Fine-Tuning Cost Calculator — Estimate Model Training Expenses

Use this free Fine-Tuning Cost Calculator to estimate how much it will cost to fine-tune a large language model (LLM) on your own data. Simply enter your dataset size (in tokens or examples), number of training epochs, and the model you plan to fine-tune — this tool calculates the expected compute cost based on current pricing for fine-tuning jobs. Fine-tuning lets you tailor AI models to your specific data or tasks, but costs can vary significantly depending on model size and usage. This calculator helps developers, AI engineers, and product teams plan and budget fine-tuning costs before they start training.

OpenAI currently supports fine-tuning for these models. Claude fine-tuning available on request.
Min 50 examples recommended
Input + output combined
Default: 3 epochs
Input + output

Fine-tuning Pricing (OpenAI)

Model Training Cost Input Cost Output Cost
GPT-4o Mini $3.00 / 1M tokens $0.30 / 1M tokens $1.20 / 1M tokens
GPT-4o Mini (base) - $0.15 / 1M tokens $0.60 / 1M tokens
GPT-3.5 Turbo $8.00 / 1M tokens $3.00 / 1M tokens $6.00 / 1M tokens
GPT-3.5 Turbo (base) - $0.50 / 1M tokens $1.50 / 1M tokens

Note: Fine-tuned models have 2-6× higher inference costs than base models. The performance improvement must justify this cost.

Should You Fine-tune?

✅ Fine-tune When:

  • You have >1000 high-quality training examples
  • Base model prompt engineering isn't good enough
  • You need consistent output formatting
  • Prompt context is too large (want shorter prompts)
  • You're making >10K requests per month
  • Task requires domain-specific knowledge

❌ Don't Fine-tune When:

  • You have <100 training examples
  • Prompt engineering gives good results
  • Low usage volume (<1K requests/month)
  • Task changes frequently
  • Budget is very constrained

💡 Alternatives to Consider:

  • Few-shot prompting: Include examples in prompt
  • RAG (Retrieval): Use embeddings + vector DB
  • Prompt optimization: Better instructions
  • Larger base model: Sometimes GPT-4o > fine-tuned 3.5

Common Fine-tuning Use Cases

Customer Support Automation
Fine-tune on past tickets for consistent tone and responses
Code Generation for Specific Framework
Train on your codebase style and patterns
Content Moderation
Classify content according to your specific guidelines
Entity Extraction
Extract domain-specific entities from text
Style Transfer
Convert text to match your brand voice

Frequently Asked Questions

How much does it cost to fine-tune GPT-4?

OpenAI doesn't currently offer GPT-4 fine-tuning. You can fine-tune GPT-4o Mini ($3/1M training tokens) or GPT-3.5 Turbo ($8/1M training tokens). For 1,000 examples with 500 tokens each over 3 epochs, fine-tuning GPT-4o Mini costs about $4.50, while GPT-3.5 costs $12.

Is fine-tuning worth it?

Fine-tuning is worth it if you have 1000+ quality examples, prompt engineering isn't sufficient, you need consistent formatting, and you're making 10K+ monthly requests.

Does this estimate include all charges?

This tool estimates compute charges based on model pricing for fine-tuning. Actual provider bills may also include storage, data transfer, or platform fees.