The smallest textual units a model reads and writes, such as words or subword pieces is correct because tokens are the atomic pieces of text that generative models consume and produce.

Tokens can be whole words or smaller subword or character pieces depending on the tokenizer used. Token counts are what most generative AI offerings measure for input and output volume so services like Amazon Bedrock commonly scale pricing and quotas with token usage.

The pre-trained parameter values of a foundation model that can later be fine-tuned is incorrect because that phrase describes model weights and parameters rather than the textual units a model processes.

The dense vector representations that capture word or concept meaning is incorrect because that refers to embeddings, which are numeric vectors derived from text and not the raw tokens themselves.

Amazon Comprehend is incorrect because it is an AWS service for natural language processing tasks and it is not a definition of what a token is.

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