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To reduce wasted tokens, first measure what your model counts, then remove input it does not need, limit unnecessary output, and reuse stable context when your provider supports caching. Token counts and billing differ by model and API, so compare equivalent requests using that provider’s usage data—not word counts or a universal savings estimate.
Why token counts are easy to misjudge
Tokens are pieces of text, not words. Counts vary with the model, its encoding, language, and the structure of a request. A plain-text tokenizer can therefore undercount a full API call that includes message structure, tool definitions, schemas, images, or files. OpenAI explains these counting differences in its token documentation; Google documents token counting and usage metadata in its Gemini API token guide.
That distinction matters: you can shorten a prompt in a text editor without reducing the billable input in the way you expect, or overlook generated tokens that do not appear in the final visible answer. Use the exact model and endpoint’s usage fields as the basis for optimization.
Measure before changing the prompt
Record representative requests before optimizing them. Capture input and output tokens, cached-token usage where available, and reasoning or tool-use counts if the provider exposes them. Note whether each request contains tools, schemas, images, files, a long conversation history, or other structured content.
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OpenAI provides usage fields and a complete-input counting API; Google documents ways to count input and conversation tokens and inspect usage metadata. Check the documentation for the specific API you use, because fields and accounting differ. Compare equivalent tasks, then change one factor at a time so you can tell whether token use, latency, or answer quality actually changed.
Cut input that does not help answer the task
Remove duplication and irrelevant history
Delete repeated instructions, stale conversation turns, and retrieved passages unrelated to the current question. If instructions already appear in a stable system or developer message, avoid repeating them in every user message unless the endpoint or workflow requires it.
Send only the relevant parts of source material
Do not include a whole document when the model needs only a section. Filter retrieved text to the question, preprocess or summarize material that need not be quoted verbatim, or split a large job into focused chunks. OpenAI’s latency optimization guidance recommends shortening inputs, removing repeated context, dividing large inputs, and summarizing or preprocessing.
Keep the evidence and qualifications the answer needs. After trimming, check that the response remains correct and complete; a shorter context that drops a crucial exception is not an optimization.
Reduce avoidable output without making answers unusable
Tell the model what form the answer should take and how much detail is useful: for example, a concise summary, a short list, or a fixed set of fields. Clear output requirements can prevent long introductions, repeated explanations, and irrelevant alternatives. For structured output, use compact field names and syntax where doing so does not impair parsing or maintainability.
Set the endpoint or model’s output-token limit as a guardrail, not as a replacement for a clear request. A limit can truncate a useful answer, so allow enough room for the task. Reported output usage may include generated tokens that are not visible in the final text, such as formatting or tool-call tokens; leave headroom if the visible response must meet a minimum length.
Reuse repeated context with caching
If many requests share a large instruction block or reference text, keep that common prefix stable and place frequently changing content later when the provider’s cache behavior supports it. Caching can reduce charges for repeated input; it does not make the logical prompt shorter.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteOpenAI and Google both document caching mechanisms, but eligibility, minimum sizes, controls, and billing vary by provider and model. Google describes implicit caching as providing no guarantee of cost savings, while explicit caching is configurable and its costs depend on cached tokens and storage duration. See OpenAI’s prompt-caching guide and Google’s context-caching guide. Verify cache hits in usage metadata rather than assuming that a request was cached.
Best Value
Choose the reduction that fits your workload
| Method | What it changes | Best fit | What to verify |
|---|---|---|---|
| Trim or filter input | Input tokens | Prompts with repeated instructions, long histories, or irrelevant source passages | Answer quality and input usage for the same task |
| Summarize or chunk source material | Input tokens per request; may also change how work is divided | Long material that need not be passed verbatim in one call | Whether important details survive preprocessing |
| Specify concise output and set a cap | Generated output tokens | Responses that routinely include unnecessary explanation or alternatives | Truncation, visible completeness, and output usage |
| Cache stable context | Cost or accounting for repeated input, depending on provider | Repeated requests with substantial shared context | Cache reads or hits, cache writes, and provider-specific billing |
These methods affect different parts of usage and are not interchangeable. A shorter prompt is not automatically better if it removes necessary context; a cache can lower repeated-input charges while leaving the prompt’s token count unchanged.
Track cost and latency separately
Token reduction and latency reduction are related but not identical goals. OpenAI’s latency optimization guide offers workload-dependent heuristics: cutting output tokens by 50% may cut latency by about 50%, while cutting prompt tokens by 50% may produce only a 1–5% latency improvement. These are latency estimates, not universal promises about API-cost savings or answer quality.
For each change, track input tokens, output tokens, cache reads and writes, latency, and whether the result still meets the task. There is no universal percentage of token or API-cost savings established for prompt cleanup; results depend on the model, API, request, pricing, and caching behavior in use.
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