Why does few-shot cost more?

Few-shot prompting prepends worked examples to every request. Those example tokens are re-sent and re-billed on every single call, so a few-shot prompt with five 200-token examples adds 1,000 input tokens to every request for the life of the deployment.

When is few-shot worth it?

When the quality improvement reduces expensive downstream costs — retries, human review, refunds, or churn. If few-shot lifts accuracy from 80% to 92% on a task where each error costs you real money, the extra token cost is usually trivial by comparison.

Can I get few-shot quality without the cost?

Often yes. Prompt caching (where examples are cached and re-used cheaply), fine-tuning, or a slightly stronger base model can deliver similar quality without re-paying for examples on every call. Compare those against the few-shot overhead shown here.

Is output cost included?

This tool focuses on the input-side difference, since zero-shot and few-shot usually produce similar output length. Add output cost separately if your few-shot examples change response length materially.

Zero-Shot vs Few-Shot Cost-Quality Calculator

Does few-shot prompting pay for itself?

Adding worked examples to a prompt (few-shot) almost always improves quality — but those examples are re-sent on every call, so the token cost compounds with volume. This calculator shows the per-call and monthly cost of zero-shot vs few-shot, the extra spend the examples add, and a breakeven view so you can decide if the quality gain is worth it.

How the comparison works

fewshot_input = zeroshot_input + (example_tokens × example_count)
extra_per_call = (fewshot_input − zeroshot_input)/1e6 × in_price
monthly_extra  = extra_per_call × requests_per_month
cost_per_quality_point = monthly_extra / quality_delta_pct

The key insight is that the example tokens are a fixed tax on every request. At low volume the tax is negligible; at high volume it can dominate, and that is exactly when prompt caching or fine-tuning starts to look attractive.

How to read the result

If the cost per quality point is small relative to what an error costs you, keep the examples. If it is large and your volume is high, evaluate prompt caching (re-use the example prefix cheaply) or fine-tuning (bake the behaviour into the model so you stop paying for examples at all).