Cost formula
Total cost = (generate: raw examples × gen tokens / 1M × gen price) + (critique: raw examples × judge tokens / 1M × judge price) + (filter overhead); Usable examples = raw examples × pass rate
Calculator
Synthetic data generation cost calculator
Plan instruction sets, preference pairs, and paraphrase augmentation. Set a target accepted size and accept rate—CentsPerToken sizes the raw generation volume and optional critique/filter passes.
Dataset cost
Cost per useful example
$0.001519/ accepted · $15.19 total
13,334 raw gens → 10,000 kept at 75% accept
Ideal (100% accept)$11.39
With quality filter$15.19
Cost / useful example$0.001519
Cost / 1k accepted$1.52
Quality waste %25%
Over-gen overhead$3.80
Pipeline
Dataset generation
Generate + quality filter; over-gen for rejects
Quality
Useful-example economics
Reject rate drives over-generation and cost per kept example.
Quality waste25%
25% of generations are discarded at 75% accept — that waste shows up as $3.80 over-gen overhead.
GeneratorGPT-5 mini
Raw generations13,334
Cost / useful example$0.001519
Accept rates are pipeline-specific—calibrate on a pilot batch. Updated 2026-07-31. Approximate static list prices for planning only. Always verify against each provider’s official pricing page before production budgeting.
Guide
How to get value from this calculator
Estimate cost to generate synthetic training or eval datasets through generate, critique, and filter pipelines. Over-generation is standard—you produce more examples than you keep—so plan gross generation volume, not just final dataset size. Includes judge and filter pass pricing.
Why it matters
Synthetic data pipelines look cheap per example until over-generation, critique passes, and failed filters multiply token volume. Cost per usable labeled example is the number that matters for fine-tuning ROI.
How to use it
- Set target final dataset size in examples.
- Enter over-generation factor (e.g., 3x raw examples to get 1x usable after filtering).
- Set tokens per generation call and tokens per critique or judge call.
- Choose generator and critic model tiers.
- Review total pipeline cost and cost per usable example.
- Compare against manual labeling or licensed dataset purchase for ROI context.
Planning tips
- Cheaper models for draft generation and stronger models for critique often optimize cost-quality.
- Aggressive filters reduce fine-tuning noise but increase generation waste—tune pass rate assumptions.
- Dedupe embeddings after generation to avoid paying twice for near-duplicate examples.
- Start with 1,000-example pilots before budgeting million-token synthetic runs.
- Watch cost per useful example and the quality-waste gauge—raising pass rate often beats a cheaper draft model.
- Include human review spot-checks in project cost outside this API calculator.
Frequently asked questions
What over-generation factor is typical?
2–5x is common depending on filter strictness. Measure pass rate on a pilot batch before scaling assumptions.
Are generator and judge prices exact?
CentsPerToken uses approximate list prices for planning. Verify both models' rates officially.
Does this include fine-tuning cost?
No. This covers synthetic dataset creation only. Add fine-tuning training and inference from the fine-tuning calculator.
How does output token pricing affect synthetic data cost?
Generation is output-heavy on the generator model. Critique adds judge input and output. Output premiums drive total pipeline cost.