To measure whether a prompt revision reduces token usage, compare the original and revised prompts on the same representative requests, using the same model, endpoint, and settings. Record actual API usage for each run, then compare token counts alongside task quality, latency, and realized cost. A shorter prompt is not automatically a better prompt: lower token usage matters only if results remain useful.
What to measure in a prompt comparison
Keep the comparison tied to the exact model and API you use. Tokenization, message formatting, tools, structured output, multimodal inputs, generated response length, and caching can all affect what a count means. OpenAI’s token-counting guidance distinguishes input tokens sent to a model from output tokens it generates; a short visible answer may still include reasoning tokens in reported output usage.
- Input tokens: the tokens sent as the request input.
- Output tokens: the tokens generated by the model. Where applicable, this includes reasoning tokens that may not appear in the visible answer.
- Total tokens: the reported total for the request.
- Cached input tokens: input reused through prompt caching; these may be priced differently from uncached input.
- Quality, latency, and realized cost: operational checks that help establish whether a token change is actually beneficial.
Use the API’s returned usage fields for the primary comparison. A text tokenizer estimate is useful before sending a request, but it is not a substitute for actual usage, especially when the request includes message structure, tools, schemas, images, or files.
Build a reproducible baseline
Save the exact prompt and the conditions under which it runs. Treat prompt text as application code: version it, and preserve enough information to rerun the baseline after making a change. OpenAI’s prompting guidance recommends testing prompt changes with evaluation cases.
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- Prompt version, including system and developer instructions and any templates.
- Model, endpoint, and relevant request settings.
- Representative test inputs, including ordinary cases and meaningful edge cases from the application.
- A consistent quality rubric or evaluation criteria, such as correctness, completeness, format compliance, and usefulness.
Use the same test set for the baseline and the optimized prompt. One unusually short or long example can distort the comparison; use enough cases to reflect the work the prompt actually handles.
Estimate input tokens before sending
For plain text
Use OpenAI’s Tokenizer or the programmatic tiktoken library, selecting the encoding for the model you intend to call. This gives a useful estimate of text tokenization, not necessarily the count of a complete structured API request.
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For a complete Responses API input
Use the Responses API input-token counting method to count the full input before making the request. Unlike a plain-text estimate, a complete input count accounts for formatting tokens such as message roles and boundaries. Structured content, tools, schemas, and multimodal material are additional reasons to count the actual request rather than only a prompt string.
Pre-send input counting does not predict generated output tokens. The model’s response length—and any reasoning tokens reported—must be measured from the completed request.
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Run the baseline and revised prompt on identical cases
- Send each saved test input with the baseline prompt and the recorded model, endpoint, and settings.
- Repeat the requests with the optimized prompt, changing only what you intend to test.
- Save each request’s usage fields, response, latency, and any cost data available to your application.
- Evaluate both sets of responses using the same rubric or evaluation cases, ideally without changing the scoring standard between versions.
OpenAI’s guidance emphasizes testing representative tasks, not just comparing visible response length. If generation is variable, run cases enough times to understand that variation and report the spread or a representative range along with any average.
Record actual API usage and compare like with like
Field names depend on the API. Chat Completions returns prompt_tokens, completion_tokens, and total_tokens. Responses returns input_tokens, output_tokens, and total_tokens. Save the per-request values and aggregate the same fields across the same test set. The Usage Dashboard can show activity over time, but per-request records make it easier to tie usage back to a prompt version and test case.
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| Comparison item | What to record |
|---|---|
| Input usage | Chat Completions: prompt_tokens; Responses: input_tokens. |
| Generated usage | Chat Completions: completion_tokens; Responses: output_tokens. |
| Total usage | total_tokens for either API. |
| Quality | Results scored using the same rubric or evaluation cases for both prompt versions. |
| Latency and cost | Observed latency and realized cost for the same requests, where relevant. |
| Caching, when applicable | Cached input and cache-write counts, along with total input usage. |
For a chosen comparable metric, calculate relative change as (baseline total − optimized total) / baseline total × 100. Name the field, test sample, model, and conditions the percentage describes. For example, a percentage based on total tokens across a test set is not the same claim as a percentage based only on input tokens. This formula is arithmetic guidance, not a published savings benchmark; no general percentage reduction is established for prompt optimization.
Check quality, cost, and caching—not just token totals
Validate that the task still succeeds
Compare response quality on the same cases as usage. A token reduction alone does not establish that the revised prompt is equally correct, complete, or reliable. If quality falls on a material task, report that trade-off rather than presenting the token change as an unqualified improvement.
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Use actual usage categories to assess cost
Token reduction and cost reduction are related but not interchangeable. Model rates can differ for input, cached input, and output tokens, and generated reasoning usage may also matter. Calculate realized cost using the selected model’s current rates and the actual usage categories returned for the requests; do not infer savings from total-token change alone. Check OpenAI’s API pricing for current rates.
Track cache behavior when prefixes may be reused
When requests share reusable prefixes, include caching in the measurement rather than attributing every cost change to prompt length. OpenAI’s prompt-caching guide recommends tracking usage.input_tokens_details.cached_tokens, usage.input_tokens_details.cache_write_tokens, input-token counts, latency, and realized cost. Aggregate cached and total input counts over the same request group or period when calculating a cache-hit rate.
Cache thresholds, accounting fields, rates, and retention behavior depend on the model and can change; verify the active model’s documentation before applying a numeric assumption. For GPT-5.6 and later, OpenAI’s guide gives an illustrative minimum cacheable prefix of 1,024 visible input tokens. Under its stated usual 0.1× cache-read-rate assumption, writing and then reading an eligible 1,024-token prefix once costs 1.35× ordinary input-token cost, compared with 2× for processing it twice without caching. Across ten requests, one write plus nine full reads costs 2.15× ordinary input-token cost, compared with 10× without caching. These are guide-specific illustrations, not guaranteed savings or values to apply to other models.
Report the result clearly
A useful report makes the test conditions visible so another developer can reproduce the comparison. Include:
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- Prompt versions, model, endpoint, and relevant settings.
- The test set and number of requests or runs.
- Input, output, and total usage, with averages and a distribution or representative range when results vary.
- Quality results from the shared rubric or evaluation cases.
- Latency and realized cost, if those outcomes matter to the application.
- Cached input and cache-write counts if caching applies.
Keep estimates and API-reported usage separate in the report. Also label the exact metric behind any percentage change; do not imply that the result applies universally to other prompts, models, or workloads.
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