Gemini Decision 1: Google's AI that doesn't write, it just decides

What's going on

On November 11, 2026, Google unveiled the Gemini Decisions API, powered by a new model called Gemini Decision 1 (in code: gemini-decision-1). The twist: this model doesn't write text. It only answers questions with a fixed set of answers, like yes or no, option A, B or C, or a rating from 1 to 5, and it attaches probabilities to them. According to a post on r/GeminiAI, it's Google's first so-called "decision model".

The API is available through the Gemini Developer API (version v1beta) and in Google AI Studio, Google's developer platform. There's a free tier there if you want to try it. According to Marktechpost, there are no open weights and no way to run the model yourself. It only runs on Google's servers.

It's aimed at developers building AI agents, meaning programs that use AI to work through tasks on their own. Agents like these constantly have to make small decisions: Which tool should I use? Does this request violate a policy? Is a small model enough, or do I need a big one? Is this document even relevant? Until now, developers have often asked a regular language model, which writes out an answer word by word that the program then has to laboriously parse. Cryptobriefing sums it up: Google doesn't want its AI writing essays when a simple answer will do.

How Gemini Decision 1 works

A request has three parts: the model ID, a shared input and a list of questions. The input can include text, images, audio, video and PDFs, which the model reads in a single pass. The context window, meaning the maximum amount of information the model can process at once, covers 262,144 tokens (roughly 256,000 word or subword chunks) for the input and all questions combined.

There are three question types:

• Yes/no (called "noul" in the documentation): returns the probability of "yes", clamped between 0.01 and 0.99.
• Choice: picks one of 2 to 255 options and returns a probability for each option.
• Score: places the input on a rating scale with 2 to 255 levels and returns a probability-weighted value.

Every answer also includes a confidence value between 0 and 1, the estimated probability that the most likely answer is correct. Google recommends routing uncertain cases to a fallback system or a human. According to Unite.AI, developers should set those thresholds using their own human-labeled examples, and for high-stakes actions like payouts, a human or a second check should always be involved.

Under the hood, the difference is that the model scores the possible answers in a single computation (a forward pass) instead of generating text token by token. All questions are answered in parallel and can't see each other: adding or removing a question doesn't change the other answers. If one decision depends on another, that logic has to live in your own code, for example with a second request.

A key selling point is calibration: a well-calibrated model that says it's 90 percent sure should be right about nine times out of ten. That matters when a program has to decide automatically whether to trust an answer.

Speed and pricing

Google cites a median pure compute time of 50 to 100 milliseconds, or 150 to 250 milliseconds including network transfer. That applies to inputs up to 16,000 tokens and stays almost the same whether you ask one question or 100.

Reports on pricing conflict: Cryptobriefing writes that Google didn't announce prices at launch. Marktechpost and Unite.AI, on the other hand, both report $0.10 per million input tokens, with output tokens free because there aren't any. The shared input is billed only once per request, no matter how many questions you ask; each question only costs its own instruction tokens. There are no charges for "thinking tokens", audio transcription or text recognition in images (OCR). Across Google's benchmark suite, the model is said to be almost five times cheaper than Gemini 3.5 Flash-Lite ($0.88 vs. $4.14).

Limitations at launch: there's no prompt caching (storing repeated inputs for reuse at reduced cost), no Batch API for bulk jobs, and no Flex or Priority tiers. Questions can only contain text, and conversation history and tool calls aren't supported. You need the google-genai SDK version 3.2 or later.

Benchmarks compared

Google tested its model against three competitors: TypeSafe Jev 1.14, GPT-6 Luna via OpenAI's Decisions API, and its own Gemini 3.5 Flash-Lite. All numbers come from Google itself; refusals and safety blocks count as wrong.

The clearest lead is in calibration: the error is about half that of Jev 1.14. On Mind2Web and When2Call (recognizing when a tool should be called), Jev comes out ahead. And Jev is cheaper: $0.042 per million tokens and $0.68 across the shared test suite. According to Marktechpost, GPT-6 Luna offers a much larger context window (1.05 million tokens) and caching, but only allows up to 400 questions per request and handles text, images and JSON, but not audio or video.

Use at Google and integrations

Google is already using the model itself. Gemini Notebook (formerly NotebookLM) uses it to decide in real time which model tier and thinking depth a request gets, cutting wait times for everyday questions by up to 40 percent, according to Google. The Labs agent CC uses it to pick from more than 200 emoji reactions, and the Stitch experiment uses it to build playable wireframes (rough screen mockups) in seconds.

According to Google, support is rolling out in OpenRouter, LangChain, Vercel AI SDK, LiteLLM, Pydantic AI, Pipecat, LiveKit and OpenCode. For Gemini in Google Cloud, it's announced as "coming soon".

The big picture

Decision models are a new, fast-growing product category: OpenAI's Decisions API entered public beta on October 6, 2026, and Microsoft followed on October 9 with Microsoft-Decision-1. That means three of the biggest AI providers have entered this niche within about five weeks. End users won't notice, but for developers it matters: the many small decisions inside agents, moderation and compliance checks, or filtering out irrelevant documents could get faster and cheaper, leaving expensive large models to work only where text is actually needed.

According to the reports, Google's strengths are native audio and video processing and strong calibration. The catch: all benchmarks come from Google itself, and independent measurements are still missing. On price, Google isn't the cheapest option, and the lack of caching makes repeated inputs more expensive than they need to be. As Unite.AI points out, despite good probability scores, responsibility for safe automation still lies with developers: with their criteria, test data and escalation rules.