Gemini Decisions API

Canonical version: Gemini Decisions API.

The Gemini Decisions API is Google's decision model endpoint, announced on 11 November 2026 by Google DeepMind. It runs a new model, Gemini Decision 1 (gemini-decision-1). You give it an input and a set of bounded questions, and instead of writing an answer token by token, it scores every option in a single forward pass and returns probabilities.

Why should you care? Because it confirms that decision models are a real category, not a one-company trick. Jev opened it in September 2026, Microsoft-Decision-1 followed in October, OpenAI has a Decisions API in beta, and now Google. As one Hacker News commenter put it: "Looks like Google announced their own Jev inspired API and model." Google's announcement doesn't name Jev, though.

How it works

• One shared input per request: text, images, audio, video and PDFs, up to 256K tokens
• Up to 1,000 independent questions about that input in one request
• Three question types:
  • noul (yes/no): the probability that a condition holds. The name follows Jev's naming ("Bernoulli")
  • choice: pick one of 2 to 255 options, with normalized probabilities for each
  • score: grade the input against an ordered rubric, with a weighted score and per-level probabilities
• Every answer comes with a confidence score from 0 to 1, so your code can send ambiguous cases to a fallback or a human

The multimodal part is what sets it apart. Commenters called it "the first decision AI that actually handles audio, video, and PDF". Grade a scanned PDF claim against damage photos, or check a call recording against account data, without paying for transcription or OCR first.

Pricing and latency

• $0.10 per million input tokens, $0 for output. No thinking tokens, no transcription or OCR fees
• The shared input is billed once per request, whatever the number of questions; each question only adds its own instruction tokens
• 150-250 ms end to end (50-100 ms of model compute), nearly flat from 1 to 100 questions per request, for inputs up to 16K tokens
• Free tier in Google AI Studio for prototyping

Google says it's nearly 5x cheaper than asking Gemini 3.5 Flash-Lite the same questions, and that it has "the lowest calibration error" on its benchmark suite. On Hacker News, people found it pricey compared with other decision models ($0.10 vs $0.02 per million tokens for one alternative). One commenter measured a Brier score about 4x lower than Jev (better calibrated) and faster p50 latency, but at twice Jev's end-to-end cost.

Where Google uses it

• Gemini Notebook: model routing, up to 40% lower latency on everyday queries
• Google Search: a safety layer that detects adversarial behavior in MCP tools
• Google Labs experiments: picking an emoji reaction among 200+ options, or a voice-driven wireframe canvas (Stitch)

Integrations are rolling out for OpenRouter, LangChain, Vercel, LiteLLM, Pydantic AI, Pipecat, LiveKit and OpenCode. Google Cloud support is "coming soon"; nothing on Vertex AI yet.

Reception on Hacker News

The thread (568 points, 161 comments) was mostly about decision models in general:

• Why did it take so long? Reading a probability out of a model is not new. Some think vendors had no incentive to sell something that cuts token use by orders of magnitude. Others say it's branding: Jev gave the idea a name
• Calibration is the whole game. "LLM token probabilities are really poorly calibrated for decision making", and a model calibrated on the vendor's data isn't automatically calibrated on yours (see AI Model Calibration)
• No reasoning. "no other tokens means I can't get reasoning or a justification"
• Opaque classifiers as a service. One commenter warned about using it for mortgage decisions and similar cases, where a confident number hides discrimination
• Trust in Google. People brought up products Google killed (Jules) and a prompt blocked by the prohibited-use policy

My take

I think this is one of the most useful things Google shipped this year. A lot of what agents do all day is deciding: which tool, is this relevant, is the agent stuck, does a human need to look at this. Doing that with a text generator is slow and wasteful. The audio, video and PDF support fills a real gap.

The usual caveat applies: measure calibration on YOUR data before you trust any threshold. And split big judgments into many small questions (Atomic Question Decomposition), which is exactly what 1,000 questions per request is made for.

References

• Google, "Gemini Decisions API: Fast, calibrated multimodal decisions" (Min Choi and Felix Yu, 2026-11-11): https://blog.google/innovation-and-ai/technology/developers-tools/gemini-decisions-api/
• Documentation: https://aistudio.google.com/docs/decisions-api
• Hacker News discussion: https://news.ycombinator.com/item?id=50456791

Related

• Decision Models (DMs)
• Jev
• Microsoft-Decision-1
• Laya
• AI Model Calibration
• Atomic Question Decomposition
• Model routing
• Gemini
• Google AI Studio

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