Today IFM is releasing K2 Horizon, a connected fleet of six models: 375B-A23B, 36B-A4B, 32B, 7B, 3.7B, and 0.9B. Across reasoning, mathematics, coding, agentic tasks, and general capabilities, 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.
K2 Horizon is also our most comprehensive open release to date. For every model, we are opening the training lifecycle from pretraining through reasoning and agentic post-training. We are releasing intermediate checkpoints, training data or detailed data-construction recipes, open architecture, mixture compositions, training code, configurations, fine-grained logs, evaluation results, and final weights.
The models and code are released under the Apache 2.0 license. Datasets are released under their applicable licenses, such as ODC-BY; We disclose how the data was constructed and mixed when redistribution is not possible.
Together, K2 Horizon represents the most comprehensive open model release to date:
The 0.9B, 3.7B, and 7B models achieve state-of-the-art results in their respective classes across mathematics, reasoning, general capability, coding, and agentic tasks.
The 36B-A4B model performs beyond the level normally expected from its active parameter count, demonstrating the efficiency of our unique Mixture-of-Expert design when computing attention values. The 32B and 375B-A23B models place among the top models in their respective comparison classes.
The small models are especially notable. K2 Horizon 0.9B achieves an AIME 2026 score above 48, along with strong reasoning, tool-use, and agentic capabilities. K2 Horizon 3.7B and 7B extend these capabilities to more demanding software-engineering and multi-step environments, demonstrated on strong performance in SWE-bench and BrowseComp. Although complex tasks that require extensive exploration and repeated recovery, such as those in TerminalBench, remain difficult for the smallest models, K2 Horizon moves the boundary of what is possible at every scale.
A transparent model that falls far behind the capability frontier has limited value as a foundation, even for research. At the same time, a powerful model released only as final weights allows people to run it, but provides little insight into how its capabilities were created.
K2 Horizon brings these two together. The fleet provides highly competitive models and releases the recipes used to train them. Researchers can study advanced capabilities in models strong enough to exhibit them, while developers can reproduce, adapt, and extend the methods rather than treating the final checkpoint as an opaque starting point.
Since introducing the fully open principle in our 2023LLM360 paper, we have released open models every year while extending that commitment to larger scales, stronger capabilities, and now the complete lifecycle through agentic post-training.
K2 Horizon 375B-A23B is the fleet’s largest and most capable model. Its sparse MoE architecture provides 375 billion parameters of total capacity while activating approximately 23 billion parameters for each token, allowing it to draw on the capacity of a much larger model without using every parameter for every token.
The model ranks among the top models below 400 billion parameters across general, reasoning, coding, and agentic evaluations. It is designed for demanding workloads where model quality matters most, including complex reasoning, software engineering, research, and long-horizon agentic tasks.