Introducing K2 Horizon: a connected fleet of six foundation models ranging from 0.9 billion to 375 billion parameters. - Frontier performance: Across coding and agentic tasks, K2 Horizon delivers top-tier performance in every size class—with the 0.9B, 3.7B and 7B models setting new state of the art at their respective scales. - Radical openness: K2 Horizon represents the largest fully open-source model launch in AI history. The fully open code, training data and recipes are a significant step forward in transparency. Launch page: ifm.ai/k2/ Tech blog: ifm.ai/blog/k2 Hugging Face: huggingface.co/collections/I…

The most detailed open LLM data release yet: K2 Horizon's pre-training data and post-training data-generation pipelines are now open! 13 trillion pre-training tokens across 5 datasets and 24 subsets. Over 10 trillion tokens of synthetic data spanning mathematics, reasoning, coding, planning, and problem-solving. Beyond the scale, four highlights for model builders (details in the threads): - Scaling synthetic data without losing diversity. Our generation methods produce diverse training examples at scale, nearly matching natural web text in measured diversity. - More verification isn't always better. Strict verification can throw away useful training signal. - Small-scale wins can mislead. Our chosen data mix actually lost in smaller experiments. - Beyond teacher imitation. Our post-training data generation methods produce training trajectories the teacher couldn't generate on its own. Open pre-training datasets, post-training data-generation pipelines, and processing tools for researchers to inspect, reuse, and build on. Pre-training data: huggingface.co/collections/I… Post-training data-generation code & tools: github.com/ifm-ai/DATA_NAVIG…

The most interesting thing about the current state of the world: Even though everything is accelerating a lot, the overall dynamics of the world seem to stay the same

We're open-sourcing RL360, the RL infrastructure behind K2 Horizon's six domain experts: 8,192 concurrent sandboxes, 150 steps in 7 days, ~1.3M rollouts. Long-horizon agentic RL runs thousands of agents in real sandboxes on tasks that take minutes. Some of those sandboxes crash mid-task, and a crash arrives as the same low score as a wrong answer. Without infrastructure that can keep up, GPUs are underutilized and the model gets penalized for system crashes rather than its own mistakes. RL360 is built for this. We share how it works, what broke at scale, and what we learned in the thread. Code: github.com/ifm-ai/RL360 Run: wandb.ai/mbzuai-llm/rl360-pu…

We're launching our leaderboard for FrontierCode 1.1, a private evaluation from Cognition that tests models on completing coding tasks in a way that would actually get merged by maintainers of real-world repositories @cognition announced FrontierCode in June 2026 and updated it to version 1.1 in July. Cognition built the dataset with open source maintainers who spent more than 40 hours per task. Each task requires the agent to make a change to a real open source repository, then checks both unit tests and qualitative tests for what maintainers would require to merge the change. We run FrontierCode 1.1 Main, the primary 100-task set of the benchmark's 150 tasks, using our Stirrup open source reference harness for apples-to-apples comparisons across models. All tasks are private to avoid contamination. Each attempt is graded with both blocking and non-blocking criteria. An attempt that fails any blocking check scores zero, and one that passes them all scores between 70% and 100%. Unlike other coding evaluations, FrontierCode has a more holistic view of success based on real usage. Models need to pass unit tests, but are also assessed on behaviors like making excessive changes and large diffs, modifying files outside the relevant scope, or adding unhelpful tests. We think this approach is valuable in the current context: models are now very capable at coding tasks, but their changes can still require substantial cleanup before a maintainer would merge them. Key takeaways: ➤ Leading models still fail more than 40% of the time: Claude Opus 5.5 (max with fallback) leads at a score of 52% after passing blockers in 58% of its attempts. Nine of the 100 tasks were not passed in any attempt by any of the models we evaluated for launch ➤ The top of the leaderboard is tightly packed: nine models from six labs score between 43% and 52%. MiMo-V2.6-Pro is the leading open weights model at 45%, followed by DeepSeek V4.1 Flash (max) and Kimi K3 (max) at 44% ➤ GPT-6.1 Sol (max) offers the strongest performance for its cost: At $0.81 per task it scores 49%, matching Claude Fable 5.1 (max with fallback). This means it achieves similar performance to leading Anthropic models, at less than a tenth of the cost per task Thank you to @silasalberti, @ybenpan, @cl571128, and the rest of the team at Cognition for building FrontierCode and collaborating with us to bring it to Artificial Analysis - we’re working to add FrontierCode to our coding agent leaderboard very soon.

K2-Horizon-36B-A4B scores 25 on the Artificial Analysis Intelligence Index, matching models with over 20× the total parameters while using 4B active parameters per token. These capabilities come from our new architecture MoVA (Mixture-of-Value Attention), which incorporates MoE-based sparsity into the compute of value vectors in multi-head attention. It opens a second axis for scaling sparsity in an LLM, beyond MoE in the FFN module. Importantly, MoVA enjoys the following advantages: • Simple and compatible with efficient attention algorithms, such as flash attention, GQA, and sparse attention • No additional KV cache cost comparing to standard GQA K2-Horizon-36B-A4B available at: huggingface.co/IFM/K2-Horizo…

K2-Horizon-7B scores 21 on Artificial Analysis Intelligence Index, punching above its weights. It scores 50% above the next best model under 10B, beating Qwen3.6-35B-A3B and closely matching Qwen3.6-27B. • Try it: huggingface.co/IFM/K2-Horizo… • 8-bit quantization: huggingface.co/IFM/K2-Horizo… • Uno, the speed-up diffusion-augmented version: huggingface.co/IFM/K2-Horizo…