SubQ 1.1 Small vs. Capitol

SubQ 1.1 Small is a long-context enterprise AI model developed by Subquadratic to address the limitations of traditional models that struggle with large artifacts. It is built for tasks where the full context matters, including analyzing entire codebases, reviewing lengthy contracts, comparing financial filings, and reasoning across document collections. The model uses Subquadratic Sparse Attention, which replaces dense attention with a learned sparse approach that scales more efficiently as context length grows. This allows SubQ 1.1 Small to process extremely large context windows while sharply reducing attention compute requirements. In benchmark testing, the model achieved near-perfect needle-in-a-haystack retrieval at 1M, 2M, 6M, and 12M tokens. It also scored 99.12% on the RULER 128K benchmark, demonstrating strength on tasks involving multi-hop reasoning, variable tracing, aggregation, and long-context understanding. Beyond retrieval, SubQ 1.1 Small maintains competitive performance in general knowledge, coding, and enterprise agent benchmarks such as GPQA Diamond, LiveCodeBench, and AutomationBench Finance. Its efficiency is a major advantage, requiring 64.5x less compute than dense attention and running 56x faster than FlashAttention-2 at 1M tokens on a single attention layer. The model was trained through staged context extension and continued pretraining on long-form artifacts such as books, documents, and repository-scale code. SubQ 1.1 Small is suited for financial analysis, legal work, software engineering, due diligence, long-horizon coding tasks, and enterprise workflows that depend on relationships spread across large bodies of information. It gives organizations a way to reason over complete artifacts more directly instead of relying only on retrieval pipelines, chunking strategies, and agentic scaffolding.

Capitol functions as a sophisticated AI platform tailored for high-stakes businesses that must adhere to strict regulations, enabling them to convert expert insights and sensitive information into dependable solutions for critical decision-making. It guarantees that all data remains secure within the organization's infrastructure and delivers outcomes that are both traceable and auditable, allowing for implementation in mere moments instead of weeks. By utilizing sovereign agentic search and automated intelligence synthesis, Capitol proficiently transforms enterprise knowledge into actionable results, providing a wide array of customizable artifacts. Its research engine is carefully crafted to work exclusively with the organization’s proprietary data, while the synthesis layer efficiently produces detailed reports, briefs, and tailored documents from both structured and unstructured data. Additionally, it skillfully generates and simplifies content in multiple formats, incorporating both internal and external resources to turn proprietary intelligence into useful tools such as presentations, spreadsheets, software code, and audio files. This capability significantly enhances the organization's decision-making prowess, allowing them to act quickly and with assurance. As a result, organizations not only streamline their processes but also solidify their strategic positioning in the competitive marketplace. By leveraging these advanced features, they can maintain a significant edge over their competitors.