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AI Fine-tuning Cost Calculator
Know your training costs upfront

Calculate exact fine-tuning costs for GPT-4o, GPT-4.1, Gemini 2.0 Flash and more. Enter your dataset size and get an instant cost estimate across all supported models.

Your Training Dataset
200
Recommended: 50–1000 examples
500
Typical: 200–2000 tokens/example
Training epochs
OpenAI default: 3 epochs. More epochs = more cost + risk of overfitting.
300K
Total Training Tokens
$X.XX
Cheapest Option
200
Training Examples
Training Cost by Model
Model Training / 1M tokens Training Cost Inference Input / 1M Inference Output / 1M

Prices as of May 2026. Fine-tuning availability and pricing changes frequently — always verify at OpenAI, Google.

Before you fine-tune — try prompt engineering first

PromptChief helps you build powerful system prompts and few-shot examples. Often achieves fine-tuning quality at zero training cost.

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Frequently Asked Questions

How much does it cost to fine-tune GPT-4o? +
Fine-tuning GPT-4o costs $25 per 1M training tokens. A typical job with 200 examples × 500 tokens × 3 epochs = 300,000 tokens = $7.50 for training. After fine-tuning, inference costs $3.75/1M input and $15/1M output tokens — more expensive than base GPT-4o.
How many training examples do I need? +
OpenAI recommends starting with 50–100 high-quality examples. You can see improvements with as few as 10 well-crafted examples. For complex tasks, 500–1,000 examples produce better results. Quality always beats quantity — diverse, representative examples outperform large low-quality datasets.
Fine-tuning vs prompt engineering — which should I choose? +
Start with prompt engineering — it's free, instant, and often achieves 80–90% of fine-tuning quality. Switch to fine-tuning when you need: consistent formatting at scale, shorter prompts to reduce inference cost, or behavior that's hard to describe in a system prompt. Tools like PromptChief help maximize prompt engineering quality before you commit to training costs.
Is fine-tuning cheaper than using a larger model? +
Often yes. Fine-tuning GPT-4o-mini to match GPT-4o quality for your specific task can be much cheaper at scale. The break-even depends on your volume. At 1M requests/month: $150 inference (GPT-4o-mini fine-tuned) vs $2,500 (GPT-4o base). The fine-tuning investment pays off quickly at volume.