Introduction to K2-Think: UAE’s Efficient AI Competitor to OpenAI & DeepSeek
Excited to know about the great contribution of UAE in AI? We’re pleased to offer you a detailed introduction to K2- Think for your ease. K2 Think, developed by the collaboration between Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) and technology group G42 in Abu Dhabi, United Arab Emirates, is a revolutionary transformation in artificial intelligence reasoning. You might be thinking like me that bigger AI models are always better, but K2 Think has proved it wrong.
It has just 32 billion parameters, but its smart design can beat the reasoning skills of larger models like OpenAI and DeepSeek. We know that DeepSeek R1 is made up of 37 billion parameters, and ChatGPT has 220 billion parameters. This milestone by the UAE has positioned it as a rising power in the global AI landscape.
It’s a great step by the UAE to advance the cause of open-source transparency in a field dominated by proprietary systems of the USA and China. In this AI model, innovative training methodologies and optimization techniques are prioritised rather than scaling parameter counts.Â
The launch on September 9 aligns with the birth anniversary of the late Sheikh Khalifa bin Zayed Al Nahyan, commemorating his pivotal role in establishing the UAE’s foundations in science, technology, and innovation. Through this date, we can also understand the importance of this achievement for the UAE. It represents that this innovation is a part of a larger plan to build an economy that doesn’t just rely on oil but also on technology.
Technical Architecture
Alibaba’s Qwen2.5-32B open-source large language model is the base of K2 Think. The reason behind this foundation is that it had not been pre-optimized for reasoning tasks, so it allows researchers to validate the effectiveness of their novel post-training approach fully.
Moreover, the usage of 32 billion parameters reflects a deliberate design decision to balance capability with accessibility. It enables faster iteration cycles while maintaining sufficient capacity for complex reasoning tasks.
The breakthrough performance of K2 Think is due to a carefully engineered combination of six technical innovations that work synergistically to enhance reasoning capabilities. These six technical pillars with their functions and benefits are given below:
| Pillar | Function | Benefit |
| Long Chain-of-Thought SFT | Step-by-step reasoning training | Enhances logical problem-solving |
| Reinforcement Learning with Verifiable Rewards | Optimizes for correct answers | Improves accuracy on hard problems |
| Agentic Planning | Restructures concepts before reasoning | Mimics human cognitive planning |
| Test-time Scaling | Allocates extra compute during inference | Boosts performance on new data |
| Speculative Decoding | Predicts token sequences | Dramatically increases inference speed |
| Inference-optimized Hardware | Cerebras WSE deployment | Enables unprecedented throughput |
Introduction to K2 Think Performance and Benchmarks
Let’s discuss the performance of K2 Think in different fields and its benchmarks:
Mathematical Reasoning Excellence
K2 Think demonstrates exceptional capabilities in mathematical reasoning to establish a new state-of-the-art performance for open-source models across multiple challenging benchmarks. According to comprehensive evaluations conducted over 16 runs, the model achieves a 90.83% pass@1 score on AIME 2024, 81.24% on AIME 2025, 73.75% on HMMT 2025, and 60.73% on OMNI-Math-HARD.
Through these impressive results, we can understand that this model outperforms or matches much larger systems like GPT-OSS 120B and DeepSeek v3.1 despite having only a fraction of their parameters.
The reason behind the strong mathematical performance of this AI model is its specialized training on competition-level math problems and its ability to engage in extended chain-of-thought reasoning. From the internal tests, we can estimate that the model improved quickly in early training. Its scores stabilized at 79.3% on the AIME 2024 test and 72.1% on the AIME 2025 test before reinforcement learning made it even better.
Multi-Domain Competence
After in-depth analysis, we’ve found that K2 Think also maintains strong performance across other domains like code generation and scientific reasoning. On LiveCodeBench v5, a challenging coding benchmark, the model achieves 63.97% pass@1, while it reaches 71.08% on GPQA-Diamond for scientific reasoning.
This multi-domain capability makes K2 Think particularly valuable for complex real-world applications that require reasoning across different types of problems.
Developers noted that the model learned at different speeds across various tests. Math scores leveled off quickly, while others, like coding, saw steady gains up to 56.4% before additional training boosted them further.
Efficiency and Speed Metrics
The most remarkable achievement of K2 Think is its unprecedented inference speed when deployed on Cerebras hardware. The system achieves approximately 2,000 tokens per second per request, which enables it to generate a 32,000-token response in roughly 16 seconds. This represents a 10x improvement over typical H100/H200 GPU setups, which manage only about 200 tokens per second and would require approximately 160 seconds for the same output.
This exceptional speed results from the combination of speculative decoding optimization. Massive computational power of Cerebras’ Wafer-Scale Engine, which contains 19x more transistors than competing GPUs, is also the cause of its exceptional speed. This efficiency makes advanced reasoning capabilities accessible without requiring exorbitant computational resources.
Let’s further break down the performance benchmark of K2 Think:
| Domain | Benchmark | Performance (pass@1) |
| Mathematics | AIME 2024 | 90.83% |
| Mathematics | AIME 2025 | 81.24% |
| Mathematics | HMMT 2025 | 73.75% |
| Mathematics | OMNI-Math-HARD | 60.73% |
| Code | LiveCodeBench v5 | 63.97% |
| Science | GPQA-Diamond | 71.08% |
Deployment and Accessibility
Let’s discuss the deployment and accessibility of this new AI model in detail:
Open-Source Commitment
K2 Think establishes a new standard for openness in AI development. Unlike many “open-source” models that only release weight parameters, K2 Think is fully open-source to provide complete access to training data, parameter weights, software code for deployment, and test-time optimization techniques.
This unparalleled openness guarantees that every facet of the model’s reasoning process can be examined, replicated, and expanded upon by the global research community. The model builds upon a growing suite of UAE-developed open-source models, including Jais (Arabic), NANDA (Hindi), and SHERKALA (Kazakh).
It further extends the pioneering legacy of K2-65B, the world’s first fully reproducible open-source foundation model released in 2024. This commitment to open source reflects a philosophical belief that openness ultimately wins in terms of breadth of adoption, ecosystem robustness, and total prosperity created.
Hardware Integration and Availability
K2 Think is optimized for deployment on Cerebras’ Wafer-Scale Engine systems, leveraging what the company describes as the “world’s largest and most powerful AI chip”. These systems are built on whole wafers rather than individual chips, containing 4 trillion transistors and delivering 28x more petaflops than NVIDIA’s B200 GPU.
The model uses speculative decoding specifically optimized for Cerebras hardware, enabling its remarkable throughput of 2000 tokens per second. You can access this model through multiple channels, including a dedicated website (https://www.k2think.ai/) and Hugging Face, making it accessible to researchers, developers, and organizations worldwide.
In this comprehensive overview of K2 Thinks, I want to share that some of my friends, who are developers, have indicated plans to incorporate it into a full, large language model in the coming months to expand its capabilities beyond specialized reasoning tasks.
Development Context and Resources
It was developed using several thousand GPUs, with the final training run involving 200-300 chips. This represents a significantly smaller computational investment compared to the training of massive models like DeepSeek’s R1 with 671 billion parameters. The development process was led by MBZUAI’s Institute of Foundation Models, which was launched only in May 2025 and is structured to support distributed teams across Abu Dhabi, Paris, and Silicon Valley.
Global Implications and Future Directions
Curious to know the global implications and future direction of this new competitor of DeepSeek R1 and OpenAI. Let’s have a look at it:
Geopolitical Significance in AI Development
It marks a pivotal advancement in the worldwide AI domain. It gives the rank to the UAE as a third force in the AI race alongside the United States and China. The launching of this model gives us a signal that countries with substantial financial resources and strategic commitment can compete at the highest levels of AI development.
We can clearly understand that the extensive ecosystems of Silicon Valley or Beijing do not matter in front of strategic commitment and financial power.This milestone aligns with the UAE’s comprehensive approach to strengthening its global standing and transitioning towards a more diversified economy, reducing reliance on oil.
Scientific and Practical Applications
Unlike general-purpose chatbots like ChatGPT, K2 Think is specifically designed to excel in scientific and technical domains requiring advanced reasoning. It has significant implications for accelerating research in fields such as mathematics, computer science, and scientific discovery. As Richard Morton, managing director for MBZUAI’s Institute of Foundation Models, explained:
With this particular application, instead of taking 1,000, 2,000 human beings five years to think through a particular question, or go through a particular set of clinical trials or something like that, this vastly condenses that period.
Its efficiency also expands access to advanced AI technologies for researchers and organizations that lack the capital and infrastructure available to major U.S. tech companies. By demonstrating that smaller, more resourceful models can rival the largest systems, K2 Think paves the way for more accessible and affordable AI reasoning capabilities. This could potentially democratize access to state-of-the-art AI tools across various industries and regions.
Future Research Directions
Our team of developers positions K2 Think as a step toward more efficient and transparent AI systems. The techniques pioneered in its development, particularly the six pillars of innovation, provide a roadmap for enhancing reasoning capabilities without exponential parameter scaling.
The integration of agentic planning prior to reasoning represents a particularly promising direction for future research, as evidence suggests this approach mirrors human cognitive processes more closely than traditional immediate reasoning.
The complete openness of the K2 Think system enables us to build upon its foundations, potentially accelerating innovation in efficient AI architectures. We have indicated that more high-performance model releases are forthcoming this year from the Institute of Foundation Models, suggesting that K2 Think represents the beginning rather than the culmination of the UAE’s ambitions in AI development.
Conclusion
K2 Think represents a transformative approach to artificial intelligence that challenges the prevailing “bigger is better” paradigm in AI development. By demonstrating that a strategically engineered 32-billion-parameter model can rival or surpass the reasoning capabilities of systems twenty times its size, the developers have opened new pathways for efficient, accessible, and transparent AI development.
Its exceptional performance in mathematical reasoning, combined with strong capabilities in coding and scientific domains, makes it a valuable tool for our research and practical applications requiring advanced reasoning.
The complete open-source nature of K2 Think sets a new standard for transparency in AI that enables us to study, reproduce, and extend its capabilities. This commitment to openness, combined with the model’s deployment on high-performance Cerebras hardware, makes it both powerful and accessible.
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