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ease
features
design
support

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Description

The Laguna XS 2.1 is an enhanced coding model that operates as an open weight agentic system, ideal for long-duration tasks on local machines. Featuring a 33-billion-parameter Mixture-of-Experts framework with 3 billion parameters activated per token, this model maintains the efficient architecture of Laguna XS.2 while significantly advancing performance in multilingual software engineering and terminal-style tasks. It is specifically engineered to assist coding agents in reviewing repositories, reasoning through intricate changes, utilizing various tools, executing commands, and maintaining continuity throughout extended projects. With a generous 256K context window, the model enables agents to effectively manage extensive codebases, lengthy histories, and complex multi-step workflows. Laguna XS 2.1 benefits from support from platforms like vLLM, SGLang, NVIDIA TensorRT-LLM, Hugging Face Transformers, and Ollama, with plans for native integration with llama.cpp in the future. The model is offered in various checkpoint formats, including BF16, FP8, INT4, and NVFP4, granting developers the flexibility to select between high fidelity and configurations optimized for limited VRAM or computational resources. This adaptability makes it an excellent choice for a wide range of development environments and requirements.

Description

LongCat-2.0 represents a significant advancement in the realm of language models, featuring a staggering 1.6 trillion parameters through a Mixture-of-Experts architecture that leverages AI ASIC superpods, with approximately 48 billion parameters engaged per token, showcasing exceptional capabilities in coding and agentic tasks. This model marks a notable improvement over its predecessors by integrating a large-scale sparse architecture with specialized post-training methods tailored for tasks in real-world software development, tool utilization, long-context reasoning, and complex agent workflows. Entirely developed and executed on AI ASIC superpods, LongCat-2.0 underwent pretraining that encompassed over 35 trillion tokens and millions of accelerator hours, exemplifying cutting-edge training methodologies on innovative hardware solutions. To enhance its performance on tasks requiring long-term context, the model incorporates LongCat Sparse Attention and is trained using hundreds of billions of tokens from 1M-context datasets, enabling it to effectively manage ultra-long context tasks and ensure robust understanding of lengthy documents. This combination of features positions LongCat-2.0 as a pioneering force in the landscape of advanced language models.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Claude Code
Hermes Agent
OpenClaw
Agent Client Protocol (ACP)
Cline
Hugging Face
IntelliJ IDEA
Kilo Code
Nous Portal
Ollama
OpenAI Codex
OpenCode
OpenRouter
Poolside
Roo Code
Visual Studio
Visual Studio Code
Zed

Integrations

Claude Code
Hermes Agent
OpenClaw
Agent Client Protocol (ACP)
Cline
Hugging Face
IntelliJ IDEA
Kilo Code
Nous Portal
Ollama
OpenAI Codex
OpenCode
OpenRouter
Poolside
Roo Code
Visual Studio
Visual Studio Code
Zed

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Poolside

Founded

2023

Country

United States

Website

poolside.ai/blog/introducing-laguna-xs-2-1

Vendor Details

Company Name

LongCat

Founded

2023

Country

China

Website

longcat.chat/blog/longcat-2.0/

Product Features

Product Features

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