How AI Detectors Actually Work
AI detectors don't have a secret database of ChatGPT outputs to compare against. They measure two statistical properties that distinguish LLM output from human writing:
Perplexity — how surprising each word choice is. Language models choose words based on probability: they always pick a statistically likely next word. Human writers take risks — unusual phrases, unexpected word choices, sentences that don't "flow" perfectly. Low perplexity in a document is a strong AI signal.
Burstiness — how much sentence length varies. Human writing oscillates between short punchy sentences and long complex ones. LLMs produce text with very even sentence length because each sentence is generated under similar statistical constraints. Low burstiness is the other key AI signal.
Modern detectors combine these measurements with machine learning trained on large corpora of known-human and known-AI text. The result is a probability score — not a binary yes/no, but a 0–100% estimate.
Accuracy in 2026: What You Can Trust
The highest-risk false positive scenario is formal, structured human writing — technical documentation, legal text, and writing by non-native English speakers who tend to use simpler, more regular sentence patterns. These can score 25–35% AI on some detectors despite being entirely human-written.
The practical upshot: A score above 70% is a very strong AI signal on unedited text. A score of 20–50% is ambiguous and should be treated as "possible AI involvement, not confirmed." A score below 15% on general text is effectively human.
Using Forgely's Free AI Detector
Forgely's AI Detector requires no account and has no word limit per check. Here's what it's good for:
- Instructors — screening student submissions before flagging for review (always use as one signal among many, not as proof)
- Content managers — spot-checking freelance submissions or content agency output
- Writers using AI tools — checking your own polished AI-assisted text before submitting to confirm it reads as human
- Publishers and editors — initial triage of inbound pitches and guest posts
Important: AI detection results should never be the sole basis for an academic integrity decision. They are a signal that warrants closer investigation, not proof. False positives exist. Always combine detector results with your own reading of the work.
Detect AI writing for free — no account needed
Paste any text and get an AI probability score in seconds.
Try AI Detector Free →Can Detectors Identify Which AI Model Wrote Something?
Not reliably, no. Some tools market "GPT-4 detection" or "Claude detection" as distinct features, but in practice modern detectors detect the class of output (LLM-generated text), not the specific model. ChatGPT-4, Claude 3, Gemini Ultra, and Llama 3 all share the same fundamental statistical properties — high probability word choices, low burstiness. The statistical signatures are more similar than different.
Any tool claiming 95%+ accuracy at identifying the specific model is overstating what the underlying technique can deliver.
What AI Detectors Can't Do
- Detect AI-assisted writing where a human substantially edited the output
- Prove academic dishonesty (they indicate probability, not intent)
- Detect AI writing accurately in languages other than English (most are English-optimized)
- Work reliably on texts under 100 words (too little signal)
- Differentiate between a skilled writer who sounds clear and AI that sounds clear
Frequently Asked Questions
AI detectors are accurate on unmodified LLM output — typically 80–95% detection rate on raw ChatGPT, Claude, or Gemini text. Accuracy drops significantly if the text has been manually edited or run through a humanizer tool. No detector is 100% reliable, so results should be treated as a strong signal, not a definitive verdict.
Yes. Forgely's AI Detector is completely free with no signup required. Paste any text and get a probability score indicating how likely it is to be AI-generated, with no word limit per check.
Yes. Modern AI detectors don't fingerprint individual models — they detect statistical properties shared by all LLMs, including low perplexity and low burstiness. These properties are present in ChatGPT, Claude, Gemini, Llama, and any other transformer-based model.
A false positive is when the detector flags human-written text as AI-generated. This happens most often with highly structured, formal writing — technical documentation, legal text, and writing by non-native English speakers who tend toward simpler, more regular sentence patterns.