Analyze text for AI-generated patterns using perplexity, burstiness, and linguistic forensics. Detect ChatGPT, Claude, Gemini, and other LLM outputs.
Our detector analyzes perplexity (word predictability), burstiness (sentence variation), and repetition patterns to distinguish human writing from machine-generated text. No data is stored on our servers.
Measures how predictable your text is to a language model. AI chooses statistically likely words, producing low perplexity. Human writing surprises the model with creative, unpredictable choices.
Calculates variation in sentence length and complexity. Humans naturally mix short punchy sentences with long elaborate ones. AI tends toward uniform, medium-length sentences with consistent rhythm.
Analyzes vocabulary diversity and word repetition. AI models often reuse the same phrases and structures. Human writing shows greater lexical diversity and less formulaic patterning.
Our detector analyzes text using three core metrics: perplexity (how predictable the word choices are), burstiness (variation in sentence length and complexity), and repetition patterns. AI-generated text typically shows low perplexity, low burstiness, and higher repetition compared to human writing.
Perplexity measures how surprised a language model would be by the text. Human writing uses unexpected word choices and creative phrasing, resulting in higher perplexity. AI models choose the most statistically likely next word, producing lower perplexity scores.
Burstiness measures the variation in sentence length and structural complexity throughout a text. Human writing naturally mixes short punchy sentences with long complex ones, while AI-generated text tends toward uniform sentence lengths. We calculate burstiness as the coefficient of variation of sentence word counts.
Yes. Our detector identifies statistical signatures common to all major LLMs including GPT-4o, Claude, Gemini, and Llama. While each model has subtle differences, they all share the core characteristics of low perplexity, low burstiness, and predictable structural patterns that our algorithm detects.
Our statistical detection method achieves strong accuracy on texts over 100 words, with performance improving significantly on longer documents (300+ words). Results should be used as one indicator among many, not as definitive proof. No AI detector is 100% accurate, and all can produce false positives or false negatives.
Important: This tool uses statistical linguistic analysis to estimate the probability of AI authorship. Results are not definitive proof and should be interpreted as one data point among many. False positives can occur with technical writing, ESL text, or highly structured content. No text data is stored on our servers - all analysis happens in real-time during your session.
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