SubQ Snap 1.1 Comes to Baseten: Deployment, Context and Pricing Details

Baseten announced support for Subquadratic's SubQ Snap 1.1 on November 2, adding a dedicated-deployment route for the long-context decision model. The model already existed before this announcement. The new development is where teams can deploy it, rather than a new version or a newly announced context window.

What is available on Baseten

Baseten's model listing offers dedicated hardware through a contact-led deployment flow. It describes a proprietary model for routing, ranking, classification and other structured judgments, supporting up to 384K tokens of context and up to four images.

This is different from a chatbot that writes a long answer. Snap returns probabilities, category choices and scores that application code can use to decide what happens next.

The November 2 announcement confirms the provider rollout. The library page does not publish a dedicated-hosting price or establish that a self-serve, per-token Baseten endpoint is available. Its sample request points to Subquadratic's own API, so developers should not mistake that example for Baseten endpoint documentation.

SubQ API pricing and output format

Subquadratic's Snap 1.1 release page lists its direct API price at $0.04 per million input tokens, with no output-generation charge. That price belongs to the direct SubQ service. Dedicated infrastructure on Baseten needs a separate commercial quote.

The Decisions API documentation labels Snap as a public preview. Requests contain the material to evaluate and named questions with criteria. A question can return a yes/no probability, choose among supplied categories or score an ordered set of levels. The response reports zero output tokens.

For a support-ticket workflow, for example, the application might ask whether a ticket needs escalation and which queue should receive it. The value comes from obtaining a bounded decision that can be checked, rather than generating prose and then extracting a decision from it.

What the benchmark does and does not show

Artificial Analysis records a Decision Index score of 65 for Snap 1.1. Its index combines knowledge, reasoning, tool-use and guardrail evaluations. It is a useful comparison for decision tasks, not proof of equivalent performance on an arbitrary enterprise workflow.

The same page reports 190 milliseconds median task latency in its evaluation. That is not a measured latency guarantee for a full 384K-token request or for a new Baseten deployment.

Teams considering the model should evaluate their actual routing errors, including the cost of sending a difficult case to the wrong place. A confidence field is only useful when its behaviour has been checked on the data and decisions the application will encounter.

The provider expansion gives teams another way to operationalize a specialized model. The important comparison is then broader than token price: workload volume, hardware utilization, response time and the quality of the decisions all determine whether moving routine judgments out of a larger model is worthwhile.

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