K2-Think - An open-source AI inference model that excels in mathematics and coding.

What is K2-Think?

K2-Think is an open-source inference model jointly developed by the Mohammed bin Zayed University for Artificial Intelligence (MBZUAI) in the UAE and G42. With 32 billion parameters, it excels in multiple fields, including mathematical reasoning, code generation, and scientific knowledge, achieving particularly outstanding results in mathematical competition benchmarks. The model achieves efficient inference through long-chain thinking-supervised fine-tuning and reinforcement learning techniques, reaching an inference speed of over 2000 tokens per second on the Cerebras Wafer-Scale Engine. Its open-source nature and efficient inference capabilities make it a highly attractive option for building advanced AI inference systems.

Main functions of K2-Think

• Mathematical reasoningThey excel in mathematical problem-solving, achieving high scores in benchmark tests such as AIME and HMMT, and are capable of handling complex mathematical problems.
• Code generationIt can generate high-quality code, supports multiple programming languages, and is suitable for programming assistance and code generation tasks.
• Science Q&AThey also possess strong scientific knowledge and reasoning abilities, enabling them to answer science-related questions.
• Multi-domain reasoningIn addition to math, coding, and science, K2 Think can handle a variety of reasoning tasks.
• Safety and reliabilityIt performs excellently in terms of security, effectively rejecting high-risk content and possessing strong dialogue robustness and data protection capabilities.

K2-Think's Technical Principles

• Long Chain-of-thought Supervised FinetuningBy using supervised learning, the model is trained to perform long-chain thinking, which helps to better understand and generate complex reasoning processes.
• Reinforcement Learning with Verifiable Rewards (RLVR)Based on reinforcement learning techniques and combined with a verifiable reward mechanism, the reasoning process of the model is optimized, thereby improving the accuracy and reliability of reasoning.
• Agentic PlanningPerforming proxy planning before inference helps the model better organize the inference process and improves inference efficiency.
• Test-time scalingThe model's parameters are dynamically adjusted during the reasoning process to adapt to different reasoning tasks and improve the model's generalization ability.
• Speculative Decoding: In the decoding process, a speculative method is used to predict possible outputs in advance, thereby accelerating the reasoning process.
• Inference-Optimized HardwareUtilizing high-performance hardware such as the Cerebras Wafer-Scale Engine enables efficient inference computation, significantly improving inference speed.

K2-Think project address

• Project official websitehttps://www.k2think.ai/
• GitHub repositoryhttps://github.com/MBZUAI-IFM/K2-Think-SFT
• HuggingFace model libraryhttps://huggingface.co/LLM360/K2-Think
• arXiv technical paper: https://arxiv.org/pdf/2509.07604

Application scenarios of K2-Think