Fine-Tuning Cost Calculator
Calculate the one-time training cost and ongoing inference cost of fine-tuning a language model for your use case.
Tip: 1 example ≈ 300–2,000 tokens
OpenAI default: 3 epochs
Training cost (one-time)
$0.00
Daily inference cost
$0.00
Monthly inference cost
$0.00
Break-even vs base model
0 days
Total cost (1 year)
$0.00
LLM Fine-Tuning Cost Calculator
Fine-tuning a large language model involves two cost phases: a one-time training cost based on dataset size and epochs, and an ongoing inference cost for using the fine-tuned model in production. This calculator helps you plan both.
OpenAI Fine-Tuning Pricing (May 2026)
| Model | Training (per 1M tokens) | Inference Input | Inference Output |
|---|---|---|---|
| GPT-4o-mini | $3.00 | $0.30 | $1.20 |
| GPT-3.5 Turbo | $8.00 | $3.00 | $6.00 |
| GPT-4o | $25.00 | $3.75 | $15.00 |
When Is Fine-Tuning Worth It?
- When prompt engineering alone can't achieve the required output format or style.
- When you need to reduce prompt length (and cost) by baking context into the model weights.
- When you have 500+ high-quality training examples in your specific domain.
- When latency matters — fine-tuned models can be smaller and faster than few-shot prompting a large model.
Alternatives to Consider
- Few-shot prompting — often achieves 90% of fine-tuning quality at zero training cost.
- Retrieval-Augmented Generation (RAG) — inject domain knowledge at inference time without training.
- Open-source models — fine-tune LLaMA or Mistral on your own hardware for one-time compute cost.
Related Tools
- Embedding Cost Calculator — calculate the cost to embed your training dataset
- RAG Pipeline Cost Calculator — compare RAG as an alternative to fine-tuning
- Monthly API Budget Planner — plan ongoing inference costs post fine-tuning
- Multi-Model Cost Comparison — compare base model costs before fine-tuning