SubQ

SubQ is the first large language model built on a fully sub-quadratic sparse-attention architecture, designed specifically for long-context tasks at scale. Unlike transformer-based models that process every possible token relationship at O(n²) complexity, SubQ operates at O(n) by focusing only on the relationships that matter — reducing attention compute by nearly 1,000× at 12M tokens. It delivers 12M-token reasoning at 150 tokens per second, at one-fifth the cost of other leading LLMs, with no quality loss. SubQ is available as a developer API and as a coding agent integration layer.

• 12M Token Context Window — Process entire codebases, months of pull request history, and long-running agent state in a single prompt.
• Sub-Quadratic Architecture — Built on Sparse Structured Attention (SSA), SubQ scales linearly rather than quadratically, making long-context inference practical and affordable.
• OpenAI-Compatible API — Drop-in API endpoints with streaming and tool use support, making integration straightforward for existing developer workflows.
• SubQ Code Integration — A long-context layer for coding agents that plugs into Claude Code, Codex, and Cursor, delivering ~25% lower bills and 10× faster codebase exploration via a one-line install.
• Auto-Redirect for Expensive Turns — SubQ Code automatically redirects token-heavy questions away from expensive frontier models, optimizing cost without changing agent behavior.
• Benchmark-Validated Performance — SubQ 1M-Preview achieves 81.8% on SWE-Bench Verified and 95.0% on RULER @ 128K, with results third-party validated.
• Enterprise API Access — Full-context API for enterprise teams to process full repositories and pipeline states in a single call at linear cost.
• Research-Driven Team — Built by researchers from Meta, Google, Oxford, Cambridge, and BYU, pushing foundational change at the model architecture level.