IFM发布K2 Horizon六款开源模型及完整训练数据

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:

• A new performance frontier across scales. The 0.9B, 3.7B, and 7B models achieve world-leading performance in their size classes across widely used evaluations. The 36B-A4B model, equipped with our new Mixture-of-Value-Attention (MoVA) mechanism, delivers exceptional capability per active parameter, outperforming some much larger models. The 32B and 375B-A23B models rank among the top models in their respective classes. Together, the six models provide competitive performance across deployment environments ranging from edge devices to the enterprise.
• The first fully open model fleet for agents. K2 Horizon is the first open model family to expose the complete development process through agentic post-training. By releasing checkpoints, data (or data recipe), code, configurations, and training logs across every stage, K2 Horizon makes it possible to study how reasoning, tool use, planning, and agentic capabilities emerge; reproduce the methods that create them; and adapt those methods to new tools, environments, and domains.
• Six models spanning edge to enterprise. The 0.9B model is designed for highly constrained environments such as watches and glasses, while the 3.7B and 7B models bring advanced capabilities to phones and other on-device applications. The dense 32B model and sparse 36B-A4B model provide powerful options for local workstations and efficient serving. The 375B-A23B model brings the fleet’s strongest capabilities to demanding enterprise deployments. All six models include quantization support.
• One connected fleet. The six models share core architecture, vocabulary, training methodology, interfaces, evaluation infrastructure, and deployment tooling, with a smaller vocabulary for the 0.9B model. This consistency also makes it easier to move between sizes, route work dynamically, and study capability and efficiency across scale.

World-leading performance across the scales

IFM/K2-Horizon

SWE-Atlas-QnA and SWE Bench Pro: strict = no internet. BrowseComp: different models use different harness, we use the Discard-all@95k context length proposed in DeepSeek-V3.2 technical report. WildClawBench: we use a subset of the English text-only-modality tasks. Apex-Agents: we use a subset of text-only-modality tasks. GDPVal-AA is the Elo rating.

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.

Why the Horizon Fleet matters

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 2023 LLM360 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.

A Deep Dive into The K2 Horizon Fleet

K2 Horizon 375B-A23B: the enterprise powerhouse

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.

Like every model in the Horizon fleet, 375B-A23B is released not as a single endpoint but as a development tree. Its intermediate checkpoints and post-training branches expose how the base model develops into reasoning, instruction-following, and specialized agentic variants.

IFM/K2-Horizon-375B-A23B

Benchmark comparison for IFM/K2-Horizon-375B-A23B.

K2 Horizon 32B and 36B-A4B: strong performance for local deployment

Horizon 32B is the fleet’s most powerful dense model, providing a strong balance of capability, adaptability, and local deployability. It ranks among the top dense models below 40 billion parameters.

Horizon 36B-A4B reaches nearly the performance of the dense 32B model while activating only approximately 4 billion parameters per token. Its efficiency comes from MoVA, our new sparse attention architecture, together with MoE feed-forward layers.

These two models serve as an important reference point for studying how dense and sparse architectures behave under similar training conditions.

These models occupy the fleet’s local performance sweet spot. They are powerful enough for demanding reasoning, coding, and agentic applications while remaining practical for local workstations and efficient serving systems.

IFM/K2-Horizon-MoVA-36B-A4B

GPT 5.6 luna at medium reasoning effort; Muse Glimmer-30B at high.

K2 Horizon 7B, 3.7B, and 0.9B: frontier capability at small scale

k2 Horizon 7B and 3.7B deliver strong reasoning, mathematics, coding, tool-use, and agentic performance while remaining suitable for local and on-device deployment. On several evaluations, their results approach or exceed those of models many times larger from the previous generation.