Subquadratic Launches SubQ Snap, the First Long — Context Decision Model Purpose — Built for Enterprise
For best results when printing this announcement, please click on link below: Subquadratic Launches SubQ Snap, the First Long-Context Decision Model Purpose-Built for Enterprise Powered by Subquadratic Sparse Attention (SSA), SubQ Snap has a 12x larger context window than Jev, and outperforms the category leader across the decision model benchmarks that matter for enterprises. Subquadratic ( ), a foundational model company focused on improving AI scaling laws, announced today the launch of SubQ Snap, a multimodal decision model built for fast, long-context decisions in enterprise agentic workflows. Powered by Subquadratic Sparse Attention, it has constant compute cost per token, while models like Jev or Claude have quadratically exploding compute costs and inverse economics of scale. This enables it to reason over substantially more of an artifact without giving up the speed and efficiency that make decision models useful. Highlights include: Up to 384K tokens of context, 12x larger than Jev. Snap can reason over long codebases, documents, interaction histories, and other artifacts in a single call, without requiring truncation or chunking. Fast at long context. Snap can process 10
For best results when printing this announcement, please click on link below: Subquadratic Launches SubQ Snap, the First Long-Context Decision Model Purpose-Built for Enterprise Powered by Subquadratic Sparse Attention (SSA), SubQ Snap has a 12x larger context window than Jev, and outperforms the category leader across the decision model benchmarks that matter for enterprises. Subquadratic ( ), a foundational model company focused on improving AI scaling laws, announced today the launch of SubQ Snap, a multimodal decision model built for fast, long-context decisions in enterprise agentic workflows.
Powered by Subquadratic Sparse Attention, it has constant compute cost per token, while models like Jev or Claude have quadratically exploding compute costs and inverse economics of scale. This enables it to reason over substantially more of an artifact without giving up the speed and efficiency that make decision models useful. Highlights include: Up to 384K tokens of context, 12x larger than Jev. Snap can reason over long codebases, documents, interaction histories, and other artifacts in a single call, without requiring truncation or chunking.
Fast at long context. Snap can process 100K tokens and return a decision in ~1 second. Multimodal. Snap accepts text and images in the same context, allowing screenshots, charts, diagrams, and scanned pages to be reasoned over alongside the surrounding documents and logs.
Category leading performance. 0% aided by chunking. 6 and leads on 13 of 16 tasks. Achieves frontier performance on standard short-context evals.
9. 39). "Decision models get exciting when they can take real work away from LLMs, but the context window size of today’s decision models are simply too small or slow to handle real enterprise work,” said Alex Whedon, co-founder and CEO of Subquadratic. “That's why we created SubQ Snap.
" Taking real work off generative models Unlike traditional large language models designed for open-ended generation, decision models are optimized to choose from a predefined set of options, such as which tool to use next, whether a result passes, or which step should be handed to a larger model. Agents make these decisions repeatedly throughout a workflow, so moving them to a fast, specialized model can reduce the cost and latency of the system as a whole. But decision models only take meaningful work off larger models if they have enough context to make those decisions correctly.
In enterprise workflows, the relevant evidence may be spread across a repository, a long interaction history, a contract and its exhibits, or several related documents. If the decision model cannot process that context, the input has to be reduced, retrieved, or handed back to a larger model. Snap was built to move that boundary. Its 384K context window and SSA architecture allow it to make bounded decisions over substantially larger artifacts while maintaining the speed and efficiency expected from a decision model.
0 on Decision LCR over real multi-document inputs. The result is a broader role for decision models inside enterprise agents. More routing, verification, tool selection, and control decisions can be handled directly by a specialized model, while generative models can be reserved for synthesis, coding, and deeper reasoning. SubQ Snap is part of the SubQ model family which Subquadratic introduced after emerging from stealth earlier this year.
SubQ was the first LLM built on a fully subquadratic architecture, where compute grows linearly with context length instead of quadratically. In research testing at 12 million tokens, the architecture cut attention compute by almost 1,000x compared with other frontier models. Learn more about SubQ Snap here SubQ Snap is available today for limited access, please visit Subq. ai for access.
About Subquadratic Subquadratic is a frontier AI research company building the most compute-, memory- and sample-efficient algorithms and models. Our mission is to build a radically more intelligent world to augment human potential. This requires rewriting the scaling laws of AI, not just more capital. com)