LLM Calculators

Fine-Tuning Cost Calculator

Estimate training costs and find your break-even point.

Calculate the cost of fine-tuning supported LLM models and determine whether fine-tuning is economically worthwhile for your use case. Compare training costs across GPT-4o, GPT-5 mini, GPT-4o mini, Mistral Large, and Mistral Small.

1,000
500
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1,000

Training Cost

Total training tokens1,500,000
Training price/1M tokens$25.00
Total Training Cost$37.50

After Fine-Tuning

Input price/1M (fine-tuned)$2.00
Output price/1M (fine-tuned)$8.00
Base model input/1M$2.00
Base model output/1M$8.00

Break-Even Analysis

Fine-tuning costs $37.50 upfront. At 1,000 requests per day, you break even in approximately 7 days.

How Fine-Tuning Costs Work

Fine-tuning involves training a base model on your custom dataset. You pay a training cost based on the total tokens in your training data multiplied by the number of training epochs. After fine-tuning, the model costs more per request than the base model. GPT-4o fine-tuning training costs $25.00 per million tokens, while GPT-4o mini costs just $3.00 per million. Mistral Large training costs $4.00 per million tokens.

When Fine-Tuning Makes Economic Sense

Fine-tuning is worth it when the improved output quality or reduced prompt length saves you more per request than the additional per-token cost. If fine-tuning lets you eliminate a long system prompt (saving 1,000+ tokens per request), the savings add up quickly at high volume. The break-even calculator above shows exactly how many requests you need to recoup the training investment.

Frequently Asked Questions

How much does it cost to fine-tune GPT-4o?
Fine-tuning GPT-4o costs $25.00 per million training tokens. A dataset of 10,000 examples averaging 500 tokens each (5M total tokens) trained for 3 epochs costs approximately $375. After fine-tuning, inference costs $3.75/$15.00 per million tokens compared to $2.50/$10.00 for the base model.
Is fine-tuning cheaper than prompt engineering?
Fine-tuning has a higher upfront cost but can reduce per-request costs by eliminating long system prompts. If your current prompt uses 2,000+ tokens of instructions that fine-tuning could replace, and you run 10,000+ requests per day, fine-tuning typically breaks even within 1-4 weeks.
Which models support fine-tuning?
models supporting fine-tuning include GPT-4o ($25.00/M training), GPT-4o mini ($3.00/M), GPT-5 mini ($3.00/M), Mistral Large ($4.00/M), and Mistral Small ($0.20/M). Claude and Gemini models do not currently offer public fine-tuning APIs.
How many training examples do I need for fine-tuning?
Most providers recommend a minimum of 50-100 high-quality examples. For production use cases, 1,000-10,000 examples typically produce the best results. More examples improve consistency but increase training costs linearly. Start with a small dataset to validate improvement before scaling up.

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Fine-tuning pricing from official provider documentation. Training costs are one-time per run. Actual results depend on data quality and task complexity.