What is synthetic dataset generation?

Instead of hand-labelling data, you prompt a strong teacher model to produce training examples for a smaller student model. This distillation approach is cheap and fast but adds an upfront generation cost, which this tool estimates.

Why include a validation pass?

Generated examples often need a second model call to score or filter quality. That validation pass consumes its own input and output tokens, so it materially affects the total cost — the estimator accounts for it.

Does this include the actual fine-tuning training fee?

No. This estimates only the cost of generating the dataset with the teacher model. The provider's separate per-token training fee and any LoRA/full fine-tune compute are out of scope here.

How accurate is the estimate?

It is a planning figure based on your token averages and current list prices. Real costs vary with prompt design and how many examples you discard during validation, so treat it as an upper-bound budget.

Fine-Tuning Dataset Generation Cost Estimator

Fine-tuning dataset generation cost estimator

Distillation — using a strong teacher model like GPT-4o to generate training examples for a smaller student model — is one of the cheapest ways to build a fine-tuning dataset. But “cheap” still has a number. This estimator turns your target dataset size and per-example token usage into a total generation cost and a clean per-example unit cost.

How it works

Each generated example costs the teacher model’s input price for the prompt and output price for the completion. An optional validation pass (scoring or filtering each example) adds a second round of tokens:

gen_cost/example  = prompt_tokens/1e6 × in_price + completion_tokens/1e6 × out_price
val_cost/example  = val_prompt_tokens/1e6 × in_price + val_completion_tokens/1e6 × out_price
total             = (gen_cost + val_cost) × num_examples

The estimator reports both the all-in total and the cost per surviving example so you can compare against the price of human labelling.

Tips and notes

Generate in batches and validate aggressively — discarding low-quality examples early is cheaper than fine-tuning on noise and re-running.
A cheaper teacher (GPT-4o mini, Haiku) can produce most examples, reserving the expensive model only for the hard cases or the validation judge.
Remember this is generation cost only; budget separately for the provider’s per-token training fee, which is a distinct line item.