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To reduce tokens when sending structured data to an LLM, first remove fields, records, and conversation history the task does not need. Then measure the remaining request with the target model, test compact encodings against your real data, and validate the result for quality. Minifying JSON or switching to CSV, YAML, or TOON is not guaranteed to save tokens: token counts depend on the tokenizer and the shape of the data.
1. Remove information the task does not need
Start with the model’s job, not with the serialization format. Identify which fields and records are necessary to answer the specific request. Filter or aggregate data in application code when the model does not need the raw detail; sending fewer relevant inputs is often more dependable than trying to compress everything.
Trim fields and records
- Omit fields that do not affect the requested decision or output.
- Filter records to the time range, category, or entities the task uses.
- Aggregate repetitive measurements when individual values are not needed.
- Remove duplicated content and repeated instructions.
For very large contexts, reducing input can also help latency. OpenAI discusses input-token reduction in its latency optimization guidance.
2. Measure the actual request before and after changes
Bytes, characters, and tokens are different measurements. A shorter JSON string is not necessarily a lower-token request, and a plain-text tokenizer count may omit parts of a complete API request. OpenAI recommends tiktoken for programmatic plain-text tokenization, while cautioning that such a count may not include every token sent through an API.
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Build a representative baseline
For the target model and API configuration, record input-token usage, relevant usage-detail fields, output quality, latency, and cost. Use local token estimates for quick comparisons, but use actual API usage reporting to confirm the request total. Repeat the measurement with representative examples rather than a single unusually small or tidy record.
After changing the model, tokenizer, schema, tool definitions, or API configuration, measure again. Tokenization and model behavior can change across those conditions.
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3. Choose a representation that fits the data
Use the target model’s token count and task quality to compare formats. Keep the model’s ability to interpret values in view: a few saved tokens are not useful if compact labels make fields ambiguous or increase errors.
| Approach | Where it may fit | What to check |
|---|---|---|
| Minified JSON | Existing JSON payloads where whitespace is unnecessary. | Compare its token count with pretty-printed JSON; fewer characters do not guarantee fewer tokens. |
| CSV or schema-once rows | Flat tables or repeated records with the same fields. | Make the column order and value meanings clear, and test escaping and missing values. |
| JSON | Nested or irregular data where explicit structure helps preserve meaning. | Remove irrelevant fields, but retain enough structure for unambiguous interpretation. |
| TOON | A candidate to assess for large, regular datasets. | Test on the target workload; Thoughtworks notes it is often a poor fit for deeply nested or non-uniform structures, and that CSV can be more compact for flat tables. |
Thoughtworks’ 2026 Technology Radar discussion of TOON recommends considering it for a narrower large-regular-data use case, not as a universal replacement. No universal percentage saving for converting JSON to CSV or TOON is established here. Compare token usage and task quality on your own representative inputs.
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Remove repeated keys carefully
For repeated records, a schema-once layout can avoid restating long field names on every row. Include a clear schema or instruction that maps each value to its field, then test for errors involving ordering, omitted values, escaping, and nulls. If that extra interpretation burden harms task quality, the token reduction is not a win.
4. Reuse stable prefixes for recurring requests
If many requests share the same instructions or schema, put that stable material before the changing data when the application allows it. OpenAI states that “Prompt caching reuses work when requests share the same prompt prefix.” Caching applies to an eligible matching prefix; new, varying data still has to be processed. Eligibility and economics depend on the model and request settings.
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Inspect cached-token usage in the API response rather than assuming a cache hit. OpenAI’s guide says discounted cached-input rates can be “discounted up to 95%,” but that is not a guaranteed saving for an individual request: realized economics depend on the model, cache mode, pricing, and eligible cached-token volume. Check the current Prompt Caching guide for applicable conditions and data-control details, particularly before enabling extended cache retention.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Compact accumulated conversation history when appropriate
In a long-running interaction, old turns can consume context even when their details are no longer needed. Remove obsolete history or use a compaction flow supported by the provider and API. OpenAI documents server-side compaction for reducing context size while retaining state.
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Compaction changes the rendered prompt, so assess its effect on total input usage and cache reuse as well as task quality. A shorter compacted history may alter the prefix shared with later requests; measure the net result rather than judging only the compacted text length.
6. Treat output formatting as a separate optimization
Structured-output syntax and schemas are part of a different measurement from the structured input you send. A shorter requested output format may reduce output tokens in some cases, but a schema can add input instructions and affect a reusable prompt prefix. Track input and output usage separately when evaluating a change; do not assume an output-format improvement also reduces input tokens.
Quick Recap
A practical test loop
- Define the task: list the fields, records, and conversation state needed for the next model call.
- Create a baseline: capture actual input usage and relevant usage details, plus quality, latency, and cost for representative examples.
- Trim first: filter, aggregate, and deduplicate in application code where possible.
- Compare encodings: test minified JSON and only plausible alternatives for the data shape, using a tokenizer estimate and then actual API usage.
- Check interpretation: compare task results and error modes, not just token counts.
- Test repeated-context behavior: if requests share a prefix, inspect cached-token usage and confirm the savings under the current model and settings.
- Recheck after changes: repeat the test when the model, tokenizer, schema, tools, or API configuration changes.
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