The cost of using large language models is primarily determined by the shape of the input and output tokens, rather than the specific model chosen. Even the cheapest models like GPT-5.4 Nano can become expensive if output lengths are not carefully managed. Factors such as retries and unused context also contribute significantly to costs, often being overlooked in basic token-count estimations. Understanding and optimizing token shape is crucial before considering model choice, as the difference between models is often a fixed multiplier. AI
IMPACT Optimizing token shape and managing hidden costs like retries and unused context can significantly reduce LLM operational expenses.
RANK_REASON Analysis of LLM costs focusing on token shape rather than model choice.
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