Free AI detectors compared: how they differ and how to read them

'Which free detector is best' has no stable answer: they update constantly and disagree by wide margins. What's useful is understanding how they differ, so you can read any of them sensibly.

What they measure

Some free detectors are primarily perplexity-based (how predictable your text is), some are trained classifiers, and most commercial ones combine approaches even on their free tiers. Perplexity tools are transparent but easily fooled by formal writing; classifiers can be sharper but go stale and carry their training data's biases. Two 'free AI detectors' can therefore give you very different numbers on the same paragraph, because they're computing different things.

How much text they need, and how they threshold

Almost all detectors are unreliable on short text: under a couple of hundred words there simply isn't enough signal. And each vendor chooses where to draw the 'likely AI' line, trading false positives against false negatives, and moves it without telling you. So a '72% AI' from one tool and a '30% AI' from another aren't contradictory; they're two different scales with two different thresholds.

Read a free score as a rough gauge, not a measurement. If three free tools disagree, that disagreement is the finding: the text is ambiguous, which is common for edited or non-native writing.

Using them well

Run more than one, on a decent length of text, and treat convergence as more meaningful than any single number. Use the score to find uniform, templated passages and improve them. The durable win is writing that reads as human to a person, which is also what tends to lower the estimate across tools. In antigpt, the in-app detector plays this role inside the edit loop; on paid plans a second provider gives you that convergence check in one place.

What no free (or paid) detector can do is guarantee an outcome. Anyone claiming a tool makes text 'undetectable' is selling a snapshot as if it were a law.