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What is SubQ?

SubQ is a next-generation large language model developed by Subquadratic, designed to handle extremely long-context reasoning tasks with high efficiency. It supports up to 12 million tokens in a single prompt, allowing it to process entire codebases, months of development history, and large datasets in one step. The model uses a fully sub-quadratic sparse-attention architecture, which reduces unnecessary computations by focusing only on meaningful relationships between data points. This approach significantly lowers computational costs while maintaining strong performance across complex tasks. SubQ is optimized for use cases such as software engineering, code analysis, long-context retrieval, and AI agent workflows. It enables developers to analyze large amounts of information without breaking it into smaller segments. The model offers fast processing speeds and lower operational costs compared to traditional transformer-based models. SubQ is accessible through APIs, making it easy for developers and enterprises to integrate it into their systems. It can also be used within coding agents to improve code mapping, exploration, and understanding. The platform supports streaming and tool usage for more dynamic workflows. Its architecture allows it to scale efficiently as data size increases, overcoming common limitations of standard models. SubQ also delivers competitive performance on benchmarks related to coding and long-context tasks. By combining efficiency, scalability, and large context capabilities, it provides a powerful solution for advanced AI applications.

What is Olmo 3?

Olmo 3 constitutes an extensive series of open models that include versions with 7 billion and 32 billion parameters, delivering outstanding performance in areas such as base functionality, reasoning, instruction, and reinforcement learning, all while ensuring transparency throughout the development process, including access to raw training datasets, intermediate checkpoints, training scripts, extended context support (with a remarkable window of 65,536 tokens), and provenance tools. The backbone of these models is derived from the Dolma 3 dataset, which encompasses about 9 trillion tokens and employs a thoughtful mixture of web content, scientific research, programming code, and comprehensive documents; this meticulous strategy of pre-training, mid-training, and long-context usage results in base models that receive further refinement through supervised fine-tuning, preference optimization, and reinforcement learning with accountable rewards, leading to the emergence of the Think and Instruct versions. Importantly, the 32 billion Think model has earned recognition as the most formidable fully open reasoning model available thus far, showcasing a performance level that closely competes with that of proprietary models in disciplines such as mathematics, programming, and complex reasoning tasks, highlighting a considerable leap forward in the realm of open model innovation. This breakthrough not only emphasizes the capabilities of open-source models but also suggests a promising future where they can effectively rival conventional closed systems across a range of sophisticated applications, potentially reshaping the landscape of artificial intelligence.

Media

Media

Integrations Supported

Claude Code
OpenAI
OpenAI Codex
SubQ 1.1 Small

Integrations Supported

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

Pricing Information

Free
Free Version

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
Online Training

Company Facts

Organization Name

Subquadratic

Date Founded

2026

Company Location

United States

Company Website

subq.ai/

Company Facts

Organization Name

Ai2

Date Founded

2014

Company Location

United States

Company Website

allenai.org/blog/olmo3

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Large Language Models

Not specified

Categories and Features

AI Models

Not specified

Large Language Models

Not specified

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