Google Launches Gemini Decision 1, a Model That Scores Up to 1,000 Questions in One Pass
We're introducing the Gemini Decisions API in @GoogleAIStudio, powered by our new Gemini Decision 1 model. Instead of generating a text response token by token, Gemini Decision 1 evaluates your input (across text, images, audio, video, and PDFs) against up to 1,000 questions to make fast, confident decisions in a single pass, with zero output tokens billed. Here's a look at how it works 馃У
Context
It is a new kind of Gemini model built to make fast, cheap judgments for agents and workflows instead of writing text.
Google announced the Gemini Decisions API on Nov 11, available in Google AI Studio and through the Gemini API. It runs on a new model called Gemini Decision 1, which checks text, images, audio, video and PDFs against a list of questions and returns confidence scores instead of a written answer. Google AI Studio posted the launch, and the main Google account explained how it works in a short thread.
Deep dive
Google introduced the Gemini Decisions API on Nov 11, powered by a new model it calls Gemini Decision 1. According to Google, the model does not generate a response token by token. It evaluates an input, which can be text, images, audio, video or PDFs, against up to 1,000 questions in a single pass and returns decisions with calibrated confidence scores. Google says no output tokens are billed.
In its thread, Google says the API returns results in 150 to 250 milliseconds end to end and costs $0.10 per 1M input tokens. It claims this makes it nearly 5 times more cost efficient across a shared benchmark suite than its lightest Gemini 3.5 Flash model used through the Interactions API.
The idea is that developers give the model a checklist of yes or no, multiple choice and rating questions rather than asking it to describe something. In Google's example, Gemini Decision 1 scans 100 delivery photos in 4.5 seconds, which Google says is nearly 4 times faster than that same Flash model, and answers 16 questions per photo, such as whether a box is damaged, whether it sits under a porch, a delivery quality rating from 0 to 3, and what should happen next, such as routing to a claim review.
Google AI Studio said the API is available to try now in AI Studio and the Gemini API, and it shared demo apps named Doorstep Parcels, Speed Sketch and Find the Zookeeper. The speed and cost figures come from Google and have not been independently tested.
What to expect next
Developers will now test whether the confidence scores are reliable enough to drive real decisions, such as moderation, inspection or routing inside agents. Independent comparisons against using a normal model with structured output will show whether the claimed speed and cost gains hold up. If it works, other labs may ship similar decision models, and agent builders could start splitting work between models that write and models that only judge. Watch for pricing details, rate limits and early customer case studies.