The perplexity of a language model can be evaluated across different scenarios based on its prediction accuracy. In the best-case scenario, the model perfectly estimates the target token's probability as $1$, resulting in a perplexity of $1$. In the worst-case scenario, the model predicts the target token's probability as $0$, leading to a perplexity of positive infinity. As a baseline, if the model predicts a uniform distribution over all available tokens, the perplexity equals the number of unique tokens in the vocabulary. This baseline provides a nontrivial upper bound that any useful model must beat.