AI Content Detector API
Our AI Content Detector API offers rapid and accurate detection across 26 languages by using 4 models.
Sample Usage
model
can be: fast-detect-advanced
, fast-detect-basic
, glimpse-premium
, glimpse-standard
Response
Detection Models
Fast Detect Advanced
Uses larger parameter models (7B)
for both sampling and scoring. Offers the highest accuracy among fast detection methods while maintaining good speed. Recommended for general-purpose detection when balance between accuracy and speed is needed.
Fast Detect Basic
Employs smaller parameter models (2.7B)
for both sampling and scoring. Faster processing time but slightly lower accuracy. Good for quick initial assessments and situations where computational resources are limited.
Glimpse Premium
Top-tier multilingual detection supporting 26
languages. Uses the most powerful models for scoring text across multiple languages. Highest accuracy but more computationally intensive. Best for critical content verification.
Glimpse Standard
Balanced multilingual detection supporting 26
languages. Uses efficient models to provide good accuracy across languages. Less resource-intensive than Premium while maintaining solid detection capabilities. Suitable for routine multilingual content verification.
Detection Metrics
Probability
Score between 0-1
indicating likelihood of AI generation. Values above 0.5 suggest AI authorship, while values below 0.5 suggest human authorship.
Criticality
Confidence metric for the detection. Higher values indicate stronger statistical evidence for the classification made by the detector.
Token Count
Number of text chunks analyzed, providing context for detection reliability. Longer texts generally provide more reliable detection results.
How to Interpret Results
Probability > 0.8: Strong indication of AI-generated content
Probability 0.6-0.8: Moderate indication of AI-generated content
Probability 0.4-0.6: Ambiguous, could be either AI or human
Probability < 0.4: Likely human-written content
For most accurate results, consider using multiple detection methods and comparing their outputs.
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