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What is SubQ 1.1 Small?

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.

What is Smaug Flash?

The Smaug Flash series features three meticulously refined open-weight models created by Abacus.AI to effectively handle production agentic workloads, each positioned thoughtfully along the capability–efficiency continuum. This lineup is crafted from a blend of meticulously selected real-world agentic data and synthetic scenarios that challenge conventional limits, resulting in significant advancements in agentic programming, effective tool use in real contexts, automation capabilities, long-context reasoning, and adherence to instructions. At the forefront is the flagship model, Smaug Flash, which is based on DeepSeek V4 Flash 0731 and serves as the ideal choice for enterprise agents that demand a seamless combination of speed, efficiency, and reliable performance. Its tailored adjustments significantly reduce the risk of spins and confusion during extensive tool interactions while maintaining the rapid response characteristics of the original model. Furthermore, Smaug Mini, which is based on Qwen3.8 27B, targets multimodal applications and simpler reasoning tasks, providing a more compact solution with enhanced real-world agentic functions tailored for singular workflows. Collectively, these models effectively address a variety of operational requirements across multiple applications, underscoring the adaptability and extensive potential of the Smaug Flash family, which continues to evolve in response to user needs.

Media

Media

Integrations Supported

Claude Code
OpenAI
OpenAI Codex
SubQ

Integrations Supported

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Subquadratic

Date Founded

2026

Company Location

United States

Company Website

subq.ai/subq-1-1-small-technical-report

Company Facts

Organization Name

Abacus.AI

Date Founded

2019

Company Location

United States

Company Website

abacus.ai/smaug

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

Large Language Models

Not specified

Small Language Models

Not specified

Categories and Features

AI Coding Models

Not specified

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