Model Distillation in the API

OpenAI has introduced model distillation as a new feature within its API, allowing developers to fine-tune smaller, more cost-effective models using outputs from larger frontier models like GPT-4o, all within the OpenAI platform. According to OpenAI’s blog, this capability enables users to train a distilled model—such as GPT-4o mini or GPT-4o—by generating training data from a teacher model, then fine-tuning a student model on that data. The entire process is handled through the API, eliminating the need for external data pipelines or manual data curation. This is significant for builders who want to achieve high performance at lower cost and latency for specific tasks, such as classification, structured extraction, or customer support. By distilling knowledge from powerful but expensive models, developers can deploy custom models that are both efficient and accurate. The feature also integrates with existing fine-tuning workflows, making it a practical addition for anyone optimizing AI costs without sacrificing quality.