Choose an LLM by cost per acceptable result, not by the lowest input-token price. Test inexpensive candidates on examples from your own workload, compare their accuracy and latency with a stronger baseline, and include output tokens, service mode, caching, and data-use terms in the decision.
What “low-cost” should mean
A model’s quoted token rate is only one part of what you pay. A cheaper model can cost more in practice if it produces unusable labels, misses required fields, writes incomplete summaries, or needs extra retries and human review.
Use cost per acceptable result as the deciding measure: estimate the spend for a representative run, count the outputs that meet a task-specific quality bar, and divide spend by accepted outputs. The acceptance rules depend on the job. For classification, check whether the assigned label is correct. For extraction, check required-field validity and whether the model invents unsupported values. For summarization, check coverage of important points and adherence to any length or format constraints.
Shortlist candidates that fit the task
Start with a low-cost generative model
One concrete starting point is Google’s Gemini 3.1 Flash-Lite. Google describes it as “A cost-efficient model, optimized for high-volume agentic tasks, translation, and simple data processing.” Google’s pricing page lists paid standard rates of $0.25 per million text, image, and video input tokens and $1.50 per million output tokens; its listed Batch rates are $0.125 per million input tokens and $0.75 per million output tokens. These are Google’s published prices in 2026, not a guarantee that this model is the cheapest or accurate enough for your particular data. Check Google’s Gemini API pricing for current rates and eligibility.
Use embeddings only for embedding-shaped tasks
Google’s model catalogue describes its Gemini Embedding endpoint as providing representations for “text classification and RAG systems.” An embedding endpoint produces vector representations; it is not a drop-in generative replacement for extracting structured fields or writing summaries. Consider it when your classification approach is based on similarity or retrieval, and evaluate it against that design. See Google’s model catalogue for current model and endpoint status.
Estimate the whole API bill
A first-pass estimate is:
Estimated API spend = input tokens × input rate + output tokens × output rate + applicable cache, tool, or service fees
Input and output prices can differ substantially, so estimate both using your expected prompt and response sizes. Also account for retries or tool calls if your application uses them. Then apply the same workload and acceptance rubric to each candidate and compare cost per accepted output, rather than comparing token rates in isolation.
Choose a service mode that matches your deadline
Google’s optimization guide summarizes its service modes as follows. The descriptions and discounts are provider-published guidance; verify the live terms and the model’s eligibility before relying on a mode.
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| Mode | Published pricing characteristic | Service characteristic | When to consider it |
|---|---|---|---|
| Standard | Full price | Standard service | Use as the baseline for cost and latency comparisons. |
| Flex | 50% discount versus Standard | Best-effort, with a 1–15 minute target | Consider for work that can tolerate variable waits. |
| Batch | 50% discount versus Standard | High-throughput processing; up to 24 hours | Test for offline queues that do not need immediate responses. |
| Priority | 75%–100% above Standard | Seconds-level service and non-sheddable capacity | Consider when service characteristics justify the premium. |
| Caching | Up to a 90% discount on cached input, plus prorated token storage | Discount depends on cache use; storage is charged | Evaluate for repeated long prompts or corpora, including cache-hit behavior and storage cost. |
These mode characteristics are summarized in Google’s optimization and pricing documentation. Flex’s target is not a guaranteed response time, and Batch’s stated window may not suit deadline-sensitive work.
Run a fair comparison before deploying
- Build a representative test set. Include ordinary cases and difficult examples drawn from your actual labels, extraction schema, or source material.
- Write acceptance rules first. Decide what counts as a correct label, valid required fields, unsupported extraction, adequate summary coverage, and an acceptable failure response.
- Keep the comparison controlled. Give each candidate the same prompts, examples, input data, and output constraints.
- Record operational results. Track input and output tokens, latency, failures, and the number of outputs that pass your rubric.
- Calculate cost per accepted result. Compare each candidate with a stronger model as a quality baseline; the baseline shows whether savings are worth any drop in usable output.
- Repeat after meaningful changes. Re-test when prompts, model IDs or versions, data distributions, or output schemas change.
- Verify production details. Before launch, check current model status, prices, limits, batch or cache eligibility, account tier, and data-use terms.
Check data-use terms for your account and deployment
Google’s pricing documentation distinguishes free and paid tiers and indicates that paid-tier content is not used to improve its products, while free-tier content may be used. Treat that as a documentation summary, not legal advice or a substitute for checking current contractual terms, account settings, region-specific availability, and your organization’s data requirements. Review those conditions before sending sensitive inputs. The relevant provider page is Google’s Gemini API pricing documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Recheck model status and prices
Model names, endpoint availability, pricing, limits, and data-use terms can change. Confirm the live catalogue and pricing before implementation, and repeat the check before production changes. A model listing alone does not establish that an endpoint is available for your account or region.
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