Google has announced the Gemini Decisions API, a developer-focused endpoint designed for tasks where an AI system must choose among a defined set of options rather than write a long response. Published on November 11, 2026, the announcement introduces Gemini Decision 1, a model intended for decisions such as tool selection, policy checks, model routing and context grading.
What makes a decisions API different
Many generative AI applications use a language model to produce text and then ask software to interpret that text. For a bounded task, such as choosing one tool from a list or deciding whether a request meets a rule, generating a paragraph can add latency and complexity. Google says its Decisions API scores candidate options in a single pass and returns probabilities and confidence scores, rather than decoding a long natural-language answer token by token.
The API supports three decision formats described by Google: yes-or-no checks, multiple-choice selection and ordered rubric scoring. Developers can provide up to 1,000 questions against a shared input. Google says the model can process text, images, audio, video and PDFs, with a maximum input context of 256,000 tokens. The company reports end-to-end latency of roughly 150 to 250 milliseconds and pricing of $0.10 per million input tokens, with no output-token charge. Those figures are vendor-published specifications and should be tested against a developer’s own workload.
Where developers could use it
A customer-support system might use a bounded decision to route a request to billing, technical support or a human agent. A document workflow could score whether a scanned form meets a checklist, while a multimodal application might combine a photo and a text note to select the next action. In an agent system, a decision model could help select an available tool or route an uncertain case to a stronger model or human reviewer.
The confidence score is potentially useful because it gives developers a way to set thresholds. A system could accept high-confidence routine choices while escalating ambiguous cases. However, a confidence value is not the same as a guarantee of correctness. Teams still need evaluation data, error analysis, safeguards against adversarial inputs and clear rules for when a person must review the result.
Cost and implementation considerations
Google says the shared input is billed once per request, while each question adds its own instructions and criteria. The company also says the API is available through the Gemini API in Google AI Studio, with a no-cost tier for prototyping. Developers should confirm current pricing, quotas, supported regions and API version in the official documentation before using it in production.
For teams already building AI agents, the most practical opportunity may be to separate classification and routing from open-ended generation. Not every step in an AI workflow needs a conversational model. A specialized decision endpoint could reduce unnecessary text generation in some cases, but the overall benefit depends on task accuracy, integration effort, fallback behavior and the cost of mistakes.
Source
Google: Gemini Decisions API — Fast, calibrated multimodal decisions (November 11, 2026)
Image credit: Luke Chesser / Unsplash. Used as an illustrative analytics image.
