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1. Compare total task cost, not the token rate
A lower input or output rate does not guarantee a cheaper result. Models can tokenize the same text differently and may use different amounts of output or reasoning to complete the same task. OpenAI puts it plainly: “A lower price per million tokens does not necessarily produce a lower total cost.”
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Test candidate models on representative requests and compare the cost of useful, successfully completed work. Include the tokens consumed, output quality, latency, and reliability. Count retries, multiple completions, tool calls, and reasoning tokens where applicable; a cheap first attempt may not be cheap if it needs repeated correction.
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- Judge whether the output meets your quality requirements, not merely whether it is shorter.
- Compare request-level usage and the time and retries needed to get an acceptable result.
2. Send less unnecessary input
Reduce repeated instructions and irrelevant context before changing models. Tighten prompts, summarize or preprocess long material, and split oversized inputs when the task permits. This can reduce the amount billed for each request, but preserve the information the model needs to produce a sound answer.
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Count the complete structured request where possible. A plain-text estimate may omit message boundaries, tool definitions, schemas, images, and files. Token count is not word count: encoding and language affect how text maps to tokens. OpenAI’s token guide notes, “A token count is not the same as a word count.”
3. Cache stable context that you reuse
If many requests share the same instructions or reference material, use the provider’s prompt-caching feature where eligible. Keep the reusable prefix unchanged and separate request-specific data so a small change does not prevent a cache match. Check usage records for cache hits instead of assuming the provider reused the context.
OpenAI’s prompt-caching guide says eligible cached input can receive a discount of up to 95%; that is a maximum, not a guaranteed saving. The realized rate depends on the model and its pricing, and the eligible prefix must match. Cached input still counts toward token-per-minute limits, and caching does not reduce output generation. See the OpenAI prompt-caching guide for current eligibility and pricing details.
Google documents implicit caching for Gemini 2.5 and newer models, as well as explicit cache objects with time-to-live-based storage pricing. Requirements and billing differ by provider, so check the relevant documentation and pricing before redesigning a workflow. Google’s Gemini caching documentation describes its options.
4. Use lower-cost processing only when its trade-offs fit
Some providers offer lower-cost processing for work that can tolerate slower turnaround or less predictable availability. Google’s documentation, last updated September 1, 2026, lists the following Gemini API service tiers:
| Google tier | Published price relative to Standard | Operational trade-off |
|---|---|---|
| Batch | 50% of Standard pricing | Target turnaround of up to 24 hours |
| Flex inference | 50% of Standard pricing | Synchronous, but sheddable/best-effort processing |
| Priority | 75% to 100% above Standard pricing | Higher-cost tier for workloads needing its service characteristics |
These are Google’s documented tier terms, not general discounts across AI providers; confirm current prices and conditions before relying on them. Batch can suit queued work without an immediate response requirement. Flex is synchronous but may be shed, so it is not interchangeable with a dependable completion guarantee. Compare any saving against acceptable turnaround, preemption risk, and the cost of a failed or delayed task. Google summarizes the choice as balancing “speed, cost, and reliability based on your specific workload needs” in its Gemini API optimization and inference documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Limit output and inspect actual usage
Set an output-token limit that fits the task, then monitor usage by workload rather than relying on visible answer length. Track input, output, cached input, and reasoning tokens where the provider reports them. Reasoning tokens may be billed as output even when they are not visible in the final answer, so a brief response can still have substantial usage. Agentic workflows can also consume intermediate input and reasoning tokens as they call tools or repeat steps.
Use dashboards and request-level usage data to find expensive paths. After adjusting prompts, models, caching, or service tiers, check whether total task cost improves without violating quality, latency, or reliability requirements. A modality-specific Google example should not be generalized to text: its September 1, 2026 documentation says agentic processing for long-form video can use up to 88% fewer input tokens, with results varying by query complexity and sampling depth.
Quick Recap
Best Value
How to put the five keys into practice
- Establish a baseline: collect usage and completion outcomes for representative tasks, including retries and tool use.
- Test model and prompt changes: compare total cost per acceptable result, not unit rates alone.
- Reuse what stays the same: structure stable context for caching and verify that cache hits appear in usage data.
- Route by urgency: use discounted tiers only when their turnaround and reliability behavior meet the task’s needs.
- Review continuously: monitor token categories and check current provider pricing and feature terms before budgeting against them.
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