ScaleDown's constrained-decoding model family launches on OpenRouter
OpenRouter said a family of constrained-decoding models from ScaleDown is live, with each model purpose-built for a single task. Summarize turns long text, images and PDFs into short, consistent summaries for context compaction and transcript digests; Extract takes fields described in plain English and returns JSON with confidence scores and source spans; Classify-1 lets users set labels and returns a calibrated probability for each through the Decisions API. Launch pricing is 20% off at $0.04 per million input tokens with output free, and Neal Patel describes the approach as Constrained Decoding Models (CDMs); the capabilities and pricing are vendor claims from OpenRouter's post.
The launch adds task-specific models with constrained outputs to OpenRouter, aimed at production workloads that the post says need fast, consistent and affordable answers rather than free-form generation.
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