Audience
Developers and enterprise AI teams interested in a tool to make fast, structured decisions across long documents, codebases, multimodal inputs, and agentic workflows
About SubQ Snap
SubQ Snap is a long-context decision model for enterprise and agentic workflows, built to make structured decisions quickly over large amounts of information. It handles tasks such as choosing which files are relevant, selecting the next tool, checking whether a result passes, deciding whether a workflow should continue, and determining when a frontier model is needed. Instead of generating open-ended text, Snap takes context, a question, and a finite set of allowed answers, then returns a probability distribution over those answers. It supports up to 384K tokens of mixed text and image context, allowing it to reason over codebases, contracts, interaction histories, logs, screenshots, charts, diagrams, and scanned pages in a single call. Powered by Subquadratic Sparse Attention, Snap reduces the cost of reading long inputs while a decision head maps contextual representations directly to admissible answers without autoregressive generation.
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