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OpenRouter adds ScaleDown's Summarize, Extract, and Classify-1 constrained-decoding models

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ScaleDown's family of constrained-decoding models is now live on OpenRouter, with each model built for a single task.

Summarize turns long text, images, and PDFs into short summaries, Extract returns JSON from plain-English field descriptions with confidence scores, and Classify-1 gives calibrated probabilities for user-defined labels.

Launch pricing is $0.04 per million input tokens, with output free.

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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.

http://openrouter.ai/provider/scaledown

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 👀
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