Jev has a lot to answer for. Ever since AI startup TypeSafe unveiled the decision model in mid-September, the industry has been awash with alternatives, with OpenAI, Perplexity, Cloudflare, Amazon and, most recently, Microsoft all getting involved.

Now Miami-based foundation-model startup Subquadratic has joined the rush with SubQ Snap, a decision model that it claims can process up to 384,000 tokens of text and images in a single request — 12 times Jev’s 32K-token per-decision context limit.

In a blog post published on Monday, the company says Snap can process 100K tokens and return a decision in approximately one second, potentially allowing AI agents to make decisions across large codebases, documents, and interaction histories without having to break information into smaller chunks.

Unlike traditional large language models (LLMs), which generate text and code, decision models specialize in selecting from predefined answers. That could mean choosing which tool an AI agent should use next, identifying relevant files, or determining whether a test has passed.

On Tuesday, The New Stack reported on how Workday is using Jev to build user interfaces on the fly for its Sana AI assistant, selecting prebuilt components such as paycheck cards and date pickers based on what an employee asks.

The attraction, ultimately, is speed and cost. These decisions occur frequently during an agent’s execution, and delegating them to smaller, specialized models reduces reliance on expensive frontier LLMs.

In an email Q&A, Subquadratic CEO and co-founder Alex Whedon acknowledges that Jev’s arrival helped shape Subquadratic’s own entry into the market.

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