SubQ Snap 发布：面向企业决策的 384K 长上下文模型
有个叫 SubQ Snap 的决策模型，专门干 agent 工作流里的判断活，384K 上下文，输入才 $0.05/MM，比用前沿模型便宜太多了。
subquadratic 推出 SubQ Snap，一款用 RL 训练的决策模型，专为智能体工作流中的结构化决策调用设计。它支持 384K 多模态上下文，是 Jev 的 12 倍，在 Decision Index v0.2.1 上得 61.7 分，高于 Jev 的 57.9，在 16 个企业基准中的 13 个上领先。处理 100K token 并返回决策可在 1 秒内完成，上下文增长时每 token 计算量保持恒定。定价为每百万输入 token 0.05 美元，输出免费，前 10,000 名注册用户可获 1 亿免费输入 token。
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. Alexander Whedon @alex_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: subq.ai 8 Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 4 🔄 0 ❤️ 8 👀 1383 📊 4 ⚡
