Introducing improvements to the fine-tuning API and expanding our custom models program

OpenAI

Update on May 8, 2026: OpenAI is winding down the fine-tuning platform. The platform is no longer accessible to new users but existing users of the fine-tuning platform will be able to create training jobs for the coming months. All fine-tuned models will remain available for inference until their base models are deprecated⁠(opens in a new window). The full timeline is here⁠(opens in a new window).

There are a variety of techniques⁠(opens in a new window) that developers can use to increase model performance in an effort to reduce latency, improve accuracy, and reduce costs. Whether it’s extending model knowledge with retrieval-augmented generation (RAG), customizing a model’s behavior with fine-tuning, or building a custom-trained model with new domain-specific knowledge, we have developed a range of options to support our customers’ AI implementations. Today, we’re launching new features to give developers more control over fine-tuning with the API and introducing more ways to work with our team of AI experts and researchers to build custom models.

New fine-tuning API features

We launched the self-serve fine-tuning API⁠(opens in a new window) for GPT‑3.5 in August 2023. Since then, thousands of organizations have trained hundreds of thousands of models using our API. Fine-tuning can help models deeply understand content and augment a model’s existing knowledge and capabilities for a specific task. Our fine-tuning API also supports a larger volume of examples than can fit in a single prompt to achieve higher quality results while reducing cost and latency. Some of the common use cases of fine-tuning include training a model to generate better code in a particular programming language, to summarize text in a specific format, or to craft personalized content based on user behavior.

Today, we’re introducing new features⁠(opens in a new window) to give developers even more control over their fine-tuning jobs, including: