SiteList review score
Focused positioning for enterprise AI research. Mobile performance and publishability mechanics need focused fixes.
Reviewed on Sep 6, 2026
Subq
subq.ai
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Claim itWhat it does
Subquadratic is a frontier AI research company building compute-, memory-, and sample-efficient algorithms and models. Its SubQ model is designed for multi-million-token reasoning across repositories, financial filings, contract archives, and other data-intensive workloads.
Two ratings
Verified user rating
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Screenshots
Captured during our latest review on Sep 6, 2026
Key features
Multi-million-token reasoning
Long-context reasoning
Processes entire repositories, financial filings, contract archives, and other large artifacts in one prompt.
Whole artifact reasoning
Long-context reasoning
Analyzes entire datasets such as GitHub repositories, legal documentation, vendor contracts, or SEC filings without chunking, compression, or context loss.
Long horizon agents
Agents
Maintains the full task in context across long sequences of events.
Search and retrieval
Search and retrieval
Surfaces insights across company intellectual property, code, or documents.
Subquadratic Sparse Attention (SSA)
Model architecture
Uses a proprietary sparse-attention algorithm to isolate relevant tokens and relationships for compute-efficient processing.
Who it’s for
Repository analysis
Analyze an entire GitHub repository in one context.
Legal document analysis
Reason across legal documentation and complete vendor contract archives.
Financial filing analysis
Work across years of SEC filings and full company filings.
Enterprise knowledge search
Search across a company's intellectual property, code, or documents.
Long-running agents
Run agent tasks that require retaining long sequences of events in context.
Buyer questions
Questions buyers actually ask
Who is Subq for?
Subq is aimed at enterprise AI researchers and engineers who need compute-efficient models for multi-million token reasoning.
What is Subq's overall score?
Subq scores 76.9 out of 100 in this review, placing it in the strong band.
What is Subq's strongest area?
First impressions and positioning score 95/100. The site clearly focuses on enterprise AI researchers and engineers working with multi-million token reasoning.
What should Subq improve first?
Performance is the clearest priority. Usability, writing quality, editorial QA, and technical SEO also have fixable weaknesses in the sampled pages.
Does Subq publish pricing?
Pricing is unknown in the supplied site facts, so this review does not state a price range.
Answers are based on SiteList's latest evidence review. Read the full review.
The expert review
Subq scores 77/100, with sharply focused positioning for enterprise AI researchers and engineers working on multi-million token reasoning. Its most material weaknesses are mobile performance and editorial and conversion friction across the six-page sample.
77/100: strong research positioning, fixable execution gaps
Verified user reviews
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Scores across 13 dimensions
Brand & experience
Performance
Content & editorial
Trust & decision surfaces
Search & discoverability
Docs & measurement
- Domain
- subq.ai
- Category
- Training data & model ops
- Platforms
- web
- Last checked
- Sep 6, 2026
- Public dimensions
- 13
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