About AI Text Detector -- Free AI Writing Checker
Free AI text detector that analyzes writing patterns, sentence structure, and statistical signals to estimate whether content was written by a human or generated by AI.
How to use
- Paste or type the text you want to analyze into the input area. For the most reliable results, use passages of at least 200 words -- shorter samples lack enough statistical signal for confident detection. You can analyze essays, blog posts, emails, student submissions, or any prose content.
- Click the Analyze button to start the detection process. The tool examines multiple linguistic dimensions simultaneously, including perplexity (how predictable the word choices are), burstiness (variation in sentence complexity), vocabulary richness, and syntactic diversity. This multi-signal approach reduces the chance of a single misleading indicator skewing results.
- Review the overall human vs. AI probability score displayed as a percentage. This score represents a confidence estimate, not a binary verdict. Scores near 50% indicate the text has mixed signals and should be interpreted cautiously rather than treated as definitive.
- Check the detailed breakdown of individual writing pattern indicators below the main score. Each indicator shows where the text falls on the human-to-AI spectrum for that specific dimension. Look for patterns -- if most indicators lean one direction, the overall assessment is more trustworthy.
- Use the results as one data point in your evaluation process, not as the sole judge. Combine AI detection with your own reading of the text, knowledge of the author, and contextual clues. Heavily edited AI text, highly technical writing, and non-native English writing can all produce misleading scores.
Frequently asked questions
How does an AI text detector work?
AI text detectors analyze statistical patterns that differ systematically between human and machine-generated writing. The key signals include perplexity (how surprising the word choices are -- AI tends toward highly predictable sequences), burstiness (humans naturally vary sentence length and complexity far more than AI does), vocabulary diversity, and the distribution of common phrases. Large language models optimize for coherence and fluency, which ironically makes their output statistically smoother and more uniform than typical human prose. No single signal is conclusive, so effective detectors combine multiple indicators into a weighted confidence score.
Is AI text detection accurate?
No AI detector achieves 100% accuracy, and you should treat any tool claiming otherwise with skepticism. Detection reliability depends heavily on three factors: text length (longer passages provide more statistical signal), how much the text has been edited after generation, and the writing style itself. Unedited AI output of 500+ words can be detected with reasonable confidence. However, heavily revised AI text, formulaic human writing (like legal documents or technical specs), and text from non-native speakers can produce false positives or negatives. Use detection results as supporting evidence alongside your own judgment, not as a definitive ruling.
Can this detect ChatGPT and Claude content?
This tool analyzes general statistical patterns common to most large language models, including ChatGPT, Claude, Gemini, Llama, and Mistral. It does not identify which specific model was used, because all transformer-based models share similar output characteristics -- predictable token distributions, uniform sentence structure, and consistent register. The detection approach is model-agnostic, meaning it works on current and future AI models that share these fundamental statistical properties. That said, as models improve at mimicking human writing variability, detection becomes harder for all tools.
Is my text stored or shared?
No. All analysis runs entirely in your browser using client-side JavaScript. Your text is never sent to any server, never stored in a database, and never shared with third parties. This makes it safe to analyze confidential documents, student work, proprietary content, or any sensitive material. You can verify this by checking your browser's network tab -- no requests are made during analysis.
What types of text work best for AI detection?
The tool works best on prose-style content: essays, articles, blog posts, reports, and email drafts. Creative writing, poetry, and highly structured formats like resumes or bullet-point lists produce less reliable results because they don't follow standard prose patterns. For best accuracy, analyze text that is at least 200 words long. Very short passages (under 100 words) simply don't contain enough data for meaningful statistical analysis. If you need to check a short passage, try analyzing it alongside surrounding context for a more complete picture.
Can AI-generated text be made undetectable?
Substantial human editing can reduce detection confidence significantly. When someone rewrites sentences, varies paragraph structure, adds personal anecdotes, and introduces deliberate stylistic choices, the statistical fingerprints of AI generation become diluted. This is actually the intended use case -- AI as a drafting assistant with heavy human revision produces text that genuinely is a human-AI collaboration. Pure unedited AI output remains the most detectable, while text that has been meaningfully rewritten reflects the human author's voice and naturally reads as more human-generated.
How is this different from paid AI detectors?
Paid AI detectors like Turnitin, GPTZero, and Originality.ai use server-side machine learning models trained on large datasets of confirmed human and AI text. This free tool uses statistical heuristics that run entirely in your browser -- no server calls, no API limits, no subscription. The tradeoff is that trained ML models can pick up subtler patterns, while heuristic approaches are faster, more private, and fully transparent in how they reach their conclusions. For casual checking and quick assessments, browser-based detection is practical and immediate. For high-stakes academic integrity decisions, consider using multiple tools and human review together.
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