Before writing a single guide at Pastagi, we ran a humbling benchmark: six free AI-text detectors against progressively paraphrased model output.
The setup
Take 100 GPT-generated paragraphs. Pass each through a light paraphrase (synonym swaps, sentence reordering). Ask the detectors to classify both versions. No fine-tuning, no tricks - the same text with its surface scrambled.
The results that stung
Detectors that scored 90%+ on raw model output dropped 20-40 points after paraphrasing. One popular tool flipped to calling paraphrased human text "AI" more often than actual paraphrased AI text. The lesson isn't "detectors are bad" - it's that detector scores are meaningless without knowing the surface-form robustness of the specific detector.
What we do differently now
Every generation feature we ship gets an eval harness with three suites: raw output, paraphrased output, and adversarial near-duplicates. Regression means the harness fails, not vibes. And RAG answers must carry citations or they don't ship at all - attribution does more for trust than any detector.
The full benchmark methodology is on the site. Reproduce it before you buy any detector's enterprise tier.
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