**Meet K2 Suppose V2: The AI Model that’s Revolutionizing the Way We Reason**
In a breakthrough that’s sending shockwaves through the tech world, researchers at the Mohamed bin Zayed College of Synthetic Intelligence (MBZUAI) have unveiled K2 Suppose V2, a groundbreaking open reasoning model that’s destined to change the game in math, code, science, and beyond. So, what makes this model so special?
**The Birth of a Reasoning Mastermind**
K2 V2 is built upon a whopping 70 billion parameter base model, with a dense decoder-only transformer architecture featuring 80 layers, a hidden dimension of 8192, and 64 attention heads. It was trained on an astonishing 12 trillion tokens drawn from a vast range of sources, including web text, math, code, and scientific literature. The training process was a three-phased affair, involving pretraining, mid-training, and fine-tuning. The key takeaway is that K2 V2 is not just another generic base model – it’s been explicitly optimized for long context consistency and exposure to reasoning behaviors.
**A Sovereign Revelation on the GURU Dataset**
K2 Suppose V2 was trained using a General Reinforcement Learning for Predictive Onboarding (GRPO) recipe, on top of the K2 V2 Instruct base model, on the Guru dataset (version 1.5). This dataset focuses on math, code, and STEM questions derived from permissively licensed sources, ensuring that both the base model data and the RL data are curated and documented by the same institute – making this a truly sovereign claim.
**Benchmark Breakdown: The Numbers Speak for Themselves**
K2 Suppose V2 is all about crushing reasoning benchmarks. On AIME 2025, it scores a pass rate of 90.42, while on HMMT 2025, it clocks in at 84.79. On GPQA Diamond, a challenging graduate-level science benchmark, it reaches 72.98. On SciCode, it scores 33.00, and on Humanity’s Final Examination, it reaches 9.5 under the benchmark settings.
**Security and Openness: The Fine Print**
The research team reports a Security 4 fashion evaluation, which aggregates four security surfaces – content and public security, truthfulness and reliability, and societal alignment – all of which reach macro-average risk levels in the low range. Data and infrastructure risks remain higher, reflecting concerns about sensitive personal data handling rather than model behavior alone. The team notes that K2 Suppose V2 still shares the generic limitations of large language models, despite these mitigations.
**The Bottom Line**
K2 Suppose V2 is a game-changing 70B reasoning model built on K2 V2 Instruct, with open weights, open data recipes, detailed training logs, and full RL pipeline released through Reasoning360. This revolutionary model is set to transform the way we approach reasoning in math, code, science, and beyond – and we can’t wait to see what it can do.
