Free AI content detector that analyzes text to estimate the probability it was generated by ChatGPT, Claude, Gemini, or other AI models. Uses linguistic analysis — runs 100% in your browser.
AI Content Detector analyzes text to estimate the probability that it was generated by an AI language model like ChatGPT, Claude, Gemini, or similar systems. It uses multiple linguistic analysis techniques including sentence uniformity detection, vocabulary richness analysis, transition word density measurement, paragraph structure evaluation, repetitive pattern detection, and word length distribution analysis. Each factor is weighted and combined into an overall AI probability score. The tool runs entirely in your browser — your text is never sent to any server.
No AI detection tool is 100% accurate. This tool uses statistical heuristics and pattern analysis — it does not have access to AI model internals. Results should be used as one data point among many, not as definitive proof. Heavily edited AI text or AI-assisted writing may produce mixed results. The tool requires a minimum of 30 words for meaningful analysis; longer texts produce more reliable results.
Essential terms and definitions related to AI Content Detector.
A measurement of how well a probability model predicts a sample of text. In AI detection, lower perplexity suggests the text is more predictable and more likely to be AI-generated, because language models produce text that their own probability distributions predict well. Human writing tends to have higher perplexity because it contains more surprising word choices, unusual structures, and creative deviations.
A measure of variation in sentence complexity and length throughout a text. Human writing tends to be "bursty" — mixing short, punchy sentences with long, complex ones based on emphasis and rhythm. AI-generated text tends to have low burstiness, producing sentences of more uniform length and complexity. This tool's "Sentence Uniformity" metric measures a related concept.
The ratio of unique words (types) to total words (tokens) in a text. A higher ratio indicates more diverse vocabulary. Human writers typically use a richer, more varied vocabulary than AI models, which tend to favor common, high-probability words. A text with 500 words and 280 unique words has a type-token ratio of 0.56.
A formula that estimates the US school grade level needed to understand a text, based on average sentence length and average syllables per word. AI-generated text often scores in a narrow readability range (8th-12th grade), while human writing varies more widely depending on the author, audience, and purpose.
No AI detection tool is 100% accurate. This tool uses statistical heuristics and pattern analysis — it analyzes sentence uniformity, vocabulary richness, transition word density, paragraph structure, repetitive patterns, and word length distribution. It provides a probability score, not a definitive verdict. Results should be used as one data point among many. Longer texts (200+ words) produce more reliable results than short snippets.
The tool analyzes general patterns common to most large language models, including ChatGPT (GPT-3.5, GPT-4), Claude, Gemini, Llama, and others. All LLMs share certain statistical tendencies — sentence uniformity, transition word overuse, and vocabulary patterns — that this tool detects. However, detection accuracy varies: heavily edited AI text or AI-assisted writing (human + AI collaboration) is harder to detect reliably.
No. All analysis happens entirely in your browser using JavaScript. Your text never leaves your device. This makes the tool safe for analyzing sensitive, proprietary, or confidential content without any privacy concerns.
Statistical analysis requires a sufficient sample size to produce meaningful results. With fewer than 30 words, there are not enough sentences, vocabulary data, and structural patterns to reliably distinguish between AI-generated and human-written text. For best results, provide at least 200 words.
Yes. AI detection tools analyze statistical patterns, and heavily edited AI text (paraphrased, restructured, or mixed with human writing) will score lower on AI probability. This is a fundamental limitation of all AI detection approaches. The tool is most effective on unedited or lightly edited AI output.
Each analysis metric measures a specific linguistic pattern: Sentence Uniformity checks if sentence lengths are unnaturally consistent; Vocabulary Richness measures whether the word choices are diverse; Transition Word Density detects overuse of phrases like "furthermore" and "moreover"; Paragraph Structure checks for uniform paragraph sizes; Repetitive Patterns detects similar sentence openings; Word Length Distribution checks if word lengths are too consistent. Higher percentages indicate more AI-like characteristics.
Common errors developers encounter and how to resolve them.
Score seems inaccurate for clearly AI-generated text If AI text has been heavily edited, paraphrased, or is very short (under 100 words), the detection accuracy decreases. For best results, test longer passages of unedited text. The tool works best with 200+ words of continuous prose.
Human-written text scored high for AI probability Some human writing styles — particularly formal academic writing, technical documentation, and formulaic business writing — share statistical patterns with AI output. The tool provides a probability estimate, not a definitive verdict. Consider the context and use the result as one factor among many.
Analysis shows 50% — what does that mean? A 50% score indicates the text has a roughly equal mix of AI-like and human-like characteristics. This can occur with AI-assisted writing (human editing of AI output), formal writing styles, or short text samples. The tool labels this as "Possibly AI-generated or mixed content." Provide a longer sample or examine the individual metric breakdowns for more insight.