SubQ Snap launches as a long-context decision model: 384K tokens, decisions in ~2-3s at $0.05/M
ryanallred · x · 2026-10-13
Subquadratic released SubQ Snap, billed as the first long-context "decision model" for enterprise work: it reads the entire conversation each turn (messages, PDFs, images, whole repos, up to 384K multimodal tokens) and decides what UI to render—chart, map, checklist—without chunking or summarizing.
- Compute per token stays constant as context grows; claimed 12x larger context than Jev
- Scores 61.7 on Decision Index v0.2.1 vs Jev's 57.9, winning 13 of 16 enterprise benchmarks
- 100K tokens processed to a decision in under a second; $0.05/M input tokens, free output; first 10K signups get 100M free tokens
Early-access user @anishfn built SnapShift with it: a 171K-token turn (big PDF + full repo) resolved in 2-3 seconds for 1.5 cents, correctly retrieving exact files and screenshot details from earlier history. Repo is open on GitHub.
Related event: Subquadratic Launches SubQ Snap, a Long-Context Decision Model(6 posts)→
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