Google launches Gemini Decision 1, a model that answers in probabilities instead of prose

Google has a new kind of model that never writes a word. Announced on Tuesday, Gemini Decision 1 powers a new Decisions API that answers up to 1,000 bounded questions — yes or no, pick one, grade this — in a single pass, returning probabilities and confidence scores instead of text. It is available now through the Gemini API in Google AI Studio.

The pitch is speed and price: 150 to 250 milliseconds end to end, $0.10 per million input tokens, and zero output tokens billed, because there are no output tokens to bill. It is also the latest in a burst of Google releases this week — AlphaProof Code, Gemini Robotics 2.5, AlphaGenome 2 — and the community that spotted it within the hour was enthusiastic, with a caveat.

What it actually does#

Most language models generate text one token at a time, which is slow and wasteful when all you need is a verdict. Decision 1 works the other way: it scores your candidate answers in a single forward pass and hands back numbers your code can act on directly. You send one shared multimodal input — text, images, audio, video, or PDFs, up to 256,000 tokens — plus up to 1,000 independent questions about it, and each answer comes with a confidence score from 0 to 1, so uncertain cases can be routed to a fallback or a human reviewer.

There are three question types. A yes/no check estimates the probability that a condition holds. A multiple-choice question picks a winner from 2 to 255 options and returns a probability for every candidate. A rubric score grades an input against an ordered scale and returns a probability-weighted score with per-level probabilities. The questions are independent of each other: adding or removing one does not change the others’ answers.

Google’s example is deliberately mundane — a photo of a breakroom counter, checked against three questions at once: is there a spill, which cleanup action fits, and how severe is the mess. The point is that the same pattern covers tool selection, policy checks, model routing, and context grading: the plumbing decisions inside an agent, where a generated explanation would otherwise have to be parsed back into a value before the program could act.

The price and the pitch#

The pricing is the most aggressive part of the announcement. At $0.10 per million input tokens with no output charge, the shared input is billed once per request no matter how many questions you evaluate — each extra question adds only its own short instruction tokens. There are no variable thinking-token bills and no separate audio transcription or OCR costs, and a no-cost tier in AI Studio covers prototyping.

On speed, Google reports 50 to 100 milliseconds of model compute and 150 to 250 milliseconds end to end, staying nearly flat from 1 to 100 questions per request. On performance, the company says Decision 1 leads its own benchmark suite — spanning agent routing, reasoning, ranking, and multimodal understanding — with the lowest calibration error, and is nearly five times cheaper across that shared suite than Gemini 3.5 Flash-Lite with the Interactions API.

Those benchmark claims are Google’s own, and nobody outside Google has repeated them yet. The launch is also limited for now: access runs through the Gemini Developer API v1beta, and context caching, the Batch API, and Flex or Priority inference are not supported. Google Cloud support is coming soon.

Where Google is already using it#

Google says teams inside the company are already running the API as a low-latency control plane. Gemini Notebook uses it as a real-time router, classifying intent in milliseconds to serve everyday queries with up to 40% lower latency while reserving deeper reasoning models for harder ones. Stitch uses it to generate playable wireframes of a product flow in seconds and apply voice or tap edits almost instantly. CC, a Google Labs experiment, uses it to pick an emoji reaction from a palette of more than 200 options in a fraction of a second. And Google Search uses it as a safety layer for MCP tools, detecting adversarial behavior against adversarial evals.

Integrations are rolling out across OpenRouter, LangChain, Vercel, LiteLLM, Pydantic AI, Pipecat, LiveKit, and OpenCode, alongside the google-genai SDKs. That breadth matters: a decision model only becomes infrastructure if it shows up wherever agents are already built.

What Reddit made of it#

The announcement surfaced on r/singularity within the hour, and the top reaction was excitement at Google’s release pace — four major launches in two days, in one commenter’s count. But the most substantive thread pushed back on the headline numbers. Pointing to Artificial Analysis’s Decision Index, commenters argued the model is not clearly better than existing decision models such as Jev, while costing more — “more expensive than Jev for marginal improvement,” in one top comment’s words, with another noting it takes longer to finish on the same tasks.

That is the right argument to have, and it cannot be settled from Google’s blog post alone. The benchmark chart in the announcement is Google’s; the independent scores are what will tell us whether Decision 1 is a new best or a convenient one. Until those land, the honest summary is narrower than the press release: a fast, cheap way to get calibrated yes/no and multiple-choice answers out of a Gemini model, with the performance claims still to be checked.

What to watch#

First, the independent numbers. Once Artificial Analysis publishes full Decision Index scores for Decision 1, we will know whether Google’s “leads the suite” claim survives contact with a benchmark it did not design.

Second, whether “decision models” become a product category rather than a feature. Commenters were already comparing Decision 1 against rivals — one said they would still pick OpenAI’s GPT-6 Luna for decision work — and if the pricing holds, expect every lab to answer with its own decision endpoint.

And third, the routing use case. If Gemini Notebook’s 40% latency cut is real and repeatable, model routing becomes the killer application: a cheap decision model deciding, question by question, which expensive model deserves the work. That is a small job with outsized leverage — and exactly the kind of invisible infrastructure that tends to spread quietly, then everywhere.