AI Text Detector
GPT Detector Accuracy, Explained
There isn't one accuracy number — here's what actually moves a detector's score.
Why there's no single accuracy number
Any published accuracy figure for a GPT detector is only true for the exact test set it was measured on — a specific model version, a specific mix of topics, a specific amount of editing. Change any of those and the real-world number shifts. Be skeptical of any detector, ours included, that markets a flat 'X% accurate' without qualifying what it was measured against.
False positives and false negatives
A false positive marks genuinely human writing as AI-generated. This happens more often with short passages, technical or formulaic writing, and text from non-native English speakers, whose patterns can statistically resemble model output even though no model was involved.
A false negative misses AI-generated text and calls it human — most commonly with heavily edited or paraphrased AI output, since edits move the text away from the model's original statistical fingerprint.
What actually affects accuracy
Text length matters most: longer passages give a detector more signal, which is why very short scans are less reliable. How much a piece has been edited or rewritten matters next — light edits barely move the needle, heavy rewrites can push AI-originated text toward a human-looking score. Language matters too — detection is generally strongest in English and weaker elsewhere.
Using the score responsibly
Read a GPT detector's output as a confidence range and a set of sentence-level flags, not a single fact. When a decision matters — academic integrity, hiring, editorial trust — use the score to decide where to look closer, and combine it with context you already have about the writer.
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Try the AI Text Detector →Frequently asked questions
Each detector is trained and tuned differently, so their statistical models weigh the same patterns differently. Disagreement between tools is expected, not a sign that one of them is broken.
Yes. Very short passages give a detector less statistical signal to work with, so scores on short text should be trusted less than scores on longer passages.