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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsSometimes—but only when minifying the JSON reduces the number of billable input tokens for the specific model and request. Billing is based on tokens, not character count, so a shorter JSON string does not guarantee a lower charge. Measure the complete request and compare its actual usage to know whether it saves money.
Why minification may reduce costs—and why it may not
Minifying JSON removes formatting such as indentation, line breaks and optional whitespace. If those characters would otherwise contribute to the model’s input-token count, removing them can reduce billed input tokens. But tokenizers do not count characters one by one, and the effect varies by model and content. There is no reliable general percentage for JSON-minification savings in the official provider documentation cited here.
API rates are charged by token category, and a model may have separate prices for uncached input, cached input and output. Minification can affect input usage; it does not by itself reduce the output or reasoning tokens generated for a task. OpenAI also cautions that a lower per-token price does not necessarily mean a lower total task cost: models can tokenize the same text differently and produce different amounts of output or reasoning.
Count the complete request, not just the JSON string
A text-only tokenizer count may omit parts of an API request. Message roles and boundaries can add formatting tokens, and tools, schemas, images, files and model-specific behavior can affect the total. OpenAI’s token-counting guidance points to the target model’s tokenizer for plain text and provides a way to count input for a complete Responses request using the same input format. Use the full-request count when it is available rather than treating a JSON character count as a cost estimate.
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For Anthropic, use the token-counting method for the model you intend to call. Anthropic notes that counts are estimates and may include automatically added system tokens that are not billed. Its current documentation says Claude 4.7 and later use a newer tokenizer that can produce approximately 30% more tokens for the same input than earlier Claude tokenizers; the actual difference depends on content. That is a model-tokenizer difference, not a minification savings estimate.
How to test whether minifying saves money
- Make two equivalent inputs. Keep the request’s meaning and content the same; compare the normally formatted JSON with a minified version.
- Hold the request setup constant. Use the same model, endpoint, tools, schemas and other input fields. If you change providers or models, count again for the new target.
- Count each complete request. Use the provider’s full-request counting tool where available. For OpenAI Responses, use its input-token counting endpoint; a plain-text tokenizer does not necessarily capture the whole request.
- Run representative requests and inspect usage. Compare actual input, cached-input, output and other applicable usage fields. Do not judge total cost from visible response length alone.
- Apply the rates that match the request. Check the target model’s current input, cached-input and output prices and account for whether caching actually applied. OpenAI lists model-specific prices per million tokens and separates these categories; rates can change.
OpenAI recommends testing representative tasks rather than comparing only visible response length. A useful comparison therefore holds the task steady and looks at both usage and the resulting cost, not merely the prompt’s character count.
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Keep prompt caching separate from minification
Minification and prompt caching are different ways a request might affect input charges. Minification changes the text sent; caching can change the rate applied to eligible repeated prompt prefixes. OpenAI’s prompt-caching documentation describes discounted pricing for eligible cached input, while its pricing page lists cached input separately from uncached input. When comparing costs, record whether the request received cached-input treatment instead of attributing that difference to whitespace removal.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When the comparison is worth doing
- Test it when large JSON payloads recur often, or when you need to optimize a high-volume workload.
- Prioritize other changes first if formatting whitespace is a small part of the complete request; tools, schemas, repeated context, model choice and generated output may matter more to total cost.
- Recount after a model or provider change. Tokenization differs across models, and counts from one provider should not be treated as the count for another.
- Preserve valid JSON and intended meaning. A cost comparison is useful only if both versions represent the same input and task.
For a fair comparison, use the same representative workload and compare complete-request token counts, actual usage categories and applicable prices. Without those measurements, a shorter JSON payload is only a possible optimization—not evidence of lower API spend.
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