Google launched the Gemini Decisions API on November 11. The model powering it — Gemini Decision 1 — cannot write a sentence. It scores options. That is not a limitation; it is the design. Send it a document and up to 1,000 yes/no, multiple-choice, or rubric questions, and it returns calibrated probabilities for each. No output tokens. No prose. No parsing. Just numbers that tell you what the model thinks is true.
What Gemini Decision 1 Actually Does
Most classification pipelines today use a generative model: prompt it to pick option A, B, or C, then parse whatever it writes back. It works until it doesn’t — the model decides to explain itself, adds caveats, or returns something that breaks your regex.
Gemini Decision 1 takes a different path. It is a discriminative model, not a generative one. Rather than producing tokens one at a time, it scores all candidate answers in a single forward pass and returns probabilities. The output token count is literally zero. The model is architecturally incapable of generating text.
The practical result: 150–250ms end-to-end latency for decisions across text, images, audio, video, and PDFs — all in a single request.
Three Question Types
Every request to the Decisions API shares a single context — your document, image, or audio clip — across a batch of questions. That shared input is billed once, regardless of how many questions you attach. Google supports three question types:
- Yes/No (noul): Returns a probability that a condition is met. Use it for policy checks, vulnerability detection, or relevance filtering.
- Select (choice): Pick from 2 to 255 options with a normalized probability per option. Use it for routing — which team, which tool, which model.
- Rubric (score): Rate on an ordered scale up to 255 levels. Use it for urgency triage or quality grading.
A support ticket routing example using the Python SDK:
from google import genai
client = genai.Client(http_options={"api_version": "v1beta"})
response = client.decisions.decide(
model="gemini-decision-1",
input={"text": "Customer complaint: my order never arrived after 3 weeks"},
questions=[
{"type": "noul", "question": "Is this complaint urgent?"},
{"type": "choice", "question": "Route to which team?",
"options": ["billing", "shipping", "general_support"]},
{"type": "score", "question": "Urgency level", "max_score": 5}
]
)
Three routing decisions in one API call, against one shared input, with zero output tokens billed.
Pricing and the OpenAI Comparison
Google prices Gemini Decision 1 at $0.10 per million input tokens — the same number OpenAI charges for their own Decisions API, which launched on October 22. The more relevant comparison is what you would pay using a generative model for the same task. Classification output from Gemini 4 Hydrogen is roughly 15 times more expensive once you account for the output tokens a generative model burns to explain itself before giving you the label you actually wanted.
The two Decisions APIs differ on ceiling and modality. Google supports 1,000 questions per call and accepts audio and video. OpenAI tops out at 400 questions and does not accept audio or video. OpenAI’s version is generally available; Google’s is v1beta. That maturity gap is real — treat the beta label seriously if you are building something production-critical.
When to Use It — and When Not To
The Decisions API is the right tool when your output is a label, not a paragraph. Agent tool selection, content moderation checks, document routing, relevance scoring — these are all bounded decisions the API handles faster and more cheaply than a generative model.
It is the wrong tool when you need extracted values, multi-field output, or any free text. The model cannot write. If a human or downstream system needs to read a sentence, use a generative model with structured outputs instead.
One developer on DEV.to put it plainly: the Decisions API makes Gemini “a control plane, not a chatbot.” This is infrastructure for agents — a fast, cheap decision layer that sits in front of your more expensive generative calls.
Availability
The Gemini Decisions API is live now in Google AI Studio and via the Gemini API (v1beta). Google Cloud (Vertex AI) support is listed as coming soon. A free tier is available for prototyping. The required SDK is google-genai >= 3.2.0 with the v1beta API version.
If you are building multi-step agents with tool routing or content pipelines with classification steps, this is worth evaluating. The 1,000-question ceiling means you can run an entire document through a full rubric in a single call that costs fractions of a cent.
