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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

Knowledge area
Best Practices
Practical token optimization advice
Topic focus
Token efficiency
token-efficiency
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How this is calculated

Concrete efficiency tactics: use concise instructions, keep only examples that demonstrate the needed pattern, and choose native structured output or JSON mode instead of repeating format rules in prose. A system or developer message can organize stable instructions, but those tokens still count. Supported caching may discount an eligible repeated prefix after a cache hit. For multi-turn conversations, remove history only after confirming the task no longer needs it.

Verdict

Reducing unnecessary input tokens lowers the input charge, while output tokens remain a separate part of the bill. A system prompt can organize stable instructions, and prompt caching may discount an eligible repeated prefix after a cache hit. Use concise examples, native structured output where it fits, and keep the conversation history the task needs.

More Tokens scenarios

What are tokens
A token is the atomic unit of text that a language model processes.
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OpenAI vs Llama tokenizer
A sentence that costs 50 tokens on GPT-5 might cost 55 tokens on Llama 4 or 48 on Claude.
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Token limits by model
A model context limit is a token budget, but providers may publish separate input and output limits.
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Related guides

Prompt optimization
Pair token efficiency with prompt optimization to slash cost.
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GPT-5.4 API pricing
GPT-5.4 per-token pricing scaled against efficient prompts.
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Frequently asked questions

Does saying 'please' to an LLM waste tokens?
A little, and it adds up. Politeness padding like 'please' and 'could you please return' costs 5-10 tokens per prompt with zero effect on output quality. Imperative phrasing ('Return a JSON array of...') is shorter and equally clear. At 10,000 daily requests, dropping 50 tokens from each saves half a million tokens a day.
Does whitespace and formatting affect token counts?
Yes, at the margins. Trailing spaces, extra newlines, and decorative indentation all count as tokens on most tokenizers. Pretty-printed JSON sent as a prompt costs noticeably more than its minified form. Clean the input programmatically before sending, since the model doesn't need your formatting to parse the content.
How much conversation history should I keep in the context?
Usually the last 5-10 exchanges. Older turns rarely change the next answer, and every retained message re-bills on every request. For conversations that genuinely need old context, summarize the earlier part into a compact note and carry that instead of the raw transcript. The summary costs a fraction of the tokens.

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