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What is Laguna S 2.1?

Laguna S 2.1 represents a state-of-the-art open weight coding model that focuses on the completion of long-term projects and demonstrates exceptional reasoning abilities. With a Mixture-of-Experts architecture comprising 118 billion parameters, it engages 8 billion parameters per token and supports a context window of up to one million tokens in both cognitive and non-cognitive modes. The model’s optimized active size enables it to execute complex tasks on local systems while remaining competitive with much larger models across a variety of benchmarks, such as terminal usage, software development, codebase question answering, and tool application. Built for durability, Laguna S 2.1 is adept at addressing demanding challenges with an emphasis on thorough verification and a willingness to backtrack when necessary, rather than hastily claiming victory. In real-world scenarios, it has successfully engineered a browser rendering engine from the ground up, improved an agent harness for faster execution and lower memory requirements, and conducted comprehensive mathematical investigations using the tools available in its environment, showcasing its adaptability and proficiency. This remarkable array of capabilities positions Laguna S 2.1 as an invaluable asset for developers in search of cutting-edge solutions, making it a top choice in the ever-evolving landscape of coding models.

What is Kimi K2 Thinking?

Kimi K2 Thinking is an advanced open-source reasoning model developed by Moonshot AI, specifically designed for complex, multi-step workflows where it adeptly merges chain-of-thought reasoning with the use of tools across various sequential tasks. It utilizes a state-of-the-art mixture-of-experts architecture, encompassing an impressive total of 1 trillion parameters, though only approximately 32 billion parameters are engaged during each inference, which boosts efficiency while retaining substantial capability. The model supports a context window of up to 256,000 tokens, enabling it to handle extraordinarily lengthy inputs and reasoning sequences without losing coherence. Furthermore, it incorporates native INT4 quantization, which dramatically reduces inference latency and memory usage while maintaining high performance. Tailored for agentic workflows, Kimi K2 Thinking can autonomously trigger external tools, managing sequential logic steps that typically involve around 200-300 tool calls in a single chain while ensuring consistent reasoning throughout the entire process. Its strong architecture positions it as an optimal solution for intricate reasoning challenges that demand both depth and efficiency, making it a valuable asset in various applications. Overall, Kimi K2 Thinking stands out for its ability to integrate complex reasoning and tool use seamlessly.

Media

Media

Integrations Supported

Hugging Face
OpenClaw
Claude Code
IntelliJ IDEA
Kilo Code
Nous Portal
OpenCode

Integrations Supported

Hugging Face
OpenClaw
GPT-5.1 Pro
GPT-5.1 Thinking
GPT-5.2
GPT-5.3 Instant
GPT-5.4 nano
GPT-5.5
GPT-5.6 Sol Ultrafast
GPT-6 Astra
GPT-6 Luna
GPT‑5.4 Thinking
Holo4
OpenAI Bel
Zo Computer

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Free
Open source
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars

Training Options

Documentation Hub

Company Facts

Organization Name

Poolside

Date Founded

2023

Company Location

United States

Company Website

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

Company Facts

Organization Name

Moonshot AI

Date Founded

2023

Company Location

United States

Company Website

moonshotai.github.io/Kimi-K2/thinking.html

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Open-Weight AI 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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