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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesTo reduce wasted tokens, first measure what your exact model and API count, then remove input that does no useful work, request only the output you need, and reuse stable context where caching is supported. Token counts and billing differ across providers, so check usage and answer quality after each change rather than assuming a shorter prompt is automatically cheaper or better.
Measure token use before changing prompts
Tokens are not words. Counts vary with the model, tokenizer, language, and request structure. A plain-text tokenizer can miss parts of a full API request, including message structure, tool definitions, schemas, images, and files. Start with representative requests and record input tokens, output tokens, and cached-token usage where available; inspect reasoning or tool-use fields if the provider exposes them.
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For OpenAI, the token-counting guide explains tokenization and request counting; its usage fields let you compare actual calls. Google documents token counting and usage metadata in its tokens guide. Use the provider’s own counts for the model and endpoint you call, and include the same tools, schemas, files, and conversation history that production requests contain.
Build a useful baseline
- Choose a small set of typical tasks, including requests with long histories or structured inputs if those occur in your application.
- Record input, output, and cached-token usage for each request, along with latency and whether the response met the task.
- Change one thing at a time and compare equivalent tasks. Otherwise, it is difficult to tell whether a reduction came from the prompt change or a different request.
Remove input that does no work
Repeated instructions, irrelevant conversation history, and retrieved passages unrelated to the question all consume input tokens. Delete duplicated material and filter reference content to what the model needs. If only a portion of a long document matters, preprocess it, summarize it, or split the work into focused chunks instead of repeatedly sending everything verbatim.
#1 Best Overall
Keep details that affect correctness: names, constraints, definitions, exceptions, and evidence the model must use. After trimming or summarizing, compare the response with the original request to make sure the missing context did not change the result. OpenAI’s prompt-engineering guidance discusses shortening prompts, removing repeated context, and summarizing or preprocessing large inputs.
Constrain output to what the task needs
Specify a useful response shape and length: for example, a brief summary, a short list, or a fixed set of fields. Set the endpoint or model’s output-token limit as a guardrail, not as a replacement for a clear instruction. For structured responses, use compact field names and syntax when doing so will not make the result harder to interpret or parse.
Do not set the cap so low that a useful answer is routinely cut off. Provider-reported output can include generated tokens that are not visible in the final text, such as formatting or tool-call tokens, so leave headroom if the response must contain a minimum amount of visible content. OpenAI describes output limits and accounting in its advanced usage guide.
Token savings and latency are not the same
OpenAI’s Latency optimization documentation offers workload-dependent heuristics: cutting 50% of output tokens may cut latency by about 50%, while cutting 50% of a prompt may improve latency by only 1–5%. These are latency estimates, not guarantees or universal estimates of API-cost savings. The consulted provider guidance does not establish a general percentage of tokens or cost saved by prompt cleanup.
Reuse stable context when requests repeat
If many calls share a large instruction or reference section, keep that common prefix unchanged and put frequently changing information later when the provider’s caching behavior supports it. This can reduce charges for repeated input without shortening the logical prompt. Confirm cache reads or hits in usage data rather than assuming they occurred.
OpenAI and Google both document caching, but eligibility, minimum context, controls, and billing differ by provider and model. Google’s context caching guide describes implicit caching, which has no guaranteed cost saving, and configurable explicit caching, whose costs depend on cached tokens and storage duration. Check the current documentation for the exact model and endpoint you use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose the right optimization for the workload
| Approach | What it changes | Best fit | What to verify |
|---|---|---|---|
| Remove irrelevant or repeated context | Input tokens | Prompts with duplicated instructions, long histories, or noisy retrieved material | Whether the trimmed context still supports correct, complete answers |
| Summarize or chunk source material | Input tokens per request | Long documents when the task needs only selected details or focused analysis | Whether important facts, caveats, or relationships were lost |
| Specify response shape and set an output cap | Generated output tokens | Tasks that do not require lengthy explanations or open-ended output | Whether answers are complete and not truncated |
| Cache a stable prefix | How repeated input is processed or billed | Repeated calls with substantial shared context | Model eligibility, cache hits, usage accounting, and provider-specific billing |
Compare input, output, cache reads and writes, latency, and task quality for the actual model, API, and workload. A shorter prompt is not necessarily better if it removes necessary context; caching may reduce repeated-input charges even though the prompt itself remains the same length.
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