An AI score is a reading of how predictable your writing is, printed as a number. It is not the probability that you used AI, and 100% does not mean every word came from a machine. A detector never sees how a document was written. It sees finished text and measures how closely the phrasing matches what a language model would have put in that position. A high number means the writing is unsurprising. Unsurprising writing comes out of machines, and it also comes out of careful editing, second-language instruction, formal registers, and anyone who followed the rubric closely. That is why the same paragraph can read as machine-like on one tool and human on another, and why a score is a reason to look closer rather than a finding on its own. Paste 65 words or more into the checker below and press check to see your own reading: a score from 0 to 100 and one of three verdicts, reads as AI-generated, borderline, or reads as human-written. One check costs one credit whatever the length, against 10 free credits a day per network, with no account, no login, no card and no email address. That flat price exists so that measuring twice, once before an edit and once after, is the normal way to use the tool rather than a luxury.
A detector reads finished prose and nothing else. It has no record of which keys you pressed and no copy of the prompt you may or may not have used. What it measures is how predictable the writing is: how uniform the sentences are, how much the vocabulary stays inside a narrow band, how often the next phrase is the one anyone would guess.
The figure you see sits at the end of a chain of choices. The classifier produces a raw measurement on its own internal scale. The vendor decides the point at which that measurement counts as a flag. Then it gets mapped onto something readable, which is where percentages and coloured bars come from. Different tools label the result differently, and all three labels can look identical on screen while meaning different things, so read the label the vendor puts on its own number before you read the number.
None of this is authorship evidence. A detector has no source document, no drafting history and no chain of custody. It has your prose and a model of ordinary language.
Two questions get collapsed into one. The detector answers: how machine-like is this writing? The reader hears: how likely is it that this person cheated? Moving from the first answer to the second requires two things the score does not contain, namely how often human writing looks like this, and how much of the pile was human to begin with.
Take a stack of essays in which most were written by hand. Even if a detector catches nearly every machine essay and misfires on only a small share of the human ones, the flagged pile still holds human essays, because the human pile was so much larger at the start. That is the shape of the arithmetic, and it is why the false positives page treats a high number as a prompt to look closer rather than a verdict.
The misfires are also not spread evenly. Liang and colleagues at Stanford HAI found in 2023 that 61% of TOEFL essays written by non-native speakers were falsely flagged as AI across seven detectors. Plain, correct, evenly paced English is the most predictable English there is, which is precisely what the measurement rewards.
Nothing standardises these numbers. There is no shared scale, no shared test set, and no shared definition of what counts as a flag. Two detectors run on the same paragraph on the same afternoon are two different models trained on different text, compared against cut-offs that two different companies chose privately. Disagreement is the expected result, not a malfunction in one of them.
The differences that produce the spread:
The checker here returns two things: a number from 0 to 100, where higher means the text reads more machine-like, and one of three verdicts. The three verdicts are "reads as AI-generated", "borderline", and "reads as human-written". The verdict is the reading. The number tells you where you sit inside it and which way an edit moved you.
The score ranks how machine-like the text reads. It is not a probability, which is why it is called a score and never a percentage chance. Reading it as 42% of my essay is AI, or a 42% chance I get accused, imports a meaning it does not carry. Read the direction and the size of the move rather than the digits. A couple of points either way is noise, and borderline means unresolved: it sits between the two readings, in the range where the evidence does not settle the question, so treat it as neither result rather than as a pass.
Two practical notes. The check needs at least 65 words, because a shorter passage gives the measurement too little to read. And a check costs 1 credit whatever the length, against 10 free credits a day per network, so measuring twice, once before an edit and once after, is the normal way to use it.
Because the measurement is predictability, the features that move it are the phrasings a model reaches for by default. The registry of 44 named tells highlights the exact words in your own text rather than handing you a number, and it runs in your browser with no daily limit. Run it alongside the check and you get the diagnosis and the location together.
The edits that reliably shift the reading:
If you would rather have the rewrite done for you, the humanizer rewrites every sentence in one click, up to 12,000 characters a run, at 1 credit per 50 words. On policy: rewriting a draft you were permitted to generate is ordinary editing, and where AI is not permitted, that rule covers the draft however it is edited. Read the policy that applies to you.
Rewriting your own honest draft to satisfy a classifier is not the only move available to you. Check the draft, read the highlighted tells, and see which of your own habits are being scored. In most flagged essays it turns out to be two or three repeated constructions running through the whole piece rather than anything wrong with the argument.
If someone has put a number in front of you, two questions are worth asking before you answer the accusation. What does the vendor say this number is, a confidence or a share of flagged passages? And what threshold turned it red, chosen by whom? A percentage without its cut-off is a colour, not a finding.
The stronger answer is evidence rather than argument. The signed drafting record here keeps a signed history of a document as you write it, using ECDSA P-256, and a third party can verify that signature without contacting us. It works on the essay you are about to write, which is the reason to start it now rather than after the meeting. If the accusation has already landed, the wrongly accused page covers the next steps.
It means the text sat at the far end of that particular tool's scale, at the point the vendor labels as fully machine-like. It does not mean 100% of your document was generated, and it is not a 100% probability that you used AI. The reading is about how predictable your phrasing is, and nothing in it observed how the document was written.
No. A detector has no source document, no drafting history and no chain of custody. It measures writing style and infers from it. A high score is a reasonable prompt to look more closely at a piece of work. It cannot stand in for what you find when you do, and it cannot separate machine text from a careful writer whose prose happens to be predictable.
Because there is no shared scale. Each tool uses a different base model trained on different text, picks its own threshold for a flag, splits the document differently, and maps the result onto 0-100 its own way. Vendors also update their models, so the same text in the same tool can read differently later. Disagreement between tools is the normal outcome rather than a sign that one of them broke.
Aim for the verdict rather than a target figure. Here that means moving out of reads as AI-generated and past borderline, which is the band where the answer is unresolved and should not be read as a pass. Every tool sets its own scale and its own cut-off, so a number chased on one scale does not transfer to another. Fix the phrasing that made the text predictable and the reading follows.
Sixty-five words minimum, because shorter passages do not give the measurement enough to read. Each run costs 1 credit whatever the length, and the free tier is 10 credits a day per network with no account, no login, no card and no email address. The proofreader, word counter, readability checker, AI vocabulary checker, originality check and signed drafting record are free and unlimited, and run in your browser.