Ole Lehmann(@itsolelehmann):damn Jev just got lapped. Snap now has 12x Jev's decision context. and these decision models mog LLMs for micro-decisions where your agent needs an instant answer: > which ticket needs a human? > is this lead worth chasing? > does this contract clause break our rules? otherwise, you're just wasting time and tokens waiting for Claude to generate an answer when your agent just needs: > yes/no > a choice > or a score its basically like hiring a rocket scientist to sort support tickets. and with a massive 384K tokens, Snap can factor in more customer history, past deals and company rules when making those calls.

2026.10.12 16:31

damn Jev just got lapped. Snap now has 12x Jev's decision context. and these decision models mog LLMs for micro-decisions where your agent needs an instant answer: > which ticket needs a human? > is this lead worth chasing? > does this contract clause break our rules? otherwise, you're just wasting time and tokens waiting for Claude to generate an answer when your agent just needs: > yes/no > a choice > or a score its basically like hiring a rocket scientist to sort support tickets. and with a massive 384K tokens, Snap can factor in more customer history, past deals and company rules when making those calls.

Alexander Whedon@alex_whedon

2026.10.12 16:03

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: