SubQ 发布长上下文决策模型 Snap，支持 384K tokens 多模态上下文，在 Decision Index v0.2.1 上得 61.7，高于 Jev 的 57.9，并在 16 项企业基准中的 13 项领先。该模型每 token 计算量随上下文增长保持恒定，处理 100K tokens 可在 1 秒内返回决策，输入定价 $0.05/MM tokens、输出免费，延迟从最小上下文的 80ms 到 384K 的 2.5 秒不等。前 10000 名注册用户可获 100M 免费输入 tokens。Elvis Saravia 表示，智能体工作流中许多调用属于结构化决策，用前沿模型价格并不划算，Snap 正是用 RL 针对这类调用训练。
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/在 X 查看被引用的帖子
来源：elvis · x.com