SubQ 发布 384K 上下文决策模型 Snap

SubQ 推出 SubQ Snap，号称首个面向企业真实工作的长上下文决策模型，支持 384K 多模态上下文，是 Jev 的 12 倍。其延迟随上下文线性增长：100K tokens 660ms、384K 2.5 秒，而标准二次注意力在该区间成本约涨 15 倍。

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.

来源：Rohan Paul · x.com