---
title: "Token Probability — How AI Detectors Analyze Word Choices"
description: "Token probability is the likelihood a language model assigns to each word in a sequence. AI detectors use high token probabilities as evidence of machine generation."
author: "Khadin Akbar"
last_updated: "2026-03-15"
secondary_keywords: ["token probability", "ai token prediction", "word probability ai", "next token probability"]
---

# Token Probability

**Token probability** refers to the likelihood that a language model assigns to a specific word (token) appearing at a given position in a text sequence. AI detectors analyze token probabilities to determine whether text was generated by an AI model.

## How Token Probability Relates to AI Detection

When an AI model generates text, it selects tokens with high probability — words that statistically fit the context. This creates a pattern:

- **AI-generated text** contains mostly high-probability tokens (the model selected them precisely because they were likely)
- **Human-written text** contains a natural mix of high and low-probability tokens (humans make unexpected word choices, use rare vocabulary, and break patterns)

## Token Probability and Perplexity

Token probability is the foundation of [perplexity scoring](/glossary/perplexity-score). Perplexity is calculated from the average token probabilities across a text:

- High average token probability → Low perplexity → More likely AI
- Mixed token probabilities → Higher perplexity → More likely human

## How Detectors Use Token Probabilities

AI detectors run the submitted text through a reference language model and calculate probabilities for each token. They then analyze:

1. The average probability across all tokens
2. The distribution of probabilities (variance)
3. Whether the probability pattern matches known AI generation signatures
4. Runs of consistently high-probability tokens

## FAQ

**Q: Can you see the token probabilities for your AI-generated text?**
A: Some AI APIs (like OpenAI's) provide logprobs (log probabilities) for generated tokens. These show exactly how "surprised" the model was at each word choice.

**Q: How do humanizers affect token probabilities?**
A: [AI humanizers](/) introduce tokens that a model would assign lower probability to — making the overall distribution look more like human writing without changing the text's meaning.