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.
https://t.co/YcCLlt2d2m
Single-purpose models for summarization, JSON extraction with confidence and spans, and calibrationHow well a model's confidence matches reality — a calibrated model saying "90% sure" is right about 90% of the time.Full definition → classification offer cheap building blocks for AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → pipelines and context compactionSummarizing an agent's earlier conversation to free room in the context window so a long session can keep going.Full definition →.
Terms in this piece · Glossary
context compaction — Summarizing an agent's earlier conversation to free room in the context window so a long session can keep going.
calibration — How well a model's confidence matches reality — a calibrated model saying "90% sure" is right about 90% of the time.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
