Unverified50% confidenceFactExact time
In LLM calls, prompt tokens refer to the number of tokens contained in the input portion sent to the model, including the system prompt, schema summary, historical conversation, and current instruction; completion tokens are the output portion generated by the model.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedLarge model services adopt a Token quota mechanism, where each model call consumes Tokens measured by the input and output text77% similarVerifiedMainstream large model APIs charge separately for input tokens and output tokens, with input tokens typically including the entire conversation history (i.e., the context).75% similarUnverifiedInput token billing for the Decision API typically covers the user's incoming prompt and retrieved recall content, while output tokens correspond to the final result returned by the model.71% similarUnverifiedThe context window is the maximum number of tokens an LLM can process in a single inference, including the system prompt, memory files, conversation history, and the code of the current task, and it is a fixed budget.70% similarPartially VerifiedThe context window refers to the maximum total number of tokens a model can process in a single request, including the input prompt and the model output.69% similar
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