AI微调价格对比

Fine-tuning is the process of training a pre-trained language model on your own dataset to specialize it for a specific task, domain, or output style. Instead of training from scratch, you adapt an existing model using examples of the inputs and outputs you want.

OpenAI charges $25.00 per million training tokens for GPT-4o fine-tuning. After training, inference costs $3.75 per million input tokens and $15.00 per million output tokens. You need a minimum of 10 training examples, though OpenAI recommends 50-100 for best results.

Yes, Google currently offers free training for Gemini 2.0 Flash and Gemini 1.5 Flash fine-tuning. You only pay for inference after training. However, Google requires a minimum of 100 training examples, which is higher than most other providers.

The minimum varies by provider: Together AI and Fireworks require just 1 example, Cohere requires 2, OpenAI requires 10, and Google requires 100. In practice, most fine-tuning jobs benefit from at least 50-100 high-quality examples, with diminishing returns above 1,000.

For zero training cost, Google Gemini models offer free fine-tuning. For open-source models, Together AI offers Llama 3.1 8B training at $0.48 per million tokens, making it the cheapest paid option. Fireworks also offers competitive pricing with the added benefit of self-hosting your fine-tuned model.

Use prompt engineering first -- it requires no upfront cost and is easy to iterate on. Fine-tuning makes sense when you need consistent formatting, domain-specific behavior, lower per-request latency, or when your prompts are getting too long and expensive. Fine-tuning can actually reduce inference costs by removing the need for lengthy system prompts.