SEOTRON

AI Content Detector
Analyze text to determine if it was generated by an AI model.

How to Use This Tool

Instructions for max. efficiency:

Run at least 3 tests using each model to get the most realistic picture. Often, the models improve scoring after re-prompting.

Note:

Error margins and model fluctuations may impact conditional scoring, highly skewing the AI probability score. More tests would help reduce the flux impact on the final score.

OpenAI (GPT-4o):Provides advanced, in-depth analysis. Scores may vary as it's highly sensitive to nuanced writing styles
OpenAI (o4-mini):Fast and efficient analysis with good accuracy for most content types.
Open Ai (GPT 5 Mini):Latest compact model with enhanced capabilities and improved efficiency.
Google (Gemini 2.5 Flash):Offers fast, efficient analysis with improved accuracy over previous versions.
Google (Gemini 2.5 Pro):Latest Google model with enhanced reasoning capabilities and improved accuracy.
Detection Results
Analysis results will appear here after processing your content.

Your analysis results will appear here.

Enter text and click "Detect AI Content" to begin.

Tutorials
Watch these short videos to get the most out of the AI Content Detector.

Part 1: How the Standard Analysis works

Learn how the Standard Linguistic analysis works and how to check a blog post that we think is human-written.

Part 2: How the Advanced Analysis works

Learn how the Advanced Behavioral analysis works, when to use it, and how to detect AI patterns.

Prompt Preview
This is the exact prompt sent to the AI model.
Analysis Capabilities Comparison
Comparison of what each AI model can detect in the selected analysis.
Analysis CategorySpecific MetricGPT-4oGPT 5 MiniGemini Flash 2.5Gemini Pro 2.5
Lexical/Sentence-LevelLexical diversity score✅✅✅✅
Sentence length variation✅✅✅✅
Grammatical/StylisticPunctuation diversity index✅✅✅✅
Grammatical error frequency⚠️✅⚠️✅
Filler word usage✅✅✅✅
Repetitiveness score✅✅✅✅
Complexity score✅✅✅✅
Naturalness MarkersNaturalness score✅✅✅✅
Contraction density✅✅✅✅
Colloquialism frequency✅✅⚠️✅
Anaphora usage✅✅✅✅
 How Metrics Are Calculated
An explanation of the parameters used in the analysis.
Lexical Diversity Score
Measures vocabulary variety. AI text tends to have lower diversity, relying on repetitive, high-frequency words.
Sentence Length Variation
Quantifies fluctuation in sentence length. AI text often shows uniform sentence lengths (e.g., 15–25 words).
Punctuation Diversity Index
Evaluates the variety of punctuation. AI-generated content relies mostly on commas and periods.
Grammatical Error Frequency
Tracks natural errors. AI text typically exhibits near-perfect grammar, which can be a strong indicator.
Filler Word Usage
Measures discourse markers. AI tends to overuse formal transitions like "however" and "thus."
Repetitiveness & Complexity
Evaluates redundant phrases and syntactic sophistication. AI can reuse exact phrases and inflate complexity unnaturally.
Naturalness Score
Rates how human-like the writing feels. AI-generated text is often overly coherent, monotone, and lacks a distinctive "voice."
Contraction & Colloquialism
Tracks informal language. AI tends to use contractions sparingly and rarely uses colloquialisms, sounding overly formal.
 AI Probability Calculation Logic
A look into the post-processing logic applied to the AI's raw output.
Detection Criteria: Standard
Focuses on writing style, grammar, and sentence structure. Good for general use, analyzing metrics like lexical diversity, repetitiveness, and naturalness.
Variable Ranges Visualization
Standard Criteria Variable Ranges Chart
Human Range
AI Range
8-Step Calculation Process
  1. Text Input & Model Selection: User provides text content and selects AI model (GPT-4o, o4-mini, Gemini 2.5 Flash/Pro).
  2. Initial AI Analysis: Selected model analyzes text and returns scores (0-100) for 11 linguistic metrics plus flagged sentences.
  3. Initialization: Set base score to 50 (neutral) and detect if text is formal writing (academic/technical keywords).
  4. Metric-Based Adjustments: Apply credit/penalty system based on human vs AI ranges for each metric. Formal writing exemptions apply to certain metrics.
  5. AI-like Sentence Density: Calculate percentage of sentences flagged as AI-like. If <8%: credit (-2.5x), if ≥11%: penalty (+2.5x), 8-10%: neutral.
  6. Confidence-Based Balancing: Calculate confidence from score extremity. If confidence <75, pull score toward neutral (50).
  7. Final Normalization: Clamp and round final score to integer between 0-100.
  8. Return Results: Output AI probability, human probability, confidence level, and detailed analysis metrics.