AI Content Detector
Paste any text to check if it was written by AI. Results from 5 independent AI models.
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Why Multi-Model Voting Solves AI Detection Better Than Any Single Tool
Most AI detectors use a single model to make a judgment. Our approach is fundamentally different:
- Ensemble voting: We ask 5 independent AI models to analyze your text. No single model can be fooled by clever prompting.
- Transparency: You see exactly which models voted "AI" vs "Human" and why.
- Free & unlimited: No signup, no word limits, no monthly caps. Unlike GPTZero (10K/month free) or Originality.ai (no free tier).
- Sentence-level analysis: We show which specific sentences triggered AI detection, not just an overall score.
- Conservative by design: Our system flags only when multiple models agree, reducing false positives that plague single-model detectors.
Frequently Asked Questions
How accurate is this AI detector?
No AI detector is 100% accurate. However, our multi-model voting approach combines 5 independent models, significantly reducing the false positive rate. When 3+ models agree, the accuracy is comparable to enterprise tools like Originality.ai. Use our result as one signal among several, not as definitive proof.
Can it detect ChatGPT, Claude, and Gemini content?
Yes. Our 5 models (Groq Llama 70B, Gemini 2.5 Flash, NVIDIA Llama 70B × 2, SiliconFlow Qwen/DeepSeek) are each capable of recognizing patterns from major AI models. Because they use different underlying architectures, they catch different patterns that a single detector might miss.
Why use multiple models instead of one?
Single-model detectors have a fundamental blind spot: the same kind of AI that generated the text must detect it. This creates a "meta-blindness" problem. Our ensemble approach is like having 5 different experts examine the text independently - if they agree, you can be more confident in the result.
Is this better than GPTZero or Originality.ai?
For casual and professional use, yes in key ways: we're completely free with no limits, we show per-model transparency, and our multi-model voting reduces false positives. Enterprise tools like Originality.ai have more specialized training data, but our approach matches or exceeds them for most common use cases.
What text should NOT be tested with AI detectors?
ESL (English as Second Language) writing, very short text (under 100 words), highly technical writing, and text that has been heavily edited. The Stanford 2024 study found AI detectors flag ESL human writing at 9.6-22.8% false positive rate. Never use AI detectors as the sole basis for decisions affecting real people.
How is this different from other free AI detectors?
Most free detectors (like ZeroGPT) use a single lightweight model with high false positive rates (14%+). Our approach uses 5 production-grade AI models from different providers, with a voting mechanism that requires consensus. We also provide per-model reasoning and sentence highlighting that no free tool offers.