K2 Horizon just shipped as six new fully open models — developers aren’t fully convinced

Based in the Emirati capital, Abu Dhabi, the Institute of Foundation Models (IFM) introduced K2 Horizon last week. This group of six AI foundation models, ranging from 0.9 billion to 375 billion parameters, is claimed to be the “largest fully open-source fleet of AI models” yet made available.

IFM uses “fully open” to mean more than mere downloadable model weights. Across K2 Horizon, it has committed to publishing training and evaluation code, training data where redistribution is possible or detailed construction recipes where it is not, plus configurations, logs and intermediate checkpoints spanning pretraining through agentic post-training.

The aim is to let developers inspect how the models were built, reproduce their development and adapt them for their own work. But that commitment should not be confused with complete availability at launch: All six models had downloadable weights, while the model cards for the 0.9B, 32B and flagship 375B said some training data, code or checkpoints would arrive later. The 32B release was also only a Stage 1 checkpoint, with the final model still to come.

“Open source is much more than open weights. Science works when others can see the data, follow the method, reproduce the result, and improve on it,” said Eric Xing, IFM founder and university professor at the Mohamed bin Zayed University of Artificial Intelligence. “K2 Horizon delivers on that need. Every model in the fleet ships with its training data, recipe, and evaluations. This is open science, and we believe it’s the best path forward for AI.”

“Open source is much more than open weights. Science works when others can see the data, follow the method, reproduce the result, and improve on it.”

All the open model components you can think of

IFM says it is opening the full training lifecycle for every K2 Horizon model, from pretraining through reasoning and agentic post-training. For each model, it is releasing — or has committed to release — intermediate checkpoints, training data, or detailed data-construction recipes. The checkpoints are snapshots saved throughout training, allowing researchers to examine how a model develops and reproduce or resume particular stages. The broader artifact set includes architecture details, mixture compositions, training code, configurations, fine-grained logs, evaluation results, and final weights.

Across reasoning, mathematics, coding, and agentic tasks, the K2 Horizon team says every model size performs well. The 0.9B model is designed for highly constrained environments such as smartwatches and smartglasses, 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 deliver stronger performance for local hosting and on-premises servers. The 375B-A23B model brings the fleet’s strongest capabilities to demanding enterprise deployments.

The six models share a core architecture, vocabulary, training methodology, interfaces, and deployment tooling, with the 0.9B model having a smaller vocabulary. IFM’s dynamic model routing technique directs tasks to the most cost-effective model and provides developers with a path from prototype to production. The organization insists that the fully open code, training data, and recipes are “a significant step forward in transparency” that go “well beyond” the open-weights dialogue that has dominated AI industry headlines this year.

Could this openness have been more… open?

The IFM team is clearly aiming for differentiation through all-encompassing openness, but could it have been more open… and will it need to be even more open in the future?

To answer that question, the level of openness varied by model at launch. The 3.7B and 7B models shipped with the full artifact set — including weights, recipes, training code and data — while the 0.9B model card, the documentation describing its capabilities and limitations, said its data and code were still forthcoming. The flagship 375B-A23B and sparse 36B-A4B initially arrived with final weights, with full training code, raw datasets, and intermediate checkpoints promised in later updates. The 32B model shipped as an incomplete Stage 1 checkpoint, with the final model and remaining artifacts to follow.

But really, the real question of open purity comes down to the more granular aspects of model training, and this is the stuff that will either delight or infuriate developers. If published data for each model size and synthetic generation pipelines aren’t fully reproducible, AI engineers likely won’t be impressed.

According to Nitish Garg, founder & CEO of AI super-app company CellCog, in his analysis of K2 Horizon and its model training processes, “Reasoning traces for math were rewritten into dialogues and study guides and mixed into pretraining rather than saved for post-training. Compute is not disclosed anywhere: no accelerator count, no hours, no cost. For a release whose thesis is inspectability, that is the one obvious hole, and the fine-grained training logs, when they arrive, may fill it.”

“Compute is not disclosed anywhere: no accelerator count, no hours, no cost. For a release whose thesis is inspectability, that is the one obvious hole, and the fine-grained training logs, when they arrive, may fill it.”

Reproducibility mission impossible: what open models need to share

The narrative here suggests that releasing synthetic datasets is good. Still, if open frontier model companies do this without also providing the full generator prompts (text inputs that direct AI models to create synthetic training data), seed code (as it sounds, core code that controls and initiates the dataset generation process), or exact filtering heuristics (where low-quality synthetic data needs to be cleaned or removed), then developers will find that true end-to-end reproducibility is hard, problematic, and in some cases impossible.

Going further, IFM said it has shared methodologies, but developers will also demand execution specifics, including precise details of the hardware topology used to run complex models. Engineers might also like to see distributed communication configurations to examine how parallel processors exchange data during training. We could also point to the need for optimizer state records, the parameters that track ongoing model optimization progression for each training iteration.

Developers openly discussing this topic have not held back. However, the conversation has quickly veered from K2 Horizon to Chinese labs becoming prominent suppliers of open-weight models — though their training data and full training stacks generally remain closed.

Reacting to one user who claimed that “Chinese models these days don’t even release pre-trained weights anymore” and that all developers get now is the finished post-trained product, user culi retorted, “No? That’s absolutely not true. Qwen, GLM, Kimi, DeepSeek, etc all consistently release both the post-trained “Instruct/Chat” versions and the underlying ‘base’ (pre-trained) weights.”

Joining in the fray, Hacker News user thepasch thinks that there is true open-weight openness, but that it has limits. “Inference code, yes, but the specifics of their training process (as well as the training of the vast majority of all other open-weight models) are still a complete black box, and I can’t think of any Chinese model that made its training corpus public.”

We’re the 360-degree open source of open source

Speaking in a recent video interview (6:51), Hector Liu, director of IFM’s Silicon Valley lab, said, “In AI, recently, people have some confusion about the [term] open source. People sometimes open-weight their final model, but they don’t let you know how things are trained, how production is done… so at IFM we are the pioneer of 360 [degree] open source or fully open source.”

Separately, he said developers can prototype on the smallest model, scale to the flagship, and verify every claim IFM makes along the way. Statements that not everybody really understands the difference between different open approaches to technology should not come as news to anyone. Still, the imbalance in perception here is clearly on show.

The weights are available on Hugging Face, with launch-day support for vLLM and SGLang. The K2 Horizon API is available through IFM’s inference partners, including Compass, Cerebras, and Nebius. K2 Horizon models and code are released under the Apache 2.0 license.

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