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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Sometimes—but only when minification reduces the billed input-token count for the complete request. API billing is based on tokens, not raw JSON characters, and tokenization varies by model. Measure both versions on the model and endpoint you plan to use; there is no reliable general percentage for JSON-minification savings.
Why shorter JSON does not automatically mean a cheaper request
Removing indentation, line breaks, and spaces can make JSON shorter. But providers bill according to token categories and model-specific rates, not character count. Whitespace removal saves money only if it lowers the tokens that are actually billed.
Tokenizers do not assign a token to every character, so you cannot estimate savings by counting spaces removed. How a payload is split into tokens depends on its content and the model. OpenAI’s token guidance explains how token counting works; its pricing page separates input, cached-input, and output rates by model. Those live prices can change, so check the applicable rate when calculating cost.
There is no general, official JSON-minification savings percentage. The practical answer is specific to your payload, model, and request setup.
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What to count: the complete request, not just the JSON string
A plain-text tokenizer can help estimate text tokens, but a full API request can include additional structure. Roles and message boundaries, tools, schemas, images, files, and model-specific handling may affect the count or the bill.
For OpenAI Responses requests, the input-token counting endpoint accepts the same input format as a request and includes formatting tokens for roles and boundaries. Use the target model and count the complete request where the provider offers that capability. Do not treat a text-only count as a complete API estimate.
How to test whether minification saves money
- Make two equivalent versions. Keep the request’s meaning and all fields the same; change only the JSON formatting you want to test.
- Count both complete requests. Use the provider’s counting tool for the intended model and endpoint. For OpenAI Responses, use the input-token counting endpoint; for plain text, use the target model’s tokenizer as an estimate.
- Send representative requests. Compare the normal and minified versions under the same model, tools, schemas, and task conditions. Record actual usage rather than relying only on a local estimate.
- Compare all billable usage categories. Check input, cached input, output, and any other usage fields reported for the request. A shorter visible response is not enough to determine total cost.
- Apply the current rates. Calculate each version using the model and token-category prices in effect for your service tier when the request is made.
- Repeat after changing providers or models. Recount with the model you intend to use; a count from another tokenizer is not a reliable substitute.
OpenAI’s token-counting guidance advises testing representative tasks rather than comparing only visible response length. That matters because total task cost can change with output or reasoning as well as input.
Keep prompt caching separate from minification
Cached input and ordinary input may have different rates. OpenAI’s prompt-caching documentation describes discounted pricing for eligible repeated prompt prefixes. A minified request may affect the input text, while whether a repeated prefix is cached is a separate factor. When comparing costs, record cache usage rather than crediting a lower cached-input charge to minification.
Why counts change across providers and models
Token counts are not portable between providers or model generations. Anthropic’s token-counting documentation says its counts are estimates, may include automatically added system tokens that are not billed, and should be obtained for the intended model. The guide also says Claude 4.7 and later use a newer tokenizer that can produce approximately 30 percent more tokens for the same input than earlier Claude tokenizers; the exact change depends on content. That is a model-specific tokenizer difference, not an estimate of JSON-minification savings.
When comparing providers or models, compare measured complete-request counts, applicable input and cached-input rates, output and reasoning usage for the same task, and whether caching actually applies. A lower per-token rate or smaller input count alone does not establish a lower total cost.
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