SubQ Snap 长上下文决策模型：384K 上下文、线性延迟
SubQ Snap 是一款支持 384K 上下文的长上下文决策模型，100K tokens 延迟 660ms，384K 延迟 2.5 秒，输入规模扩大 3.84 倍时耗时约增加 3.8 倍，接近线性扩展；作者称标准二次注意力在该区间仅注意力开销就会上升约 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