In a customer service process, what is often the most time-consuming is not necessarily writing a long response. The system first needs to determine whether the user is checking a bill, updating an address, or complaining about an order; then it decides which tool to use and whether to transfer the case to a human operator. Such judgments are brief, but they may be repeated dozens of times in each interaction. On October 22nd, OpenAI announced that Decisions API had ended its public testing and was now officially available, targeting precisely these types of micro-decisions where the range of possible answers is predetermined in advance. It is not another free-form writing chat model; instead, it allows developers to provide a question, candidate answers, and context, and then it returns probabilities, options, or scores.

According to the official statement, this interface, which is based on GPT-6 and Luna, can process applicable tasks up to ten times faster than the same model processed through Responses API. The key terms here are "applicable tasks" and "up to ten times faster": these do not represent a general performance ranking for all types of open-ended tasks. Decisions API sacrifices long, freely generated outputs in exchange for structured results and a shorter decision-making process. The product update also includes native integration with GPT-Live, a repeat context caching mechanism, options for inference residency in the United States and the European Union, and an increase in the maximum number of questions per request to 400. For teams that want their agents to respond in real-time, these improvements are more concrete than a general model evaluation.

Shift from generating growth answers to making constrained judgments

OpenAI provides three types of results: predicate estimates the probability that a certain condition is met, choice allows selection from predefined options set by developers, and score returns a weighted score based on grading criteria. For example, in order processing, choice allows for choices between "automatic refund," "request for additional information," and "manual review"; predicate can estimate whether the uploaded product images are damaged; score can assess the urgency of a complaint. In all cases, it is required that the business party clearly define the boundaries of the questions and answers in advance. If the candidate options themselves overlook the complex realities of the real world, no matter how fast the interface outputs, it cannot compensate for this business design error.

This division of labor also explains why it is attractive for real-time agents. A voice assistant, which continuously receives transcription fragments as the user speaks, may need to decide in a timely manner whether to follow up, interrupt, or hand over the task to another module. If it had to wait for the generation of each long piece of text, the end-to-end experience would be slowed down by numerous small delays. The official example shows how the transcription event from Decisions to GPT-Live is processed to select the appropriate response from the virtual avatar based on what the user says next. That is an application example of the interface, but it does not mean that all voice products have automatically achieved the same speed or quality.

OpenAI lists customer cases such as Box, Datadog, Ramp, Sierra, and Speak. The test scenarios for these cases vary greatly: some involve document routing, some command risk scoring, and others concern the next steps in real-time conversations. A client reported a five-fold increase in speed, while another client reported a forty-fold reduction in end-delay; both should be understood within the context of their respective limited test scopes and cannot be generalized into a universal promise. Especially in businesses that involve manual review and security backups, what truly matters is the total time from input to the completion of the correct action, rather than the milliseconds it takes for a single interface response.

This interface also allows for the input of native JSON, text, and images. For developers, JSON is not just about having a better-looking output format; it eliminates the need for the model to generate natural language first, which is then parsed by the program. The image capability provides a more direct form of input for quality inspection or sorting tasks. However, visual judgment can still be affected by factors such as shooting angle, lighting, and sample distribution. If a blurry photo is considered conclusive evidence by the system, this may actually lead to errors being quickly propagated through subsequent processes. A reasonable approach to integration is to first identify tasks with low risk and closed answers, and then set confidence thresholds and conditions for manual review.

Lower unit prices do not equal lower business costs.

The price listed in the announcement is $0.10 per million inputs of token, and $0.01 per million inputs for cached data of token; this interface does not incur any token fees. At first glance, the pricing seems suitable for high-frequency categorization and routing. However, the budget still depends on how much context needs to be carried with each decision, whether the data can be cached effectively, how much manual rework may result from incorrect decisions, and how many additional calls the system requires to obtain sufficient information. If a complete segment of customer history is included in each input, even a low nominal unit price may not be enough to compensate for the increased context overhead. The prices and regional availability should also be based on the latest settings in the actual account's console.

Developers also need to break down and measure what is "correct" in a more detailed way. A classifier that appears stable in terms of overall accuracy may consistently make mistakes in a small number of high-value or high-risk categories; even if the average latency is good, the latency at peak times could still cause lag in live voice transmissions. During evaluation, it is important to record the proportions of incorrect selections, missed selections, rejections, and cases that are referred to human operators. Additionally, observations should be made by grouping different languages, image qualities, and abnormal inputs. Even if the output is based on probability, it cannot be directly used as the true risk probability after business calibration unless it has been calibrated and retested on a target sample set.

For enterprises, data control is also a procurement issue. OpenAI claims to offer regional processing for the United States and the European Union, and eligible customers can also apply for zero data retention. However, neither of these options is default for global coverage or all accounts. Who has access to the logs, whether personal information should be classified, and for how long it should be retained must still be confirmed on a case-by-case basis according to deployment and contract terms. If risk commands, financial transactions, or customer complaints are entrusted to automated decision-making modules, business teams must still maintain traceable versions of the rules and manual revocation channels.

The real innovation of Decisions API is to break down the "what to do next" information, which was previously buried within the long responses of large models, into independent and measurable components. It is now officially available, but the official availability of a single interface does not mean that any business processes have been verified. First, it is necessary to identify a high-frequency, low-risk, and clearly defined point of judgment, and then use our own samples to compare them with existing rules or models to determine whether this improvement in efficiency will indeed lead to a real enhancement in user experience.

Source: OpenAI Announcement on October 22, 2026 https :// openai.com / index / decisions-api / ; OpenAI Development Documentation https :// developers.openai.com / api / docs / guides / decisions