Free ChatGPT Detector
Check whether a text was generated by ChatGPT (GPT-3.5 or GPT-4). AIDetego uses 16 independent analysis modules that detect typical ChatGPT patterns – entirely local in your browser, without sending your text to any server.
How Does AIDetego Detect ChatGPT Text?
ChatGPT-generated text has characteristic patterns that differ from human writing. ChatGPT tends to produce uniform sentence lengths, predictable word sequences, and specific vocabulary distributions. Our 16 modules systematically analyze these features:
- Sentence Length Variance – ChatGPT writes noticeably uniform sentences. Humans naturally vary more.
- Pseudo-Perplexity – ChatGPT text is highly predictable – a core signal for AI detection.
- Phrase Detection – 750+ typical ChatGPT phrases like 'It's important to note' or 'In today's digital landscape'.
- N-Gram Analysis – Detects typical ChatGPT word sequences that occur statistically more often than in human text.
Which ChatGPT Versions Are Detected?
AIDetego detects text from all common ChatGPT versions:
- ChatGPT-3.5 – The free version with characteristically formal patterns
- ChatGPT-4 – Writes more naturally but still shows detectable statistical patterns
- ChatGPT-4o – The latest version, detectable through entropy and variance analysis
Who Uses a ChatGPT Detector?
- Teachers & Professors – Check submitted papers for ChatGPT usage
- Content Managers – Quality control of purchased content
- Journalists – Verification of sources and texts
- Students – Check own texts before submission
Frequently Asked Questions
Is the ChatGPT detector free?
Yes, AIDetego is 100% free, with no sign-up and no limits.
How accurate is ChatGPT detection?
AIDetego uses 16 independent modules for a comprehensive analysis. Results are probability estimates – no AI detector can guarantee 100% accuracy.
Are my texts stored?
No. The entire analysis runs locally in your browser. No text leaves your device.
Can ChatGPT-4 also be detected?
Yes. Although GPT-4 writes more naturally, it still shows statistical patterns that our modules can detect – particularly through pseudo-perplexity and Zipf distribution.