On September 28 2026, Hugging Face released Holo4, a family of agentic AI models that can operate across graphical interfaces, raw code, machine-checkable protocols and traditional APIs using the same weights, enabling versatile automation in everyday workflows without needing separate models for each platform.
Short answer: On September 28 2026, Hugging Face released Holo4, a family of agentic AI models that can operate across graphical interfaces, raw code, machine-checkable protocols and traditional APIs using the same weights, enabling versatile automation in everyday workflows without needing separate models for each platform.
Hugging Face announced the release of Holo4 on September 28, 2026, introducing a new family of agentic models designed to work across many computer interfaces. The series comes in two configurations: a 27-billion-parameter dense model and a 35-billion-parameter mixture-of-experts version labeled 35B-A3B. Both are accessible through the H Models API, and alongside them the team updated the earlier Holotron 3 release to a lighter variant called Holotron4 Nano.
What sets Holo4 apart is its ability to switch between graphical user interfaces, raw code, machine-checkable protocols and traditional APIs depending on what the task requires. Earlier agentic systems tended to specialize in one mode, leaving them helpless when faced with software that lacks the expected interaction method. Holo4 avoids this limitation by using the same weights and calling convention whether it is clicking buttons on a desktop, editing a script in a sandbox, or invoking a cloud service. Users therefore do not need to maintain separate models for different platforms.
On the OSWorld 2.0 benchmark, which measures long-horizon desktop control, the 27-billion model achieved a score of 61.7 percent while the 35B-A3B variant reached 30.9 percent. These results trail only the top closed models such as Opus 5.5, which scored 81.8 percent, but they were obtained with far fewer parameters and at a markedly lower cost per run. The cost estimates are based on token consumption and reflect the pricing of the H Models API, showing that Holo4 delivers competitive performance for a fraction of the expense incurred by larger proprietary alternatives.
A similar story emerges from AutomationBench, a suite that evaluates API-driven task completion. In internal measurements Holo4 matched the scores of leading models while consuming significantly fewer tokens, translating into a lower cost per task. The team noted that they will soon publish results on the private split of the benchmark once the evaluation is complete.
To illustrate practical utility, the post walks through three concrete examples. In a FreeCAD session the 27-billion model built a scaled 3D replica of the Eiffel Tower using 84 tool calls and 1.3 million tokens, outperforming its base Qwen3.8 counterpart which required 60 calls and 1.0 million tokens. Modeling the company logo in the same environment needed 94 calls and 1.5 million tokens for Holo4 versus 118 calls and 1.9 million tokens for the baseline. Finally, constructing a self-playing Pac-Man clone in Godot took 68 calls, 2.4 million tokens and 268 lines of code for Holo4, while the base model needed 197 calls, 11.4 million tokens and 327 lines. Across all cases Holo4 achieved the goal with fewer steps and less computational overhead.
The models were trained via a combination of supervised and reinforcement learning on a broad collection of environments and tasks. Many of these scenarios originated from the internal Agentic Task Factory, a system that turns documentation, screenshots and open-source software into interactive, verifiable challenges. To date the factory has generated roughly ten thousand tasks spanning web applications, MCP servers and desktop setups, including hybrid configurations that expose the same state through both a graphical interface and a machine-checkable protocol. Training also involved rebuilding the execution harness, the loop that carries out model actions and manages context over hundreds of steps, using feedback from failures observed on OSWorld 2.0.
For developers interested in experimenting with Holo4, the models are immediately downloadable from the Hugging Face Hub under the Holo4 collection, which includes FP16, FP8 and GGUF variants. Full trajectories that underlie the benchmark scores are available for replay at trajectories.hcompany.ai or as downloadable datasets. Quickstart guides for the H Models API show how to send prompts and receive actions in a uniform way regardless of the target platform. By integrating these agents into existing workflows, teams can automate repetitive GUI interactions, script generation and API orchestration without maintaining multiple specialized models.
Hugging Face announced the release of Holo4 on September 28, 2026, introducing a new family of agentic models designed to work across many computer interfaces.
The series includes a 27-billion-parameter dense model and a 35-billion-parameter mixture-of-experts version labeled 35B-A3B; both are accessible through the H Models API.
On OSWorld 2.0, the 27-billion model scored 61.7 percent and the 35B-A3B variant scored 30.9 percent; these results trail only the top closed models such as Opus 5.5, which scored 81.8 percent.
In internal measurements Holo4 matched the scores of leading models while consuming significantly fewer tokens, translating into a lower cost per task.
Source: Hugging Face
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