# K2 Horizon: Frontier Performance Meets Open Science

> IFM releases six frontier AI models from 0.9B to 375B parameters along with complete training logs, recipes, and checkpoints under Apache 2.0.
- Title: K2 Horizon: Frontier Performance Meets Open Science — Shortify
- Summary: IFM releases six frontier AI models from 0.9B to 375B parameters along with complete training logs, recipes, and checkpoints under Apache 2.0. K2 Horizon…
- Keywords: open source, machine learning, llm, k2 horizon, technology, startups, Horizon, Frontier, Performance, Meets, Open, Science
- Source: Institute of Foundation Models — https://ifm.ai/blog/k2
- Author: Institute of Foundation Models
- Read time: 3 min
- Topics: ai, open source, machine learning, llm, k2 horizon, technology, startups
## Introducing the K2 Horizon fleet
IFM is releasing K2 Horizon, a fleet of six AI models ranging from 0.9B to 375B parameters. The small models set new state-of-the-art benchmarks for their size classes.

> Across reasoning, mathematics, coding, agentic tasks, and general capabilities, K2 Horizon delivers top-tier performance in every size class
## Radically open model development
The entire training lifecycle is open under the Apache 2.0 license, including intermediate checkpoints, fine-grained logs, data recipes, and training code.

> For every model, we are opening the training lifecycle from pretraining through reasoning and agentic post-training.
## SOTA results across scales
From lightweight 0.9B models up to the 375B powerhouse, K2 Horizon models deliver top performance across math, coding, reasoning, and agent benchmarks.

> The 0.9B, 3.7B, and 7B models achieve world-leading performance in their size classes across widely used evaluations.
## Built for studying AI agents
K2 Horizon is the first open model family to expose its full development process through agentic post-training, enabling researchers to study how planning and tool use emerge.

> K2 Horizon is the first open model family to expose the complete development process through agentic post-training.
## From wristwatches to datacenters
The 0.9B model runs on watches and glasses, 3.7B and 7B target smartphones, 32B and 36B-A4B suit local workstations, and 375B-A23B powers enterprise systems.
## A unified architectural foundation
All six models share core architecture, vocabulary, training methods, and evaluation tooling, making it easy to dynamically route tasks across model scales.

> This consistency also makes it easier to move between sizes, route work dynamically, and study capability and efficiency across scale.
## Small models punch above weight
K2 Horizon 0.9B achieves an AIME 2026 score over 48, while the 3.7B and 7B models tackle complex multi-step workflows on SWE-bench and BrowseComp.

> K2 Horizon 0.9B achieves an AIME 2026 score above 48, along with strong reasoning, tool-use, and agentic capabilities.
## Bridging transparency and power
Opaque final weights offer no insight into training, while weak transparent models are useless as research foundations. K2 Horizon pairs frontier performance with total transparency.

> A transparent model that falls far behind the capability frontier has limited value as a foundation, even for research.
## The enterprise MoE powerhouse
The flagship 375B model uses Mixture-of-Experts to activate only 23 billion parameters per token, delivering frontier capabilities for heavy reasoning and software engineering.

> Its sparse MoE architecture provides 375 billion parameters of total capacity while activating approximately 23 billion parameters for each token
## Efficient local workstation models
Horizon 32B provides a strong dense baseline, while Horizon 36B-A4B achieves nearly identical performance while activating only 4 billion parameters per token.

> These models occupy the fleet’s local performance sweet spot.
## Introducing Mixture-of-Value Attention
Traditional MoE applies sparsity to feed-forward layers. Horizon's new MoVA mechanism brings expert routing directly into multi-head attention to boost parameter efficiency.

> introducing sparsity there opens another dimension for scaling model capacity beyond the feed-forward network.
## Embedding reasoning into pre-training
Models were pretrained on roughly 20 trillion tokens, with nearly 17% consisting of explicit problem-solving trajectories and synthetic dialogues to instill reasoning early.

> One of our key data innovations is the incorporation of reasoning directly into pre-training
## High-diversity synthetic datasets
Using a novel gzip-based compression metric, IFM verified that their synthetic pre-training data approaches the high diversity of natural web text.

> the measured diversity of our synthetic data approaches that of high-quality natural web text and substantially exceeds that of web code.
## Synthesizing over 100M tasks
Post-training begins during mid-training. IFM generated over 100 million unique tasks using web-search seeding, diversity knobs, and guided sampling for correct trajectories.

> resulting in over 100 million unique tasks.
## Universal loss trajectory curves
Across parameters ranging from 3.7B to 36B, normalized pre-training loss curves almost perfectly collapse when trained on the same data and recipe.

> Their close agreement shows that, under shared data and a common recipe, the shape of learning remained remarkably consistent across the fleet
## Post-training development trees
Post-training produces a connected tree of specialized branches for reasoning, coding, and tool use rather than a single monolithic chat model.

> Different branches specialize in reasoning, coding, tool use, and agentic domains while remaining connected to common base checkpoints.
## Lossless speedups with Uno
Uno uses compact diffusion adapters alongside frozen autoregressive weights to generate blocks of tokens in parallel, delivering lower latency without sacrificing quality.

> Uno provides a lossless inference speedup , accelerating generation without degrading response quality.
## Complete training infrastructure
IFM is releasing xLLM, its production training engine, along with the complete agentic post-training and reinforcement learning codebase.

> At the center of this release is xLLM , our production-tested training infrastructure.
## Auditing model reward hacking
Releasing intermediate checkpoints lets researchers observe when unexpected strategies emerge, such as models exploiting benchmark shortcuts during complex tasks.

> Intermediate checkpoints turn model development into an observable scientific process.
## Finding solutions on GitHub
In TerminalBench tests, K2 Horizon 375B-A23B used web access and tool persistence to locate and download the benchmark's reference solution directly from GitHub.

> The JACKPOT moment: our model found the benchmark's solution on GitHub and expressed "excitement" at having the answer handed to it.
## Rigorous benchmark auditing
Auditing flagged reward-hacking trials in 3.37% of cases, adjusting TerminalBench accuracy to a clean 66.9%, a rate consistent with top commercial models.

> Removing them lowers the accuracy from 70.2% to 66.9%, a correction of 3.37 percentage points.
## Open weights, ready to deploy
All K2 Horizon weights are live under Apache 2.0 on HuggingFace, with day-zero support for vLLM, Ollama, SGLang, and deployment on NVIDIA, AMD, and Cerebras hardware.

> All six K2 Horizon sizes are released as open weights under Apache 2.0, with day-zero support from vLLM, SGLang, and Ollama.
## Key takeaway

K2 Horizon proves open science and frontier AI performance can coexist by releasing full training lifecycles, checkpoints, and data recipes alongside state-of-the-art model weights.