K2 Think vs. Muse Spark Open Comparison


K2 Think Institute of Foundation Models	Muse Spark Open Meta	+	+
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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 Muse Spark Open is an open-weight, natively multimodal language model for developers and organizations building AI applications, coding systems, and agentic workflows. The released model has 816 billion total parameters, 42 billion active parameters, and a 1-million-token context window. Teams can download the model weights, run inference through supported engines such as vLLM, SGLang, TensorRT-LLM, Hugging Face Transformers, Apple MLX, llama.cpp, or Unsloth, and customize the model using PyTorch. It supports long-context reasoning, software engineering, coding, tool use, and multimodal inputs. Deployment options include cloud inference through listed API providers or self-hosting on compatible GPU systems; Meta documents a desktop setup using two Apple M5 Ultra Mac Studio computers. The model is released under the Muse Model License with limited commercial terms.
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 Supported Mac Supported Linux Supported Cloud Supported On-Premises 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 AI developers, machine learning engineers, software engineers, AI researchers, enterprise development teams, and organizations seeking customizable open-weight multimodal models for local inference, autonomous agents, complex coding, and long-context AI applications
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 Not 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 $0 Model weights are available at no charge under the Muse Model License, which has limited commercial-use terms. Hosted inference providers set their own usage prices. Free Version 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 Read all reviews
	Pros & Cons from Real Users Pros Meta finally opened up a genuinely frontier-level Muse model. The 1M-token context window and 42B active parameters make it strong enough for large repos, long agent sessions, multimodal work, and complex coding without feeling like a stripped-down open model. The local deployment story is also a huge deal. Being able to run something this capable on two M5 Ultra Mac Studios is impressive, especially for teams that want more control over data, infrastructure, and customization. I also like how much of the stack is open around it. Muse Spark Open works with MLX, vLLM, SGLang, TensorRT-LLM, Transformers, llama.cpp, and Unsloth, and Meta is making the weights available through Hugging Face, SageMaker, Google Cloud Model Garden, and Microsoft Foundry. Cons Even though it is technically desktop-capable, needing two M5 Ultra Mac Studios for the full local experience is still a serious setup, so most developers will probably use hosted inference instead.
Training Documentation Supported Webinars Not Supported Live Online Not Supported In Person Not Supported	Training Documentation Supported Webinars Not Supported Live Online Not Supported In Person Not Supported
Company Information Institute of Foundation Models United States www.k2think.ai/k2think	Company Information Meta Founded: 2004 United States research.meta.ai/blog/muse-spark-open
Alternatives DeepSeek R1 DeepSeek	Alternatives GPT-6.1 Terra OpenAI
Inkling Thinking Machines Lab	Gemini 4 Argon Google
Qwen3.6-27B Alibaba	Claude Sonnet 5.5 Anthropic
Sarvam 105B Sarvam	Muse Glimmer Meta
Kimi K2 Thinking Moonshot AI View All	Recursive Flint Recursive View All
Categories AI Models Supported Large Language Models Supported Open-Weight AI Models Supported	Categories AI Coding Models Supported AI Models Supported AI Reasoning Models Supported AI Receptionists Supported Foundation Models Supported Large Language Models Supported Multimodal Models Supported Open-Weight AI Models Supported

Integrations Baseten Not Supported Fireworks AI Not Supported Groq Not Supported Hugging Face Not Supported Modal Not Supported Nebius Not Supported OpenRouter Not Supported Together AI Not Supported vLLM Not Supported	Integrations Baseten Supported Fireworks AI Supported Groq Supported Hugging Face Supported Modal Supported Nebius Supported OpenRouter Supported Together AI Supported vLLM Supported View All 9 Integrations
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