Cost Calculators → Cost Scenarios
Fine-Tuning vs Few-Shot Prompting — Break-Even Analysis
When does fine-tuning become cheaper than few-shot prompting? Real break-even calculation.
Low estimate
$150/mo
Mid estimate
$200/mo
High estimate
$280/mo
Assumptions
- 1,000 training examples × 500 tokens × 3 epochs = 1.5M training tokens
- Training cost: $3/M = $4.50 one-time
- Few-shot examples: 2,000 extra tokens per call
- Fine-tuned inference: $0.30/M input (no few-shot)
- Volume: 100,000 requests/month
Base model: GPT-5 mini — $0.25/M input · $2/M output · 2026-08-19
Cost breakdown by component
Fine-tuned inference (monthly)100%
How to cut this cost by 50%+
Fine-tuning eliminates few-shot tokens
Without 2k few-shot tokens per call, inference drops from $0.50 to $0.30/M effective
40-60% savings
Calculate for your exact workload
These are estimates based on typical patterns. Enter your specific token counts and volume for a precise calculation.
Frequently Asked Questions
Is fine-tuning cheaper than few-shot prompting?
At 100k requests/month with 2,000 extra few-shot tokens per call, fine-tuning typically breaks even in 2-4 months. Below 10k requests/month, few-shot is almost always cheaper because the training cost doesn't amortize. Above 500k requests/month, fine-tuning savings are substantial.
Pricing verified 2026-08-20. All figures are estimates with stated assumptions. See methodology.