LLM Token Optimization: 9 Techniques in the Order to Apply Them

LLM token optimization reduces application costs without sacrificing quality by shrinking sent and generated tokens while using lower-cost mechanisms such as prompt caching, batching, and task-appropriate model routing. Agent workloads are especially expensive because stateless APIs repeatedly resend system prompts, tool definitions, conversation history, and retrieved content, with studies indicating that input tokens account for much of the spending and output tokens typically cost five times more per token. The recommended sequence begins with measuring token use and success rates per task, then reducing retrieved material at the source by returning targeted answers, structured fields, or clean markdown rather than full web pages or raw HTML. Stable prompt prefixes and tool schemas should be organized for cache reuse, while output and reasoning limits, deferred tool loading, removal or summarization of obsolete history, smaller models for simple work, and asynchronous batch APIs can further reduce costs. A worked research-agent example estimates that combining extraction, caching, shorter replies, and batching could reduce a 10,000-task monthly bill from $11,648 to $425, though the text emphasizes that compression, routing, and lossy extraction require evaluation because lower token use can backfire if it increases errors, retries, or human review.

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