Google’s decision model scores up to 1,000 questions at once without generating text
Written by AI
Gemini Decision 1 does not write an answer. Instead, it returns probabilities for options the developer defines, at $0.10 per 1 million input tokens. In Google’s own comparisons the model came first in most tests, but TypeSafe AI’s Jev was cheaper.
Google introduced an API that picks an answer from predefined options in a blog post on 11 November. The Gemini Decisions API runs on the new Gemini Decision 1 model. Google lists uses such as choosing a model or an AI agent’s tool, and rule checks.
A conventional language model generates its answer token by token. Gemini Decision 1 scores the options a developer supplies in a single forward pass and returns a probability for each. Every response includes a confidence score between 0 and 1, so a program can route uncertain cases to a fallback system or a human.
A request can hold one input of up to 256,000 tokens and up to 1,000 questions. The input can contain text, images, audio, video and PDFs. A question can be a yes/no question (noul), a multiple-choice question with 2–255 options or a rating on an ordinal scale.
Google said median latency is 150–250 milliseconds for inputs of up to 16,000 tokens. The company said the time stays almost the same with 1 to 100 questions.
Lowest calibration error in Google’s comparison
Google compared the model with two previously released decision models, TypeSafe AI’s Jev 1.14 and OpenAI’s GPT-6 Luna, and with its own Gemini 3.5 Flash-Lite. In Google’s table, as cited by MarkTechPost, Gemini Decision 1 leads seven of nine accuracy benchmarks. In two of these (audio and video), Flash-Lite was the only comparison model. Gemini Decision 1 has the lowest calibration error. This metric shows how far a model’s stated confidence differs from its actual accuracy.
| Benchmark | Gemini Decision 1 | Jev 1.14 | GPT-6 Luna | Gemini 3.5 Flash-Lite |
|---|---|---|---|---|
| JevBench (public set) | 92.2% | 90.0% | 90.9% | 86.6% |
| Mind2Web Multimodal | 86.2% | 86.8% | 81.8% | 78.0% |
| When2Call | 79.9% | 82.0% | 70.4% | 60.9% |
| Calibration error, Top-1 ECE (lower is better) | 0.0250 | 0.0494 | 0.0721 | 0.2104 |
| Cost, 7 shared benchmarks | $0.88 | $0.68 | $0.93 | $4.14 |
Source: Google’s table, as reported by MarkTechPost. Refusals and safety blocks are counted as wrong answers.
The figures come from Google and cover benchmarks the company chose. Jev was the cheapest on the shared benchmarks. Google’s claim that the model is nearly five times cheaper refers to the comparison with Flash-Lite.
Blogger Simon Willison tested the model on a personal video. Jev and OpenAI return 1.0 or 0.0 for a yes/no question, Willison wrote, whereas Gemini gave, for example, about 0.92 for a question about whether a robot appears. MarkTechPost reported that the documentation limits the yes probability to between 0.01 and 0.99.
Price and availability
Google does not bill output tokens. The shared input is billed once per request, and each question adds only its own instruction and criteria tokens. MarkTechPost reported that there is no caching or Batch API yet, so repeated input is paid for every time. Jev costs $0.042 per 1 million input tokens.
The model cannot be downloaded to run locally, MarkTechPost said. Developers use it through Google’s Gemini API (v1beta, google-genai SDK 3.2 or later) and can try it free in Google AI Studio. Support is coming to OpenRouter, LangChain and other services, and Google said it would reach Google Cloud soon.