AI Fine-Tuning Cost Calculator

Fine-Tuning Cost Estimator

Training a custom model? Calculate the cost of fine-tuning runs on OpenAI.

Total tokens in your training file.
Number of training passes.

How This Estimator Works

The AI Fine-Tuning Cost Estimator uses OpenAI's current training rates to project the total investment required to build a custom model. Fine-tuning costs are calculated based on the total number of tokens processed, which is a function of your Dataset Size multiplied by the number of Epochs (training passes).

How to Use the Calculator

  • Dataset Size: The total tokens in your Prepared JSONL file. (Standard rule: 1,000 words ≈ 1,300 tokens).
  • Epochs: The number of iterations. OpenAI defaults to 3, but more complex styles may require 5-10.
  • Base Model: GPT-4o-mini is currently the best balance of power and price for custom fine-tunes.
Example: Customer Service Bot

You have 500 successful chat logs totaling 250,000 tokens.

- Training Passes: 3 Epochs
- Model: GPT-4o-mini
- Total Training Tokens: 750,000
- Total Cost: ~$2.25

Fine-tuning is incredibly affordable compared to human training. For less than the cost of a coffee, you can teach an AI your company's entire brand voice.

Expert Tip: Always run a "Dry Run" on a small subset (10%) of your data first. Fine-tuning can sometimes cause "Catastrophic Forgetting," where a model becomes great at your task but loses its ability to follow basic conversation.

Fine-Tuning Strategy FAQ

What is an 'Epoch'?

An epoch is one full cycle through your entire training dataset. Too few epochs and the model won't learn your style; too many and it will 'overfit,' becoming rigid and repetitive.

Are there hidden costs?

Yes. Once a model is fine-tuned, the inference (usage) cost is usually higher than the base model. For example, GPT-4o-mini fine-tuned inference is ~2x more expensive per token than the standard public version.

Can I fine-tune Claude or Gemini?

Yes, though their pricing structures and UI's differ. This calculator focuses on OpenAI standards, but the token-to-cost ratio is comparable across the industry leaders.