ScaleDown约束解码模型上线OpenRouter
OpenRouter 宣布，ScaleDown 的一组约束解码模型已上线，每个模型只针对单一任务。其中，Summarize 可将长文本、图像和 PDF 转为简短一致的摘要，用于上下文压缩和对话记录摘要；Extract 可用自然语言描述所需字段并返回带置信度与来源片段的 JSON；Classify-1 可自定义标签，并通过 Decisions API 给出校准概率。发布期价格为每百万输入 token 0.04 美元、输出免费（八折）；Neal Patel 将这类模型称为约束解码模型（CDM）架构，上述能力与定价均来自 OpenRouter 发布帖。
此次上线为 OpenRouter 增加了面向单一任务、输出受约束的模型，发布帖称其面向需要快速、一致且低成本答案而非自由生成文本的生产负载。
A family of constrained-decoding models from @scaledown_team is live on OpenRouter, each one purpose-built for a single task: • Summarize: turns long text, images, and PDFs into short, consistent summaries for context compaction and transcript digests • Extract: describe the fields you want in plain English and get clean JSON back, with confidence scores and source spans • Classify-1: set your own labels and get a calibrated probability for each, via our Decisions API 20% off for launch: $0.04/M input tokens, output free. openrouter.ai/provider/scale…
You already gave the LLM the answer. So why is it writing it out from scratch? Introducing a new AI model architecture: Constrained Decoding Models (CDMs) LLMs were built to write. Most production workloads just need an answer: fast, consistent, and affordable. CDMs are
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#ScaleDown#OpenRouter#Summarize#Extract#Classify-1#Decisions API#Neal Patel