AI detection tools compared — what actually works
AI detectors are everywhere now. GPTZero, Originality.ai, Turnitin, ZeroGPT — each claims to spot AI-written text with high accuracy. The reality is messier. False positives, inconsistent results, and an arms race between generators and detectors that neither side is winning.
Here's what each detector does well, where it fails, and what the accuracy numbers actually mean.
The detector landscape at a glance
| Detector | Best for | Claimed accuracy | Real-world accuracy* | False positive rate | Price |
|---|---|---|---|---|---|
| Originality.ai | Professional publishers, agencies | 99% | ~85-90% | ~2-5% | $14.95/mo |
| GPTZero | Education, teachers | 98% | ~75-85% | ~5-10% | Free / $9.99/mo |
| Turnitin AI Detection | Universities, academic integrity | 98% | ~80-85% | ~1-4% (claimed <1%) | Institutional only |
| ZeroGPT | Casual checking, free use | 98% | ~60-75% | ~10-20% | Free |
| Sapling | Business, CRM integration | 97% | ~70-80% | ~5-10% | $25/mo |
| Copyleaks | Enterprise, LMS integration | 99% | ~80-90% | ~2-5% | $10.99/mo |
*Real-world accuracy based on independent testing, not vendor claims. Your results will vary depending on the AI model used, humanization applied, and text type.
What the accuracy numbers don't tell you
AI models keep changing
Every time OpenAI, Anthropic, or Google releases a new model, detector accuracy drops. The detectors need to retrain on new AI output patterns. There's always a gap — weeks or months where new AI text flies under the radar. Humanized text widens that gap further.
Human writing gets flagged
This is the scariest part of the detector landscape: false positives. Students accused of cheating because their writing style is "too consistent." Professional writers flagged because they use clear structure. Non-native English speakers disproportionately affected because their writing patterns overlap with AI patterns.
Originality.ai and Turnitin have the lowest false positive rates (around 2% and 1-4% respectively), but even 2% means one in 50 human-written papers gets incorrectly flagged. At university scale, that's hundreds of false accusations per semester.
Humanized text is harder to detect
Run raw ChatGPT output through any detector and you'll get 95-100% AI probability. Run it through a good humanizer first and those numbers drop to 10-40%. The same detectors that confidently flag raw AI output become uncertain with humanized text.
This isn't a bug in the detectors — it's the whole point of humanization. Varied sentence structure, natural imperfections, human voice patterns: these are the things detectors look for as evidence of human authorship. A good humanizer adds them deliberately.
How our built-in detector compares
Our tool uses a hybrid approach: client-side heuristics (perplexity proxy, burstiness, pattern matching) for instant scoring, plus server-side AI analysis for deeper detection. It's not as comprehensive as Originality.ai (which is a dedicated detection service) but it's integrated into the humanization workflow — you see the score change in real time.
For casual use, it tells you what you need to know: is this text likely to get flagged? For high-stakes situations (academic submission, publishing), run the output through multiple dedicated detectors to be safe.
Practical advice
- Don't rely on one detector. Run text through at least two (Originality.ai + GPTZero is a good combo).
- Scores above 80% are usually real. Scores between 20-60% are uncertain territory — could be AI, could be human.
- Detectors are getting better, but so are generators. This is an arms race. What passes today might not pass in six months.
- The safest approach: Use AI as a drafting tool, humanize the output, add your own expertise and voice. The combination of AI speed + human judgment is harder to detect — and produces better content anyway.
Also: Browse 8 writing styles for humanizing AI text · How to humanize AI text — the complete guide