Subquadratic
Subquadratic is a Miami-based AI company building SubQ, a long-context large language model using its proprietary Subquadratic Sparse Attention architecture. It serves developers, enterprise teams, and coding-agent builders via an OpenAI-compatible API and CLI with up to 12M-token context.
- Company typePrivate
- Founded2026
- HeadquartersMiami, United States
- Headcount51–100
- GTM typeB2B
- OfferingSoftware
What Subquadratic does
Subquadratic is a Miami-based AI company that builds SubQ, a long-context large language model constructed on a proprietary Subquadratic Sparse Attention (SSA) architecture. SSA replaces the standard O(n²) all-pairs dense attention with content-dependent selection that scales linearly (O(n)) with context length, enabling a native 12-million-token context window. The model is delivered through three product surfaces: the SubQ API (an OpenAI-compatible, full-context API for developers and enterprise teams), SubQ Code (a CLI coding agent that loads entire codebases into a single context and integrates with Claude Code, Codex, and Cursor), and SubQ Search (a long-context deep-research tool). All products are currently in private beta and gated behind a public waitlist.
SubQ was built by replacing dense attention in an existing open-weight frontier model with SSA, then running staged context extension (262K to 512K to 1M to 2M) followed by approximately one trillion tokens of continued pretraining on naturally long artifacts such as books, documents, and repository-scale code. According to third-party validation by Appen, SubQ 1.1 Small achieves 56x faster prefill than FlashAttention-2 at 1M-token context, 64.5x less compute than dense attention, near-perfect needle-in-a-haystack retrieval (100% at 1M to 2M, 98% at 6M to 12M), 99.12% on RULER 128K, 86.2% on MRCR v2, 89.7% pass@4 on LiveCodeBench v6, and 85.4% on GPQA Diamond. Public benchmarks are independently validated through Appen and, going forward, through a Stratix partnership with LayerLens.
The company operates a usage-based API, individual subscription accounts, and enterprise or design-partner contracts under an API Services Agreement, with no public price list disclosed. Founded circa 2025 to early 2026, Subquadratic emerged from stealth in May 2026 alongside a $29 million seed round at a rumored $500 million valuation, backed by angels including Tinder co-founder Justin Mateen (JAM Fund), Javier Villamizar, Grant Gittlin (Lasagna), and Jaclyn Rice Nelson (Coalition Operators), alongside early-stage investors in Anthropic, OpenAI, Stripe, and Brex. The 13-person team includes 11 PhD researchers from Meta, Google, Oxford, Cambridge, BYU, ByteDance, Adobe, and Microsoft, led by CEO Justin Dangel (a five-time founder) and CTO Alex Whedon (formerly Meta and Head of Generative AI at TribeAI). The company is incorporated in Delaware with operational headquarters in Miami and is pre-revenue at the time of analysis.
Subquadratic firmographics
Firmographics- Name
- Subquadratic
- Legal name
- Subquadratic Inc.
- Website
- https://subq.ai
- Company type
- Private
- Founded year
- 2026
- Operating status
- Operating
- Headcount range
- 51–100 employees
- Short description
- Subquadratic is a Miami-based AI company building SubQ, a long-context large language model using its proprietary Subquadratic Sparse Attention architecture. It serves developers, enterprise teams, and coding-agent builders via an OpenAI-compatible API and CLI with up to 12M-token context.
- Ownership category
- akta.pro rank
Subquadratic industry classification
Industry- Product category
- Large Language Model Infrastructure
- NAICS
- Custom Computer Programming Services (541511), Web Search Portals and All Other Information Services (51929)
- SIC
- Services-Computer Programming, Data Processing, Etc. (7370), Services-Prepackaged Software (7372)
- akta.pro primary industry
- Search / Index Databases (HDAEAAAK)
Keywords
Where Subquadratic is headquartered
LocationHeadquarters
- HQ city
- Miami
- HQ country
- United States
- HQ region
- North America
Offices2 records
Markets served
Subquadratic business model
Business model- GTM type
- B2B
- Offering type
- Software
- Cost components
- Technology or R&D, Personnel, Infrastructure, Marketing or Sales
Revenue model
- SubQ API (usage-based): Full-context API sold to developers and enterprise teams for processing full repositories and pipeline states in a single API call at linear cost. Currently in private beta, with usage-based billing implied per Terms of Use.
- Individual subscriptions: Paid subscription accounts for individual users of Subquadratic's foundational model and speech processing APIs (text-to-speech, speech-to-text, speech-to-speech), with automatic periodic renewal billing.
- Enterprise / design-partner contracts: Enterprise sales motion for design partners and broader enterprise rollout through 2026, delivering SubQ via API Services Agreement with usage-based and contracted pricing.
Pricing tiers
| Model | Billing | Price |
|---|---|---|
| Usage-based | Pay-as-you-go | Usage-based API (private beta) |
| Subscription | Monthly | Individual subscription accounts |
Go-to-market motion4 records
Distribution channels4 records
Marketing channels8 records
Subquadratic product offering
Product offeringCore offering
Subquadratic builds and sells the SubQ family of large language models, a long-context LLM platform built on its proprietary Subquadratic Sparse Attention (SSA) architecture that replaces O(n²) dense attention with content-dependent sparse attention that scales linearly (O(n)) with context length. SubQ is delivered as a full-context, OpenAI-compatible API for developers and enterprise teams, a CLI coding agent (SubQ Code) that loads entire codebases into a single context window, and a long-context Deep Research tool (SubQ Search), all backed by a native 12-million-token context window.
Product overview
Subquadratic offers a unified long-context AI platform anchored on its proprietary SubQ large language model and built around a single core innovation: Subquadratic Sparse Attention (SSA), a linearly scaling attention architecture that replaces standard dense attention. The core SubQ model is delivered through three product surfaces — the SubQ API (a full-context, OpenAI-compatible API for developers and enterprise teams), SubQ Code (a CLI coding agent that loads entire codebases into one context and plugs into Claude Code, Codex, and Cursor), and SubQ Search (a long-context Deep Research tool) — all of which share the same 12M-token context window and linear-cost economics. The current model lineup is led by SubQ 1.1 Small (the latest iteration in private design-partner deployment), which sits alongside the earlier SubQ 1M-Preview preview release, with a broader 2M–12M model family planned for end of 2026.
Differentiator
Problem solved
Functional benefit
Brands
- SubQ: Subquadratic's flagship large language model built on the proprietary Subquadratic Sparse Attention (SSA) architecture, supporting up to 12M-token context windows. Available via API and as a foundation for other products.
- SubQ Code
- SubQ Search
Products and services
- SubQ API Full-context, OpenAI-compatible API for developers and enterprise teams that processes full repositories and pipeline states in a single API call at linear cost, with a 12M-token context window, streaming, and tool use.
- SubQ Code Coding agent CLI built on SubQ that loads entire codebases into a single context window, enabling plan/execute/review across a full repository without multi-agent orchestration; plugs into Claude Code, Codex, and Cursor.
- SubQ Search
Quantifiable outcome
- 56× faster prefill than FlashAttention-2 at 1M-token context (third-party verified by Appen)
- +7 more outcomes
Companies that use Subquadratic
Customer profileSegments6 records
Ideal customer profiles4 records
Subquadratic technology and API
TechnologyTechnology focussed Yes
API detail
- Has API
- Yes
- API docs
- API detail
Core technology
AI maturity
App detail
Integration3 records
AI capability9 records
Feature6 records
Subquadratic partnerships and signals
Strategic signalPartnerships
Five partnerships are on record, tiered core and minor.
- LayerLenscoreSubquadratic partnered with LayerLens to evaluate SubQ on the Stratix benchmark platform covering 200+ models and ~100 benchmarks. Long-context retrieval, positional consistency, synthesis, reasoning, coding, instruction-following, and tool-use evaluations will be run, with public results published at stratix.layerlens.ai and per-benchmark breakdowns, methodology, strengths, and limitations. Subquadratic is adopting Stratix Enterprise for all future SubQ releases, creating a continuous independent evaluation layer.
- AppencoreAppen independently verified SubQ's benchmark results (third-party-validated RULER, MRCR v2, LiveCodeBench, and SubQ 1.1 Small efficiency numbers), publishing a public whitepaper at appen.com/whitepapers/subquadratic-preview-model-benchmark-evaluation. The validation is referenced throughout Subquadratic's product materials as evidence for performance claims.
- Claude Code (Anthropic)minorSubQ Code is designed to plug into Claude Code, Codex, and Cursor as a long-context layer that auto-redirects expensive model turns. This is a documented integration rather than a formal commercial partnership announcement.
- Codex (OpenAI)minorSubQ Code plugs into Codex as part of its supported integration list for long-context coding tasks.
- CursorminorSubQ Code plugs into Cursor as part of its supported integration list for long-context coding tasks.
Scale indicators11 records
Recent moves5 records
Expansion highlights6 records
Subquadratic competitors and assessment
Company assessmentBroad incumbents
- Anthropic: Anthropic's Claude family is a frontier LLM with industry-leading long-context support (1M tokens in Claude Sonnet 4) and is one of SubQ Code's explicit integration targets. As a broad incumbent, it offers long-context as one capability within a wider general-purpose model portfolio rather than specializing in the architecture-level cost niche Subquadratic targets.
- OpenAI: OpenAI's GPT-5 and o-series models define the frontier LLM benchmark, and Subquadratic's API is explicitly OpenAI-compatible to ride on this developer mindshare. As a broad incumbent, it competes across the full model stack and ecosystem with vastly greater compute, data, and distribution.
- Google DeepMind: Google's Gemini family offers multi-million-token context windows and frontier coding/reasoning performance, directly competing with SubQ on long-context workloads. Comparable as a broad incumbent pursuing long-context capabilities with massive compute, native multimodal support, and deep enterprise distribution via Google Cloud.
Direct peers
- Mistral AI: Mistral builds efficient open-weight foundation models (Mistral Large, Codestral, Pixtral) with strong long-context performance. It is comparable as a foundation model competitor pursuing architectural efficiency, offering enterprise APIs, and serving coding-agent workloads that overlap with SubQ Code.
- AI21 Labs: AI21 Labs builds enterprise foundation models, most notably the Jamba family, which combines Mamba state-space layers with Transformer attention for long-context efficiency. It is directly comparable to Subquadratic because both are pursuing hybrid/efficient attention architectures as alternatives to dense transformers.
- Inflection AI: Inflection AI builds and fine-tunes large foundation models for enterprise and consumer applications with a focus on differentiated architecture and capabilities. Comparable to Subquadratic as a foundation model lab pursuing a non-commodity model architecture rather than fine-tuning existing open weights.
- DeepSeek: DeepSeek builds efficient open-weight foundation models (DeepSeek-V2/V3) with innovations like Multi-head Latent Attention (MLA) and DeepSeekMoE targeting long-context efficiency. It is highly comparable to Subquadratic because both pursue architectural innovation to cut long-context inference cost, and both rely on open-weight lineage (Qwen for SubQ; DeepSeek's own models for ecosystem distribution).
- Magic AI: Magic is a frontier AI research lab focused on long-context reasoning and autonomous coding agents with substantial venture backing. It is directly comparable to Subquadratic's SubQ Code positioning around full-repository reasoning and long-horizon agentic coding workflows.
Emerging players
- Letta: Letta (formerly MemGPT) is an emerging player focused on long-memory AI agents and persistent agent state. Comparable to Subquadratic's long-horizon agentic positioning, particularly SubQ Code and SubQ Search, though Letta emphasizes memory-augmentation architectures rather than a new attention mechanism.
- Contextual AI: Contextual AI builds enterprise-focused long-context AI and 'RAG 2.0' systems targeting the same financial, legal, and enterprise-document workflows as Subquadratic. Comparable as an emerging long-context player, though its approach relies on retrieval-augmentation rather than a single multi-million-token model.
Market position
Strengths4 records
Weaknesses4 records
Competitive moat5 records
Key risks6 records
Key highlights7 records
Customer concentration
Subquadratic social profiles
Digital presenceSubquadratic compliance and trust
Trust signalCompliance3 records
Subquadratic financial estimates
Financial estimateRevenue estimate
Valuation estimate
Subquadratic leadership team
Management profileNumber of profiles
Profiles2 records
Subquadratic funding detail
Funding detailFunding overview
Funding rounds1 record
Investors1 record
Funding detail is available on the Subscription and Enterprise plan.Contact sales →
Subquadratic M&A and investment
M&A and investmentM&A
Investments
M&A and investment is available on the Subscription and Enterprise plan.Contact sales →
Frequently asked questions about Subquadratic
What does Subquadratic do?
Subquadratic builds and sells the SubQ family of large language models, a long-context LLM platform built on its proprietary Subquadratic Sparse Attention (SSA) architecture that replaces O(n²) dense attention with content-dependent sparse attention that scales linearly (O(n)) with context length. SubQ is delivered as a full-context, OpenAI-compatible API for developers and enterprise teams, a CLI coding agent (SubQ Code) that loads entire codebases into a single context window, and a long-context Deep Research tool (SubQ Search), all backed by a native 12-million-token context window.
Is Subquadratic a public or private company?
Subquadratic is a private company. It is classified as venture growth investor backed and is currently operating.
When was Subquadratic founded?
Subquadratic was founded in 2026. It employs 51 to 100 people.
Where is Subquadratic based?
Subquadratic is headquartered in Miami, United States, in the North America region.
How does Subquadratic make money?
Three revenue lines are on record. SubQ API (usage-based) is the primary driver. The others are individual subscriptions and enterprise / design-partner contracts.
Who are Subquadratic's main competitors?
Broad incumbents on record are Anthropic, OpenAI and Google DeepMind. Direct peers are Mistral AI, AI21 Labs, Inflection AI, DeepSeek and Magic AI. Emerging players are Letta and Contextual AI.
Does Subquadratic have an API?
Yes. The full-context SubQ API for developers and enterprise teams processes full repositories and pipeline states in a single API call at linear cost. Features include a 12M token context window, streaming + tool use, and OpenAI-compatible endpoints. Currently available via private beta (waitlist). Request API access via /request-early-access. Developer documentation is at subq.ai/docs/subq-1-1-small-model-card.pdf.
What industry is Subquadratic in?
Subquadratic's product category is Large Language Model Infrastructure. Its primary akta.pro industry code is HDAEAAAK, Search / Index Databases. Its NAICS code is 541511 and its SIC code is 7370.