AI 导读
Subquadratic 推出决策模型 SubQ Snap，支持单次调用最多 384K tokens 的文本与图像多模态上下文，处理 100K tokens 可在一秒内返回决策，各档延迟为最小上下文 80ms、32k 250ms、100k 660ms、384k 2.5 秒。
正文
Subquadratic says SubQ Snap processes 100K tokens and returns a decision in *under a second*.
Its new decision model supports up to 384K tokens in one call, including text and images. The team’s pricing is $0.05 per million input tokens, cached or uncached, with free output.
Lead qualification is a good example: check each new lead against deals you’ve already won or lost, then let a frontier model write the outreach for the ones that look promising. Your past deals end up guiding the next ones.
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：Chubby♨️ · x.com