Rohan Paul (@rohanpaul_ai) · 10/13/2026, 02:30:40
SubQ Snap Launches Long-Context Decision Model with Linear Attention for Constant Compute at 384K Tokens
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.
SOURCE COVERAGEFull Original Report
Rohan Paul (@rohanpaul_ai) · 2026-10-12 18
Decision models deserve more attention than they get.
look at the latency numbers on SubQ Snap from @subquadratic
660ms for 100K tokens. 2.5 seconds for 384K. that’s about 3.8x the time for 3.84x the input, which is what linear scaling looks like. with standard quadratic attention, the attention cost alone would rise about 15x over that range.
that scaling is how a decision model can hold 384K tokens of code, docs, logs and screenshots in one call, when most stop at 32K to 64K and make you chunk.
$0.05 per million input tokens, output free.
many of the existing decision models have mostly been short-context tools. 32K tokens is enough to classify a ticket, but too little to judge a change against a whole codebase. Now that changed.
↳ 引用 @alex_whedon (Alexander Whedon): Introducing SubQ Snap, the first long-context decision model for real enterprise work.
Snap is the only decision model or LLM where compute per token stays constant as context grows.
• 384K tokens of multimodal context, 12x larger than Jev. • 61.7 on Decision Index v0.2.1 vs Jev’s 57.9, outperforming on 13 of 16 enterprise benchmarks. •Processes 100K tokens and returns a decision in under one second. •$0.05/MM input tokens. Output is free.
Available today to anyone.
The first 10,000 sign-ups get 100M free input tokens.
We’ll likely hit capacity fast and will add users as we add compute. Compute offers welcome 🙂.
Latency will be variable as we tune the platform to this new model and category of users.
Model speeds are 80ms for minimal context, 250ms for 32k, 660ms for 100k, and 2.5 seconds for 384k.
Visit:
