both still ship together. Useful, and a source of constant confusion: a similarity score matches your text against documents that already exist, and an AI score looks at your text alone. A document can score zero on one and high on the other.
How accurate is Copyleaks? Two answers, and who published each.
This is the most useful thing on the page, because the honest answer is not a percentage. It is an account of where the percentages come from.
Copyleaks reports up to 99.12% accuracy and emphasises a low false-positive rate, with the specific claim that it finds generated content even when carefully blended into human writing. That figure comes from Copyleaks.
A comparative study reports it misclassifying about one human document in twenty, a rate that would be unusable in a classroom or an editorial process. That study was published by GPTZero, which came out ahead in it.
Meanwhile Originality.ai publishes a review of Copyleaks. And Copyleaks publishes comparisons of its own. Follow the citations in this field for ten minutes and you end up in a closed loop where every evaluation of a detector was written by a detector.
None of that means anyone is lying. Vendors test on the data they have and report what they find, and the differences between test sets are enough to produce honestly held, wildly different numbers. What it means is that a headline accuracy figure in this category tells you what the publisher measured, not what will happen to your document.
And accuracy is two numbers, only one of which gets advertised. Catching generated text and not flagging human text are separate problems with wildly different costs. Any tool can catch almost everything by being aggressive, and pay for it in honest work flagged. The false-positive rate is the number that matters if you are the one being checked, and it is the number least likely to appear in a headline. This is why we publish no accuracy figure of our own: we would be adding one more self-reported number to a pile that is already the problem.
How ChatGPT, Gemini, DeepSeek, Grok, LLaMA and Perplexity text reads to Copyleaks.
Written for the commissioning case rather than the classroom one, since that is who mostly runs this check.
ChatGPT Copyleaks checker
The default case, and the one every detector handles best because it has seen the most of it.
ChatGPT prose dominates the training data behind all of these tools, so it is the output they recognise most confidently. What carries the signal is the shape: a restated opening, evenly weighted body paragraphs, a closing that summarises rather than concludes. Uniform paragraph length across a piece is visible to the measurement and to any editor reading it.
For commissioned content specifically, this is where the blending question bites. A freelancer who generated a draft and then rewrote the openings produces exactly the mixed document Copyleaks markets itself on, and exactly the document where any single percentage is least informative.
Gemini Copyleaks checker
Gemini output has a structural habit that interacts badly with how any detector reads a document.
It reaches for headings and bullets where prose was asked for. Short fragmented list items give a statistical measurement very little to work with, so heavily structured submissions return a less confident read than the same content written out as continuous paragraphs.
For published content this matters twice over, because the listy structure that weakens the score is also the structure that makes the piece read as generated to a human editor. A clean number on a bulleted document is close to meaningless.
DeepSeek Copyleaks checker
DeepSeek leaves evidence that no detector is needed to find, and a scoring problem that no detector handles well.
Reasoning-model output frequently still carries the reasoning: a preamble planning the answer, or an opening paragraph discussing how to approach the question. In commissioned work this is the most common giveaway of all, because the writer pasted without reading.
The prose itself runs technical whatever the brief asked for, and technical registers are regular by convention. Expect DeepSeek drafts to score high, and expect genuine technical writing to score high alongside them for the same reason.
Grok Copyleaks checker
Grok is the input most likely to produce a confusing result, and the reason cuts both ways.
Its register is more informal and more varied than the other assistants, which raises the surprise of individual word choices and can pull a document score down. What stays regular is the placement: an aside at intervals, a consistent conversational tone, jokes arriving on schedule. A pattern in where the personality lands is still a pattern.
Text copied out of X also carries the post formatter's spacing and short standalone lines, which fragments the passage and makes any statistical read noisier than it should be.
LLaMA Copyleaks checker
The case where a confident number deserves the least confidence.
LLaMA is weights rather than a product. What reaches you depends on the application serving it, the system prompt in front of it, and how heavily it was quantised, so two products running the same model name produce differently shaped prose. A detector calibrated on one deployment generalises poorly to another.
That variance is why a low score on suspected LLaMA output should not settle anything. It may mean the text is human, and it may mean the deployment producing it was not represented in training.
Perplexity Copyleaks checker
Perplexity output arrives shaped by citations, which survives their removal in a way worth reading for.
Every claim is hedged toward a source and every sentence is built to accommodate a bracket. Strip the markers before pasting, as almost everyone does, and the hedging remains — sentences leaning on an authority that is no longer named anywhere in the document. Regular in construction, and that regularity is what raises a score.
For commissioned content there is a second problem the detector does not touch. A piece built on sources it no longer cites is a factual liability regardless of what any AI score says about it.
Thirty-plus languages, and what that does and does not mean.
Language coverage is the clearest reason to choose Copyleaks over GPTZero, and it is worth being precise about what coverage buys, because supporting a language and performing well in it are different claims.
Detection quality tracks training data. The measurement depends on a model that has read enough of a language to know what is predictable in it. That makes results in widely written European languages considerably more trustworthy than results in languages with less text online, and the interface gives you no indication of which you are looking at.
The false-positive problem gets worse, not better, outside English. Languages with more formal written registers — German being the standard example, with its longer sentences and heavier subordination as a matter of convention — produce prose that is regular for reasons of grammar and custom rather than authorship. The measurement has no way to discount for that.
Translated text is its own category. Anything that has been through machine translation reads as unusually regular whatever produced the original, because translation systems optimise for the same predictability detectors measure. A human-written document translated by software can score higher than a generated document written directly in English.
So thirty languages is a real advantage and it is an advantage in availability rather than in reliability. For a publisher working across markets it may be the deciding factor. For a decision about one person’s work in a language the model knows thinly, it should carry less weight than it appears to.
Copyleaks AI checker questions.
How accurate is the Copyleaks AI checker?
Copyleaks publishes over 99% accuracy, specifically up to 99.12%, and a low false-positive rate. A comparative study published by GPTZero reports that Copyleaks misclassified roughly one in twenty human-written documents, which would be far too high for classroom or editorial use. Both numbers are published by companies with an interest in the answer, which is the honest summary of the evidence available.
Is the Copyleaks AI detector free?
There is a free tier for checking a limited amount of text, with paid plans above it aimed at organisations rather than individuals. That is consistent with who it is built for: publishers, agencies and enterprises checking volume, rather than a student checking one essay.
Is this page Copyleaks?
No, and there is no affiliation. What you can run here is our own sentence-level AI check, which marks the exact phrases that read as AI-written and names why, rather than returning a statistical score. If a Copyleaks score is specifically what you need, their own free tier will give you one directly.
How many languages does Copyleaks support?
Over thirty, which is the widest coverage among the major detectors and its clearest advantage over GPTZero. Coverage is not parity: detection quality tracks how much of a language the underlying models have read, so results outside English deserve more caution than the interface suggests.
Can Copyleaks detect AI text mixed into human writing?
That is its central claim, and the mixed document is the realistic case rather than the exception. It is also the hardest case for any detector, because a statistical measurement needs volume and a two-paragraph insertion gives it very little. Treat a result on a blended document as a pointer to where to read closely, not as a finding.
Is a Copyleaks AI score the same as its plagiarism score?
No, and conflating them causes real trouble. Plagiarism detection matches your text against documents that already exist. AI detection looks at your text alone and asks how predictable it is. They ship in one product and measure nothing in common: original work can score high for AI, and copied work can score zero.
Can it detect ChatGPT, Claude, Gemini, DeepSeek and Grok text?
It reads all of them, because the measurement does not depend on which model produced the words. Confidence varies: ChatGPT is the most recognisable because it dominates the training data, Grok scores unpredictably because its register is more varied, and LLaMA is least reliable because one model name covers many different deployments.
Do hidden characters or em dashes affect a Copyleaks AI score?
No. The AI check reads the words and their predictability. It does not count punctuation, read file metadata, or look for zero-width characters. Cleaning those is worth doing for its own reasons and none of it moves a score.
A client flagged my writing with Copyleaks. What should I do?
Ask which score they are looking at, because the similarity number and the AI number get confused constantly and the conversation is different for each. Then show the process rather than arguing the percentage: version history, dated drafts, notes, research links. Commissioned work that was genuinely written leaves a trail, and that trail settles the question faster than a counter-analysis will.
Is my text stored?
The document is attached to your account so you can return to it, and you can delete it whenever you like. It is not used to train anything.