OpenAI launches Decisions API for near-instant AI responses

A shift toward speed

OpenAI has officially released its Decisions API to all customers, providing a specialized way for developers to handle rapid-fire AI tasks. While standard models are often used to generate long, conversational text, this new tool is built for speed and efficiency. It is designed specifically for applications that require near-instant results from an artificial intelligence system.

The company first introduced this technology at a developer event in late September before making it widely available on October 22. By focusing on specific types of outputs, OpenAI aims to provide a more practical solution for high-frequency tasks. This release marks a move toward making AI more responsive in environments where a human user cannot wait for a full sentence to be typed out.

As reported by ProPakistani, the tool is significantly faster than the traditional Responses API. It can make decisions up to 10 times faster than the underlying model would normally perform. This speed is critical for developers who need to integrate AI into systems that demand immediate feedback or automated actions.

How the API functions

The Decisions API works differently than standard large language models, which are known as LLMs. Instead of creating complex paragraphs, it selects answers from a set of options provided by the developer. This allows the model to perform specific functions like classifying information, choosing between different actions, or calculating confidence scores.

Developers can provide the model with a question and a list of possible answers to choose from. The API supports three distinct types of responses to handle these needs. These include probability estimates, simple choices between options, and scores based on predefined criteria set by the user.

To achieve these results, OpenAI did not build an entirely new model from scratch. Instead, they modified their existing language model to reduce the time and cost required for processing. This modification allows the system to skip the heavy lifting of creative generation in favor of rapid selection and classification.

Performance and early use

The primary goal of the Decisions API is to lower the barrier for real-time AI. Nikunj Handa, the API product lead at OpenAI, noted that the company reduced response times from one to three seconds to approximately 100–180 milliseconds. This represents a massive leap in performance for developers who need to maintain a fluid user experience.

Several prominent companies have already begun using the tool in their production environments. These early customers include Box, Datadog, Ramp, and Speak. These organizations are likely using the API to handle repetitive decisions that require high accuracy but do not need the nuance of a full conversation.

The language-learning platform Speak provides a clear example of this performance boost in action. Their CTO, Andrew Hsu, stated that the platform reduced the time between AI actions from 1.4 seconds to 0.13 seconds. This improvement of more than 90% shows how the API can make interactive software feel much more natural to the end user.

The competitive landscape

OpenAI is not the only company looking to optimize AI for fast decision-making. The release puts them in direct competition with the startup TypeSafe, which created the Jev decision-making model. TypeSafe has already reached a valuation of approximately $7.6 billion and has expanded its model to include image support and conditional features.

Other players in the space, such as Liquid and Base Lab, have also introduced competing models. These companies are all racing to provide systems that can handle repetitive decisions without the high costs associated with traditional models. This competition is driving a trend toward specialized, high-speed AI architecture.

The demand for these models is growing as businesses seek to automate more complex workflows. By offering a cheaper and faster way to process choices, OpenAI is positioning itself to capture a large portion of the market for automated systems. This includes everything from image classification to interactive software components.

The future of real-time AI

OpenAI believes that the technology will continue to improve as they refine the underlying models. Handa suggested that future improvements could potentially reduce response times to approximately 60 milliseconds. At those speeds, the AI could be used to make decisions for individual frames in videos, which would allow for much more dynamic content.

The broader goal for these faster models is to support a wide variety of everyday applications. This includes real-time voice assistants that need to respond without awkward pauses, as well as automated workflows that require instant logic. It also opens the door for more sophisticated interactive software that reacts to user input in real-time.

As the technology matures, the distinction between human-like interaction and automated decision-making will continue to blur. Developers will have more tools to build systems that feel instantaneous. This shift ensures that AI can be integrated into more parts of our digital lives where speed is just as important as the quality of the information provided.