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Large Language ModelsAI Business & IndustrySimon Willison's WeblogPublished: Nov 12, 2026, 10:01 JST

Google launches multimodal Gemini Decision 1 API

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Google launches multimodal Gemini Decision 1 API

3 Key Points

  1. What happened

    Google released Gemini Decision 1, a decision model API handling the same yes/no, choice, and rubric-scoring questions as Jev and OpenAI, adding text, images, audio, video, and PDFs.

  2. Why it matters

    Gemini Decision 1 answers yes/no questions with probability scores rather than the 1.0 or 0.0 returned by Jev and OpenAI, so callers get graded confidence instead of a hard yes or no.

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Context & Analysis

The decision model category now has three named entrants: Jev, OpenAI, and Google. Gemini Decision 1 copies the question vocabulary directly, reusing the term "Noul" for a yes/no question, which suggests the interfaces are converging on a shared shape. What differs is the input surface: Jev is text and JSON only, OpenAI handles text and images, and Gemini Decision 1 covers text, images, audio, video, and PDFs. In a test on a video of a stomping robot in Yonkers, the model returned probabilities for four yes/no questions, including 0.9234 for a robot and 0.9230 for the USA. Jev and OpenAI return exact 1.0 or 0.0 values for the same style of question, so the practical difference is whether a caller receives a graded confidence or a hard label, a distinction that may matter in pipelines where downstream logic needs to know how sure the answer is.

FAQ
What types of questions can Gemini Decision 1 answer?
It supports the same three types as Jev: yes/no, choice, and rubric-scoring, and reuses Jev's term 'Noul' for a yes/no question.
How does its yes/no output differ from Jev and OpenAI?
Jev and OpenAI return 1.0 or 0.0, while Gemini Decision 1 returns a probability such as 0.9234 for a robot question.
How does Gemini Decision 1 compare on price?
It costs 10 cents per million input tokens with free output, the same as GPT-6 Luna, while Jev costs 4.2 cents per million.
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Also reported by Latent Space, THE DECODER

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