How to Reduce LLM Token Costs and Check Quality

For token-billed APIs, input and output usage contribute to the cost. Treat each optimization as a measured change: compare cost and output quality on your own tasks before keeping it.

1. Compress your input

In multi-step workflows, repeated explanations and unnecessary context can carry into the next model request. Inspect that input for edits that preserve the task's needs.

TrimText offers conservative Lossless edits and stronger Compact edits. Compact can change nuance; inspect the changes before using them. Neither mode promises a fixed reduction or a universal quality guarantee.

Measure savings with your target model's tokenizer and current input-token price. TrimText's displayed token counts are character-based estimates.

2. Choose the right model for the task

Not every task needs the most expensive model. A common pattern:

• Classification, extraction, formatting → use a candidate smaller model, if it passes your representative tests
• Creative writing, complex reasoning → use the model that meets your reasoning and quality requirements
• Speed-critical, low-stakes → benchmark inference latency as well as output quality

Keep a routing change only when your evaluation shows an acceptable quality and cost tradeoff.

3. Cache repeated queries

Check your provider's caching conditions for repeated system prompts and context. Keep stable text together and measure cache hits instead of assuming every repeated request receives a discount.

Start with the current provider caching documentation. Caching changes processing and billing; it does not remove tokens from your source text.

4. Trim your system prompts

Repeated system instructions contribute to input size, even when eligible cached input receives a discount. Review the prompt as it changes:

• Test whether an instruction is redundant before removing it
• Combine repeated rules while preserving their scope and exceptions
• Compare formats with the actual tokenizer; JSON and YAML are not automatically shorter

5. Limit output length

Set the endpoint's output limit to suit the task, and test whether responses are truncated. A cap that cuts off a required explanation is a quality regression, even if the bill is smaller.

6. Batch similar requests

When results can wait, consider a provider's asynchronous batch service. The OpenAI Batch API documentation describes discounted asynchronous processing. This is different from putting many tasks into one prompt; check model support, turnaround and failure handling.

The compounding effect

These changes affect different parts of a bill and can interact. Record a baseline, change one factor at a time, and compare total cost, latency and task success. Do not add headline percentage claims together or assume quality stayed constant.

Start with a small representative sample and retain the original so every compression change is reviewable.