Subquadratic
1 model across 1 family · Latest: SubQ 1M-Preview (2026-05)
Efficiency is Intelligence
Subquadratic's portfolio covers 1 active model across 1 current family, spanning rag, agents, and long context. Open a model detail page to compare provider routes and sourced benchmarks.
Covers 4 workload areas across 1 active tracked model; last verified 2026-06-29.
Use it for
- Teams evaluating rag, agents, and long context across this lab's releases
- Comparing model families before committing to a flagship
- Migration and pricing follow-ups across 1 tracked models
Do not use it for
- Choosing a hosting provider without opening a model page for price ladders
Active models
1
Current models from this lab, excluding deprecated ones
Active families
1
Current model families from this lab
Open catalog
0 open
0 open source / 0 open weights
Lowest output price
Not tracked
No provider output pricing linked yet
Latest dated release
2026-05-05
SubQ 1M-Preview
Freshness
2026-06-29
Researched 81d ago
Information
Release cadence
- Dated releases
- 1
- Latest model
- SubQ 1M-Preview
- Latest date
- 2026-05-05
Where this lab wins
- RAG: 1 tracked model with ruler / needle retrieval benchmarks.
- Agentic: 1 tracked model with BFCL, tau-bench, and SWE-bench tool-use coverage.
- Long-context: 1 tracked model with context-token or InfiniteBench-class signal.
- JSON/tool-use: 1 tracked model with BFCL / Nexus strict-JSON routing coverage.
Flagship quality / price signal
- Flagship
- SubQ 1M-Preview
- Selection source
- Best sourced coding Q/$
- Coding grade
- unknown
- Benchmark / output price
- Not enough sourced benchmark and price coverage
About
Subquadratic Inc. is a frontier AI research and infrastructure company founded in 2026 and backed by $29M in seed funding from investors including Javier Villamizar (former SoftBank Vision Fund), Justin Mateen (Tinder co-founder, JAM Fund), and early investors in Anthropic, OpenAI, Stripe, and Brex. Led by CEO Justin Dangel and CTO Alexander Whedon, the company employs 11 PhD researchers and engineers from Meta, Google, Oxford, Cambridge, ByteDance, Adobe, and Microsoft. Their core innovation is a fully sub-quadratic sparse-attention LLM architecture that reduces attention compute by ~1,000x vs.
Featured models
| Model | Released | Context | Input price ($/1M) | Output price ($/1M) | License | Openness |
|---|---|---|---|---|---|---|
| SubQ 1M-Preview | 2026-05-05 | 1m | - | - | Proprietary | Proprietary |
Model families
Recent releases
- SubQ 1M-Preview- 2026-05-05
Explore related pages
Last reviewed: 2026-06-29. Data sourced from public lab announcements and provider documentation.