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Measure actual token usage before editing prompts
Words and characters are only rough clues. Token counts depend on the model, its tokenizer, language, and the structure of the API request. Provider usage fields are the better accounting reference: visible text alone may not show all generated tokens, and request wrappers, tools, images, files, or conversation content can affect input counts.
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For OpenAI, the token-counting guide describes counting full Responses API inputs, including messages, images, files, tools, and conversation content. For Anthropic, the token-counting endpoint counts structured message inputs, but its result is an estimate; some server-side tools are not included in preflight counts. Anthropic also says the endpoint is free, has separate rate limits from message creation, and supports active models.
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Log enough information to compare like with like: model, endpoint, prompt version, input and output tokens, cached input tokens when available, generated candidate count, and task-level quality. A rough word or character estimate can help spot a very large prompt, but it should not be used as a billable-token substitute.
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Identify whether input or output is the bigger problem
If input dominates, inspect persistent instructions, conversation history, retrieved passages, tool definitions, schemas, and duplicated application context. If output dominates, look at verbosity, response format, duplicate completions, and whether the application requests more candidates than it uses. If many calls share the same large input prefix, investigate caching.
OpenAI notes in its production best practices that settings such as n and best_of above one can create multiple outputs and multiply generated tokens. That can outweigh small prompt edits.
Reduce input tokens without removing information the task needs
Once you know the input is the main source, remove material that does not help answer the current request. Keep task-critical definitions, evidence, user-specific details, and constraints; do not delete them just to hit an arbitrary token target.
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- Remove repeated rules, examples, boilerplate, and instructions that say the same thing in different words.
- Keep only retrieved passages relevant to the current question rather than sending every search result or document chunk.
- Clean unnecessary markup or HTML from text before including it as context.
- Do not resend conversation history that the model no longer needs to complete the task.
- State the task, constraints, and expected answer shape clearly so the model does not need extra clarification or broad, unfocused context.
OpenAI recommends clear, concise prompt construction in its prompting guide. Its latency guide also recommends filtering large context such as retrieval-augmented generation results and cleaning HTML. These steps reduce needless input, but they should be checked against task quality rather than treated as a license to strip useful evidence.
Control output length and avoid duplicate generation
Output tokens are often a direct usage lever. Ask for only the fields or content the application actually consumes: for example, a concise explanation, a fixed set of JSON keys, or a short answer with a specified limit. Avoid requesting multiple candidates if the application uses only one.
A maximum output-token setting is a ceiling, not a promise of a concise, complete response. If it is too low, the model can stop before finishing a required answer. Stop sequences can also cut output at a boundary that may not correspond to a complete result. Set bounds with enough headroom and inspect responses for truncation.
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OpenAI’s latency guidance says reducing input length may have only a small latency effect for ordinary prompts: its illustrative estimate is a 1–5% latency improvement from cutting prompt size in half. That is a latency example, not a claim about token or cost savings. The same guide identifies output generation as a major latency factor.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteUse prompt caching for repeated, stable prefixes
If many requests share a large block of instructions, tools, or reference material, keep that common content identical and in the same order, then put changing user data later. OpenAI’s prompt-caching guide describes reuse of matching prefixes; changing earlier content can prevent reuse of the later prefix. Caching can reduce repeated input processing or billing where supported, but it does not reduce the number of tokens in the text you send.
Cache eligibility and pricing vary by model and setup, so verify actual cached-token usage in provider usage fields or the dashboard rather than assuming a hit. The current OpenAI guide identifies a 1,024-token minimum cacheable prefix for GPT-5.6 and later; earlier models vary by request settings. Check the live documentation for the target model because these rules and rates can change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Consider request design, model routing, and fine-tuning
Combine steps only when the result stays reliable
Combining strictly sequential LLM steps into one call may remove unnecessary round trips when a single prompt and structured response can safely do the work. Batch independent requests when the endpoint supports it. These changes can reduce request count and latency, but they do not guarantee lower token use; a combined response may generate more text. Compare end-to-end token usage, errors, quality, and latency on representative traffic.
Route work to a less expensive model only after evaluation
A smaller or less expensive model can lower cost per token for tasks it handles adequately, but it may not preserve quality on every workload. Test representative examples, set an explicit quality threshold, and retain a fallback route for requests that fail it. OpenAI discusses reducing cost both through token quantity and through model choice in its production best practices.
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Fine-tuning may be worth evaluating when stable instructions or examples take up substantial context and there is enough representative data to validate the result. It is not a guaranteed token-saving or quality-preserving shortcut. Compare the full request, output quality, and operational cost against the prompt-based approach before adopting it.
Test every optimization against quality criteria
Keep a representative set of real tasks and edge cases. Compare the existing version with one change at a time, using the same inputs. Track:
- Task success, correctness, and factuality.
- Completeness and adherence to the requested format and instructions.
- Safety and refusal behavior where relevant.
- Input, output, and cached-token counts.
- Latency and total cost.
- Robustness across unusual or borderline cases.
Roll out a change only when the savings meet your target without a meaningful regression on the quality criteria that matter for the application. OpenAI’s prompting guidance recommends testing prompt changes with evaluation cases. No universal percentage of savings can be assumed: results depend on the prompt, model, task, and traffic.
Provider and model differences matter
Do not assume the same text produces the same token count across providers or model generations. Anthropic’s current token-counting documentation says Claude 4.7 and later use a newer tokenizer and produce approximately 30% more tokens for the same input text than earlier Claude models; the exact difference depends on content and workload. Recount with the target model rather than carrying over a count from a different model.
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