The distribution is conditional on the tokens that came before it. Change an earlier token and the same model produces a different ranking of candidates. This is what it means to call the output a conditional probability: the probability of the next token given the preceding sequence. The model has no fixed answer stored for a prefix; the prefix itself determines which candidates rise.
How ChatGPT Writes One Token at a Time