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What is Xgen-small?

Xgen-small is a streamlined language model developed by Salesforce AI Research, specifically designed for enterprise applications, providing effective long-context processing at a reasonable price. It integrates focused data selection, scalable pre-training, extension of context length, instruction-based fine-tuning, and reinforcement learning to meet the sophisticated and high-demand inference requirements of modern enterprises. Unlike traditional large models, Xgen-small stands out in its ability to handle extensive contexts, enabling it to adeptly gather insights from a range of sources, including internal documents, programming code, academic papers, and live data streams. With configurations of 4B and 9B parameters, it achieves a delicate equilibrium between cost-effectiveness, data privacy, and thorough understanding of long contexts, making it a dependable and sustainable choice for extensive Enterprise AI applications. This pioneering method not only boosts operational productivity but also equips organizations with the tools to harness AI effectively in their strategic goals, thus fostering innovation and growth in various sectors. As businesses continue to evolve, solutions like Xgen-small will play a crucial role in shaping the future of AI integration.

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.

Media

Media

Integrations Supported

Agentforce Vibes

Integrations Supported

Claude Code
OpenAI
OpenAI Codex
SubQ 1.1 Small

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

Salesforce

Date Founded

1999

Company Location

United States

Company Website

www.salesforce.com/blog/xgen-small-enterprise-ready-small-language-models/

Company Facts

Organization Name

Subquadratic

Date Founded

2026

Company Location

United States

Company Website

subq.ai/

Categories and Features

AI Models

Not specified

Small Language Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Large Language Models

Not specified

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