Token counts affect whether an LLM feature is affordable, fast, and reliable. Providers usually charge separately for input and output tokens. More input increases prompt-processing work, while more output increases generation time and often costs more per token.
Tokens also determine whether a request fits inside the model's context window. The prompt, conversation history, retrieved documents, tool descriptions, and expected response must all fit. Large requests can fail or lose important content through truncation.
Measure tokens on realistic traffic instead of using one average. Track input, output, cached, and reasoning tokens when the provider exposes them. Common savings include removing repeated instructions, retrieving fewer documents, limiting output length, and routing simple tasks to smaller models.
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