Subquadratic releases SubQ Snap, a decision model trained with reinforcement learning, with 384K tokens of multimodal context. It scores 61.7 on Decision Index v0.2.1 against 57.9 for Jev and leads on 13 of 16 enterprise benchmarks. Input is priced at $0.05 per million tokens, and the first 10,000 sign-ups get 100M free input tokens.
Pay attention to decision models.
From my recent tests, decision models can be extremely transformative for businesses.
Many calls in an agent workflow are structured decisions, so paying frontier prices doesn't make sense.
SubQ Snap from @subquadratic is trained with RL for those calls. It holds 384K tokens of context and leads Jev on 13 of 16 enterprise benchmarks, so it can decide with a whole repo or a stack of contracts in view.
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: https://subq.ai/
Source: elvis · x.comPublished · added here
