Token efficiency: how to say more with fewer tokens and cut your LLM bill
Token efficiency compounds. Saving 50 tokens per request across 10,000 daily requests saves 500,000 tokens per day, which is real money at scale. Write for the model the way you'd write for a busy colleague: clear, direct, no fluff.
Token efficiency is about maximizing the intelligence you get per token you spend. Wasted tokens are wasted money. A verbose system prompt with 500 tokens of polite filler costs the same as 500 tokens of detailed instructions, and the latter gets better results. The most common token wastes: redundant instructions repeated across messages, politeness padding, examples that are longer than necessary, and including entire documents when only a section is relevant.
By TechCompare · Updated
How this is calculated
Concrete efficiency tactics: use imperative mood ('Return a JSON array of...') instead of polite requests ('Could you please return...'), which saves 5-10 tokens per prompt with no quality loss. Put static instructions in the system prompt where they can be cached. Use the shortest example that demonstrates the pattern. For structured output, use the API's native structured output or JSON mode instead of describing the format in the prompt. Trim trailing whitespace, which some tokenizers count as tokens. For multi-turn conversations, prune the history aggressively: models rarely need more than the last 5-10 exchanges for context.
Verdict
The savings stack because every wasted token is wasted spend. Imperative phrasing like 'Return a JSON array of...' beats 'Could you please return...' by 5-10 tokens per prompt with no quality hit, and trimming trailing whitespace matters because some tokenizers count it. Static instructions belong in the cacheable system prompt, and multi-turn chats rarely need more than the last 5-10 exchanges. Native structured output beats describing the format in prose.
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Frequently asked questions
Does saying 'please' to an LLM waste tokens?
Does whitespace and formatting affect token counts?
How much conversation history should I keep in the context?
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