OpenAI recently launched a prediction output function for the GPT-4o model. This function jointly developed with FactoryAI can significantly improve the model response speed, up to 5 times the original speed. This feature reduces duplication by identifying and reusing predictable content parts, and is particularly effective in tasks such as code refactoring and blog updates. The editor of Downcodes will explain in detail the advantages, limitations and usage costs of this new feature.
OpenAI recently launched an important update, introducing the Predicted Outputs function to the GPT-4o model. This innovative technology significantly improves the response speed of the model, which can be up to 5 times the original speed in certain scenarios, bringing a new efficiency experience to developers.
The core advantage of this feature, jointly developed by OpenAI and FactoryAI, is that it can bypass the duplication process of generating known content. Excellent in real-world applications, especially on tasks like updating blog posts, iterating on existing replies, or rewriting code. According to data provided by FactoryAI, in programming tasks, the response time has been shortened by 2 to 4 times, compressing a task that originally took 70 seconds to complete within 20 seconds.
Currently, this function is only open to developers through API and supports GPT-4o and GPT-4mini models. Feedback from actual use has been positive, and many developers have started testing and sharing their experience. Eric Ciarla, the founder of Firecrawl, said when converting SEO content: the speed has been significantly improved and the usage is simple and direct.
Technically, predictive output works by identifying and reusing predictable portions of content. OpenAI official documents give examples that in scenarios such as code refactoring, such as when changing the Username attribute in C# code to Email, the generation speed can be greatly improved by inputting the entire class file as predicted text.
However, this feature comes with some limitations and caveats. In addition to the limitations of model support, certain API parameters are not available when using prediction output, including n values greater than 1, logprobs, and presence_penalty and frequency_penalty greater than 0.
It is worth noting that this feature, while providing faster response times, also brings a slight cost increase. According to user test data, after using the predictive output function for the same task, although the processing time was reduced from 5.2 seconds to 3.3 seconds, the cost increased from 0.1555 cents to 0.2675 cents. This is because OpenAI also charges the completion tokens rate for the non-final completion tokens provided during prediction.
Although the cost has increased slightly, this feature still has considerable application value considering the significant efficiency improvement. Developers can obtain more detailed technical instructions and usage guides through the OpenAI official documentation.
OpenAI official documentation:
https://platform.openai.com/docs/guides/latency-optimization#use-predicted-outputs
All in all, OpenAI's prediction output function provides developers with significant efficiency gains, and despite some usage restrictions and increased costs, the speed improvements it brings are still worth paying attention to. The editor of Downcodes recommends that developers evaluate the value of their applications based on their own needs.