Subquadratic — Efficiency is Intelligence

All your context. Always available.

Reason across 12M tokens in one prompt: entire repos, months of PRs, and long-running agent state, with room to spare at one-fifth the cost.

012M

Python source code

The entire 3.13 standard library

~5.1M

Six months of React PRs

~1,050 pull requests against the React codebase

~7.5M

~ Approximate token counts.

Not just another model.An architectural breakthrough.

SubQ is the first model built on a fully sub-quadratic sparse-attention architecture. LLMs today waste compute by processing every possible relationship between words, but only a small fraction of these relationships matter.

SubQ finds and focuses only on those, ensuring compute is used where it matters most. At 12M tokens, this reduces attention compute almost 1,000×, changing the way LLMs scale.

Technical report (coming soon)

A leader in long-context retrieval and coding tasks.

Benchmarks	Gemini 3.1 Pro	Opus 4.6	Opus 4.7	GPT-5.4	GPT-5.5	SubQ 1M-Preview
SWE-Bench VerifiedReal-world software engineering ability	80.6%	80.8%	87.6%	n/r	n/r	81.8%
RULER @ 128KLong-context accuracy across 13 tests	n/r	94.8%*	n/r	n/r	n/r	95.0%
MRCR v2 (8-needle, 1M)Multi-round coreference resolution in long contexts	26.3%	78.3%	32.2%	36.6%	74.0%	65.9%

n/r = result was not reported by the model provider

* = internally evaluated

SubQ results are third-party validated

Technical report (coming soon)

Two ways to use SubQ.

API

For developers and teams

The full-context API for developers and enterprise teams. Process full repositories and pipeline states in a single API call at linear cost.

→ 12M token context window
→ Streaming + tool use
→ OpenAI-compatible endpoints

Request API access →

Code

For coding agents

The long-context layer for coding agents. Plug into Claude Code, Codex, and Cursor to map codebases, gather context, and answer token-heavy questions faster.

→ ~25% lower bill, 10× faster exploration
→ Auto-redirects expensive model turns
→ One-line install

Request SubQ Code access →

We built the architecture the industry said wasn't possible.

Subquadratic is a frontier AI research and infrastructure company building a new class of LLMs. While other major labs focus on incremental improvements to Transformer models, we're pushing foundational change at the model architecture level — enabling large-context, multi-modal inference that scales efficiently where transformers can't.

Built by researchers from

Meta
Google
Oxford
Cambridge
BYU

The first modelbuilt for long‑context tasks

All your context. Always available.

Not just another model.An architectural breakthrough.

A leader in long-context retrieval and coding tasks.

Two ways to use SubQ.

API

Code

From the lab.

Introducing SubQ: The First Fully Subquadratic LLM

How SSA Makes Long Context Practical

We built the architecture the industry said wasn't possible.

Is your business ready?
Build with us.

The first modelbuilt for long‑context tasks

All your context. Always available.

Not just another model.An architectural breakthrough.

A leader in long-context retrieval and coding tasks.

Two ways to use SubQ.

From the lab.

Introducing SubQ: The First Fully Subquadratic LLM

How SSA Makes Long Context Practical

We built the architecture the industry said wasn't possible.

Is your business ready?Build with us.

Is your business ready?
Build with us.