A practical guide to cutting language-model input costs without harming results
This article frames token optimization as giving a language model only the context it needs, rather than simply shortening prompts. It describes six methods for reducing token counts and discusses savings that do not involve cutting tokens, as well as optimizations that can backfire. The piece also explains what tokens are and emphasizes measuring cost per correctly completed task.
Token optimization focuses on supplying a model with the information required for a correct response, including instructions, prior exchanges, documents, and tool outputs, while avoiding excess material. Because input is billed and cached content may cost less, oversized requests can raise expenses, slow processing, and reduce available context.
Token counts vary by model: English text of 100 tokens is about 75 words, and Claude 4.7 and later models yield roughly 30% more tokens for identical text than earlier versions. Providers separate input, output, cached input, and reasoning tokens; output often costs more, and reasoning tokens are already included in output billing.
Organizations and developers deploying AI assistants could be affected most, since lower token use may reduce operating expenses and make such tools more affordable for smaller teams. Users may benefit from faster responses and lower prices, but careless context trimming could produce errors, retries, and mistrust. The emphasis on cost per correctly completed task may encourage more careful evaluation, though the practical impact will depend on how consistently teams measure outcomes rather than only prompt length.