Maxtokens

Maxtokens is a parameter used in natural language processing to specify the maximum number of tokens that can be processed in a single model run. It typically represents the sum of tokens in the input (the prompt) and the tokens that may be generated as output (the model’s continuation).

Tokenization and token counts are central to maxtokens. Tokens are produced by the model’s tokenizer and do not map one-to-one with words or characters; a single word or punctuation can be split into multiple tokens. Different models use different tokenization schemes, so prompts of the same length in characters can occupy different token counts depending on the model.

Usage and implications are driven by maxtokens. The value constrains the context window a model can use. If the prompt consumes many tokens, fewer tokens remain for the generated output, which can lead to truncation or shorter responses. Conversely, a larger maxtokens allows longer generation but increases compute time and cost. In training and inference, maxtokens (or max_tokens) serves different roles: during training, sequences are built up to the maximum length; during inference, maxtokens often controls the maximum length of the generated text.

Model-specific notes indicate that many providers expose a parameter named max_tokens that sets the generation limit, while the model’s overall context limit imposes an upper bound on the total tokens. The effective maximum is the minimum of the model’s context limit and the sum of prompt tokens and generated tokens.