ScaleDown 推出约束解码模型家族 CDM，专为单一任务设计
ScaleDown 发了个不走生成路线的模型家族，分类、抽取、摘要各管一个，官方说比前沿 API 快 200 倍还便宜几百倍，做数据处理的可以试试。
ScaleDown 发布了名为 Constrained Decoding Models（CDM）的新模型架构，包含 Summarize、Extract、Classify 三款模型，每款只做一件事。这类模型跳过 LLM 逐 token 生成的过程，宣称比前沿 LLM API 快 200 倍、便宜 467 倍。基准成绩方面，CDM 在 DecisionBench 得到 77.2%，对比 Jev 的 72%。三款模型支持 1M token 的多模态输入，现已在 OpenRouter、Vercel、LangChain 等平台上线，输入价格 $0.04-0.05/M token，输出免费，首发有 20% 折扣。
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… Neal Patel @neal_k_patel 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 purpose-built for 1 task each. They read your input once and skip the token-by-token generative loop that makes LLMs slow and expensive. Not just a decision model, but a family of 3 CDMs live today: • Classify: define your labels and get back a calibrated probability + reason for each. Used for routing, tagging, and intent detection. • Extract: describe the entities in natural language and get back well-formed JSON with confidence scores + citations. Used for web scraping, document processing, and PII redaction. • Summarize: pass in long text, images, or PDFs and get back a consistent summary under 1,000 tokens. Used for context compaction, metadata generation, and transcript digests. Built for production: • 1M tokens of multimodal (text + image) input, 31x larger than Jev • 77.2% on DecisionBench vs Jev's 72% • 200x faster, 467x cheaper than frontier LLM APIs • $0.05/M input tokens. Output is free (yes, even for summarize) Live on your favorite platforms today: @OpenRouter @vercel_dev @LiteLLM @LangChain @TrustedRouter @itsArthurAI @useRouterPlus Get 50M input tokens for free (for the first 50 hours) at link below 👀 Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 8 🔄 3 ❤️ 59 👀 5614 📊 14 ⚡
