Today for AI
HOT RADAR
llmHEAT 16.0°

AI CLUSTERED EVENT · 10/13/2026

SubQ Snap Launches Long-Context Decision Model with Linear Attention for Constant Compute at 384K Tokens

1 reports archived1 independent sourcesupdated 10/13/2026, 02:30:40
Synthesis & Latest Updates
1 Sources Cross-Validated

Subquadratic introduces SubQ Snap, a long-context decision model designed for real enterprise workflows. By leveraging linear attention, it maintains constant compute per token as context scales from 100K to 384K tokens, avoiding the quadratic cost explosion of standard attention mechanisms. The model supports multimodal inputs and outperforms competitor Jev on the Decision Index v0.2.1, bridging the gap between short-context classification tools and full-codebase judgment capabilities.

LATEST/Subquadratic introduces SubQ Snap, a long-context decision model designed for real enterprise workflows. By leveraging linear attention, it maintains constant compute per token as context scales from 100K to 384K tokens, avoiding the quadratic cost explosion of standard attention mechanisms. The model supports multimodal inputs and outperforms competitor Jev on the Decision Index v0.2.1, bridging the gap between short-context classification tools and full-codebase judgment capabilities.

HEAT TRENDHourly heat curve

3 fully observed hours
Now
16.0
Peak
16.603:00
24h change
–

The line compares only sources observed throughout; gap hours are interpolated to keep the trend continuous.

TIMELINECoverage timeline

Total 1 reports · Latest first
  1. Rohan Paul (@rohanpaul_ai)T2·68 pts
    • SubQ Snap uses linear attention to maintain constant compute per token up to 384K context, scaling latency linearly (approx. 2.5s at 384K).
    • Scores 61.7 on Decision Index v0.2.1, surpassing competitor Jev (57.9) and outperforming it in 13 of 16 enterprise benchmarks.
    • Aggressive pricing: $0.05 per million input tokens with free output, targeting replacement of chunked short-context decision tools.