OpenAI Dev Day 2023

GPT-3.5 Turbo Fine-Tuning Pricing Overview

GPT-3.5 Turbo 4K Fine-Tuning

• Cost for 1,000 input tokens:

  • 3x less
  • $0.012

  Thoughts/Ideas:

  • This indicates a significant cost reduction, making it more affordable for users to input data for fine-tuning. The reduction by three times can suggest optimization in processing or scaling efforts.
  • Fine-tuning with more tokens could potentially lead to higher precision and better accuracy for specific tasks.

• Cost for 1,000 output tokens:

  • 2x less
  • $0.016

  Thoughts/Ideas:

  • Output tokens generally reflect the model's generative capacity. A reduction in cost by two times is a practical benefit for users who rely on generating extensive outputs, such as in content creation or detailed response generation.

GPT-3.5 Turbo 16K Fine-Tuning

• Cost for 1,000 input tokens:

  • 4x less
  • $0.003

  Thoughts/Ideas:

  • An even lower cost for input tokens compared to the 4K fine-tuning signifies a more optimized model for handling high-volume data inputs. This could be particularly advantageous for large-scale data integration or enterprise-level applications.

• Cost for 1,000 output tokens:

  • 2.7x less
  • $0.006

  Thoughts/Ideas:

  • The greater reduction for output tokens in the 16K model indicates a model designed for more efficient text generation. This could mean better handling of complex queries, richer outputs, and improved performance for applications requiring detailed response generation.

Summary of Pricing Information

Additional Information:

• The usage of "4K" and "16K" seems to refer to different fine-tuning configurations or capacities, potentially representing the maximum token limits or specific batch sizes used during the fine-tuning process.
• Cost reductions signify improved efficiencies, which could impact both small-scale and large-scale use cases, encouraging broader adoption and facilitating diverse applications.

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