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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 Qwen3.6-35B-A3B?

Qwen3.5-35B-A3B is part of the Qwen3.5 "Medium" model lineup, designed as an efficient multimodal foundation model that effectively balances strong reasoning skills with real-world application demands. It features a Mixture-of-Experts (MoE) architecture, comprising 35 billion parameters but activating approximately 3 billion for each token, which allows it to deliver performance comparable to much larger models while significantly reducing computational costs. The model incorporates a hybrid attention mechanism that fuses linear attention with conventional attention layers, enhancing its capability to manage extensive context and improving scalability for complex tasks. As a vision-language model, it adeptly processes both text and visual inputs, catering to a wide range of applications such as multimodal reasoning, programming, and automated workflows. Additionally, it is designed to function as a flexible "AI agent," skilled in planning, tool utilization, and systematic problem-solving, thereby expanding its utility beyond simple conversational exchanges. This versatility not only enhances its performance in various tasks but also makes it an invaluable resource in fields that increasingly rely on sophisticated AI-driven solutions. Its adaptability and efficiency position it as a key player in the evolving landscape of artificial intelligence applications.

Media

Media

Integrations Supported

Claude Code
OpenAI
OpenAI Codex
SubQ

Integrations Supported

Cheaper Inference
Hugging Face
ModelScope
Ollama
OpenClaw
Qwen
Qwen Studio

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Free
Open source
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Not specified

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

Alibaba

Date Founded

1999

Company Location

China

Company Website

qwen.ai/blog

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

AI Models

Not specified

AI Reasoning Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Open-Weight AI Models

Not specified

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