Startup profile · funding coverage
Subquadratic funding, valuation and investors
AI-model developer building long-context SubQ architectures with sparse, subquadratic attention computation.
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Funding, valuation & investors
Answer-first snapshotLatest funding
Seed
$29M · May 5, 2026
Latest known valuation
Not publicly disclosed
Total disclosed equity funding
$29M
Excludes debt, grants, acquisitions, secondaries, and IPO proceeds.
Current status
private
seed · Miami, Florida, United States
Investors in latest funding
Other: Javier Villamizar , Justin Mateen (JAM Fund) , Grant Gittlin (Lasagna) , Jaclyn Rice Nelson (Coalition Operators)
Overview
Subquadratic, founded by CEO Justin Dangel and CTO Alex Whedon, announced $29M in seed financing on May 5, 2026 alongside the SubQ 1M-Preview model and private-beta API, code assistant and search product. The company claims near-linear attention-compute scaling and long-context performance. Investors named in its announcement include Javier Villamizar, Justin Mateen, Grant Gittlin and Jaclyn Rice Nelson.
There is no public identification of a formal lead investor. Some benchmark claims are company-run or company-commissioned and require wider independent reproduction.
Why Subquadratic is interesting
A clear $29M seed-funded bet on reducing long-context inference cost by redesigning attention scaling, rather than NCM's parallel concept generation.
Product & use cases
SubQ 1M-Preview, long-context language API, SubQ Code and SubQ Search; subquadratic sparse attention.
- Analyzing large codebases and long repositories
- Search and synthesis over long collections of documents
- Models that handle large context without quadratic attention cost
Key facts
- $29M company-announced seed on May 5, 2026
- Named individual backers but lead undisclosed
- SubQ API, Code and Search announced for private beta
Funding history (newest first)
Seed
2026-05-05 $29M- Javier Villamizar (participant)
- Justin Mateen (JAM Fund) (participant)
- Grant Gittlin (Lasagna) (participant)
- Jaclyn Rice Nelson (Coalition Operators) (participant)
Source: Read source · subq.ai
Competitive landscape
Edge: Long-context sparse-attention model and vertically integrated application beta, though model-quality and unit-cost claims need third-party production evaluation.
Sparse attention is an alternative to full quadratic attention, not a parallel text generator. Comparisons must distinguish attention subproblem scaling from end-to-end wall-clock model speed, total context memory and retrieval accuracy.
Market / IPO context
Editorial / static context — not a live quote.
FAQs about Subquadratic
Practical answers founders, operators, and investors typically search for.
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Editorial note: AI tools assisted with research, structure, or drafting. Venture Capital Tracker retains human editorial responsibility for factual accuracy, relevance, and source quality before publication.