SubQ Snap is a decision model that supports up to 384K tokens of text and image context in a single call, according to @svpino. The post says it processes 100K tokens and returns a decision in under one second, and that it leads Jev on 13 of 16 Decision Index benchmarks.
Decision-making models are the new hot thing in the industry.
SubQ Snap is the latest model to come out, and it looks pretty strong:
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/
Source: Santiago · x.comPublished · added here
