K2 Think vs. Laguna S 2.1 Comparison


K2 Think Institute of Foundation Models	Laguna S 2.1 Poolside	+	+
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About K2 Think is an open source advanced reasoning model developed collaboratively by the Institute of Foundation Models at MBZUAI and G42. Despite only having 32 billion parameters, it delivers performance comparable to flagship models with many more parameters. It excels in mathematical reasoning, achieving top scores on competitive benchmarks such as AIME ’24/’25, HMMT ’25, and OMNI-Math-HARD. K2 Think is part of a suite of UAE-developed open models, alongside Jais (Arabic), NANDA (Hindi), and SHERKALA (Kazakh), and builds on the foundation laid by K2-65B, the fully reproducible open source foundation model released in 2024. The model is designed to be open, fast, and flexible, offering a web app interface for exploration, and with its efficiency in parameter positioning, it is a breakthrough in compact architectures for advanced AI reasoning.	About Laguna S 2.1 is an open weight agentic coding model designed to pursue longer-horizon work and make effective use of reasoning. It uses a 118-billion-parameter Mixture-of-Experts architecture with 8 billion active parameters per token and supports a context window of up to one million tokens in both thinking and no-thinking modes. Its compact active size makes it suitable for complex work on local machines while remaining competitive with models many times larger on terminal, software-engineering, codebase-question-answering, and tool-use benchmarks. Laguna S 2.1 is built to keep working through difficult tasks with greater persistence, verification, and willingness to backtrack instead of declaring success too early. In demonstrated runs, it built and validated a browser rendering engine from an empty folder, optimized an agent harness for faster execution and substantially lower memory allocation, and completed extended mathematical research using the tools in its environment.
Platforms Supported Windows Not Supported Mac Not Supported Linux Not Supported Cloud Supported On-Premises Not Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported	Platforms Supported Windows Not Supported Mac Not Supported Linux Not Supported Cloud Supported On-Premises Not Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported
Audience Researchers, developers, or institutions wanting a tool providing a model for mathematical or logical tasks, especially where resources are limited but performance is required	Audience Developers and engineering teams seeking an efficient reasoning model for long-horizon coding, software engineering, and agentic tasks
Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported	Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported
API Offers API Not Supported	API Offers API Not Supported
Screenshots and Videos View more images or videos	Screenshots and Videos View more images or videos
Pricing Free Free Version Supported Free Trial Not Supported	Pricing No information available. Free Version Not Supported Free Trial Not Supported
Reviews/Ratings Overall 0.0 / 5 ease 0.0 / 5 features 0.0 / 5 design 0.0 / 5 support 0.0 / 5 This software hasn't been reviewed yet. Be the first to provide a review: Review this Software	Reviews/Ratings Overall 5.0 / 5 ease 5.0 / 5 features 4.0 / 5 design 5.0 / 5 Read all reviews
	Pros & Cons from Real Users Pros Laguna S 2.1 looks awesome from a developer’s point of view because it is built specifically for agentic coding, not just general chatbot tasks. I like that it is open-weight, relatively compact for its capability, and designed for the kind of workflows where an AI needs to inspect a repo, reason through changes, edit code, and keep moving across multiple steps. The 1M-token context window is a huge plus. For real engineering work, context is everything: source files, docs, logs, tests, tickets, configs, and previous attempts all matter. Having a coding model that can handle that much context makes it much more useful for serious repo-level work. Cons It is still new, so I would want to test it heavily before trusting it with production code. Coding benchmarks are useful, but the real test is messy repos, weird dependencies, flaky tests, security-sensitive changes, and long-running agent loops.
Training Documentation Supported Webinars Not Supported Live Online Not Supported In Person Not Supported	Training Documentation Supported Webinars Supported Live Online Not Supported In Person Not Supported
Company Information Institute of Foundation Models United States www.k2think.ai/k2think	Company Information Poolside Founded: 2023 United States poolside.ai/blog/introducing-laguna-s-2-1
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QwQ-32B Alibaba	Laguna XS 2.1 Poolside
Olmo 3 Ai2 View All	LongCat-2.0 LongCat View All
Categories AI Models Supported Large Language Models Supported	Categories AI Coding Models Supported AI Models Supported

Integrations Agent Client Protocol (ACP) Not Supported Claude Code Not Supported Cline Not Supported Hermes Agent Not Supported Hugging Face Not Supported IntelliJ IDEA Not Supported Kilo Code Not Supported Nous Portal Not Supported Ollama Not Supported OpenAI Codex Not Supported OpenClaw Not Supported OpenCode Not Supported OpenRouter Not Supported Poolside Not Supported Roo Code Not Supported Visual Studio Not Supported Visual Studio Code Not Supported Zed Not Supported Show More Integrations	Integrations Agent Client Protocol (ACP) Supported Claude Code Supported Cline Supported Hermes Agent Supported Hugging Face Supported IntelliJ IDEA Supported Kilo Code Supported Nous Portal Supported Ollama Supported OpenAI Codex Supported OpenClaw Supported OpenCode Supported OpenRouter Supported Poolside Supported Roo Code Supported Visual Studio Supported Visual Studio Code Supported Zed Supported Show More Integrations View All 18 Integrations
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