Google DeepMind launches Gemini Decisions API with 150-250ms latency, single-pass probability scoring

Authors:

• Min Choi, Product Manager, Google DeepMind
• Felix Yu, Principal Scientist, Google DeepMind

Overview

Google is introducing the Gemini Decisions API, a new endpoint for fast, closed-set decisions powered by the new Gemini Decision 1 model (gemini-decision-1), available through the Gemini API in Google AI Studio.

The Decisions API provides purpose-built primitives for bounded choices such as tool selection, policy checks, model routing, and context grading. Users can pass one multimodal input across text, images, audio, video, and PDFs (up to 256K tokens) along with up to 1,000 independent questions. Rather than generating answer text token by token, Gemini Decision 1 scores candidate options in a single forward pass, returning probabilities and confidence scores in 150–250 ms end-to-end latency at $0.10 per 1M input tokens with zero output tokens billed.

Strong Performance and Calibration

On benchmarks spanning agent routing, reasoning, ranking, and multimodal understanding, Gemini Decision 1 leads across the suite, with the lowest calibration error.

Gemini Decision 1 scores options without token-by-token decoding, keeping median latency at 50–100 ms of model compute (150–250 ms end-to-end) across multimodal inputs and contexts up to 16K tokens, remaining nearly flat from 1 to 100 questions per request. With zero output tokens billed, Gemini Decision 1 with the Decisions API is nearly 5x cheaper across the shared benchmark suite than Gemini 3.5 Flash-Lite with the Interactions API.

Native Multimodal Evaluation Across Three Decision Types in One Pass

Built on Gemini's native multimodal architecture, Gemini Decision 1 can process text, images, audio, video, and PDFs directly in a single forward pass. Modalities can be combined in one shared input—such as pairing a call recording with account metadata, or a scanned PDF claim with damage photos—and resolution on image and video inputs can be tuned to control visual detail and token cost.

Multiple questions can be evaluated against that shared input in a single request, mixing yes/no checks, multiple choice, and rubric scores. Every answer includes a confidence score (from 0 to 1) so code can route ambiguous cases to a fallback or human reviewer:

• `noul` (Yes/No): Estimates the probability that a yes/no condition holds, with optional true and false description criteria.
• `choice` (Multiple Choice): Selects from 2 to 255 choices, returning the winner and normalized probabilities for every candidate.
• `score` (Ordered Rubric): Grades inputs across an ordered rubric (starting at 0), returning a continuous weighted score and per-level probabilities.

The input field uses the same Content block format as the Interactions API. Below is an example that checks a photo of a breakroom counter, together with a text note, against all three question types in one request:

from google import genai

client = genai.Client(http_options={"api_version": "v1beta"})

result = client.decisions.create(
    model="gemini-decision-1",
    input=[{
            "type": "image",
            "uri": "https://storage.googleapis.com/generativeai-downloads/images/spill.jpg",
            "mime_type": "image/jpeg",
        },
        {
            "type": "text",
            "text": "Breakroom counter check before the 9 AM client meeting.",
        },
    ],
    questions={
        "has_spill": {
            "type": "noul",
            "instructions": "Is there an uncleaned liquid spill on the counter?",
        },
        "next_action": {
            "type": "choice",
            "instructions": "Which cleanup action fits the scene?",
            "criteria": {
                "wipe_with_cloth": "Small counter spill with a cloth or towel already nearby.",
                "dispatch_janitorial": "Large floor hazard, broken glass, or active leak.",
                "ready_for_meeting": "Counter is clean and ready.",
            },
        },
        "severity": {
            "type": "score",
            "instructions": "Rate the severity of the counter mess.",
            "criteria": [
                "Clean and ready.",
                "Minor localized spill.",
                "Major spill or safety hazard.",
            ],
        },
    },
)

How Teams Are Building with the Decisions API

Teams across Google are already using the Decisions API as a low-latency control plane, shipping features simply by defining options rather than training custom classifiers or waiting on generative calls:

• Dynamic model routing in Gemini Notebook: Gemini Notebook uses the Decisions API as a real-time router to instantly direct incoming queries to the optimal model tier and thinking level for each request. The Decisions API classifies intent in milliseconds to serve everyday queries with up to 40% lower latency while reserving deep reasoning models for complex tasks.
• Rapid design iteration in Stitch: The new rapid canvas experiment in Stitch uses the Decisions API to generate playable wireframes of a product flow in seconds and to apply edits made by voice or tap almost instantly. When the flow feels right, one click hands the screens off to the full Stitch experience for deeper design polish.
• Emoji selection in CC: CC from Google Labs uses the Decisions API to pick the right emoji reaction from a palette of more than 200 options in a fraction of a second, so reactions appear instantly without adding latency.
• Safety layer for MCP tools in Search: Google Search uses the Decisions API to detect adversarial behavior and content against adversarial evals with high efficacy within the latency envelope.

Stitch Rapid uses the Decisions API to design an entire product flow in seconds (1x speed).

Start Building Today

Gemini Decision 1 is priced at $0.10 per 1M input tokens and $0 for output tokens, with an API no-cost tier available in Google AI Studio for prototyping. The shared input is billed once per request no matter how many questions are evaluated (each question adds only its own instruction and criteria tokens). There are no output-token charges, variable thinking-token bills, or separate audio transcription and OCR costs.

To get started:

• Grab an API key in Google AI Studio.
• Try the Doorstep Parcels, Speed Sketch, and Find the Zoo Keeper demo apps.
• Follow the Python, JavaScript, and REST guides in the Decisions API documentation.

Ecosystem integrations are rolling out across OpenRouter, LangChain, Vercel, LiteLLM, Pydantic AI, Pipecat, LiveKit, and OpenCode alongside the google-genai SDKs, with support coming soon to Gemini in Google Cloud.