Gemini 决策 API
Gemini Decisions API

原始链接: https://blog.google/innovation-and-ai/technology/developers-tools/gemini-decisions-api/

Gemini Decision 1 是一款低延迟、高成本效益的多模态决策 API,可在单次前向传播中为选项评分,无需逐 token 解码。它支持文本、图像、音频、视频和 PDF,最多 16K token,1–100 个问题的延迟几乎保持不变,且不计输出 token,因此比同类 API 便宜很多。它支持是/否、多选题和量规评分,并提供置信度分数用于路由。

Google 推出了 Gemini Decisions API,与 Jev 和 OpenAI 一同提供专用的决策/分类模型。HN 用户争论这是否来得过晚,指出此类模型在 yes/no 选择、路由、过滤和评判方面可能比 LLM 更便宜、更快,同时提供校准后的概率。评论者强调 Gemini 的多模态支持(音频、视频、PDF),但质疑其在新任务上的校准、基准测试声明、成本、延迟以及 Google 的长期支持。一些人将其与旧分类器/NLI 比较,并讨论将决策模型用作门控或评分标准。总体而言,该讨论反映人们对更便宜、结构化 AI 决策的热情,以及对准确性、校准和供应商可靠性持怀疑态度。
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原文

Gemini Decision 1 scores options without token-by-token decoding, keeping median latency 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.

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

You can evaluate multiple questions 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 your 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. Here is how to check a photo of a breakroom counter, together with a text note, against all three question types in one request:

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