From the source
They require reasoning over complete artifacts: entire codebases, document collections, contracts, financial filings.
For years, the industry worked around this problem by building retrieval pipelines, chunking strategies, and agentic scaffolding — useful tools, but ultimately workarounds for context limitations of the model architecture.
The underlying constraint was attention: compute that scales quadratically with context length, making direct reasoning over large artifacts prohibitively expensive.
SubQ is built to remove that constraint.
Today we're releasing the model card for SubQ 1.1 Small — the second iteration of our Subquadratic Sparse Attention (SSA) model, at the smallest size.
We are in the process of deploying SubQ 1.1 Small with select design partners and plan to deploy a broader lineup of models ranging from 2M to 12M tokens later in the year.
Key Features Near-perfect long-context retrieval up to 12M tokens on the needle-in-a-haystack test, with up to nearly 1,000x attention compute reduction.
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