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Holo4: powering generalist computer-use agents
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Hugging Face Blog

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AI LabsHugging Face Blog

Holo4: powering generalist computer-use agents

Holo4 is our new series of agentic models. It comes in two sizes: 27B dense and 35B-A3B Mixture of Experts. Both are available on the H Models API. We are also releasing an updated version of Holotron 3: Holotron4 Nano.

Holo4 builds on our previous model and interacts with software through any available interface: GUIs, code, MCP and APIs. It scores well on academic benchmarks, but we built it for real business workflows. It was trained through supervised and reinforcement learning on a large set of environments and tasks, including those generated by our Agentic Task Factory.

Get started now:

  • 🤖 Models: Holo4-27B | Holo4-35B-A3B | Holotron4 Nano
  • 🗂️ Full collection (FP16, FP8, GGUF): Holo4
  • 🎞️ Trajectories: viewer | dataset
  • ⚡ H Models API: quickstart
  • 📝 Full blog post: hcompany.ai/newsroom/holo4

Models built for every interface

Holo4 clicks and types on a screen, writes and runs its own code, and calls MCP or API tools. It uses whichever fits the task. Most agentic models are trained for one interface only: GUI-focused models are blind without a screen, while models that prefer tool calling are stuck in front of an application that has no API. Real work is not siloed that way, and a single business task can require combining these different approaches.

Holo4 runs on desktops, on the web, on Android, in a code sandbox and against business APIs. It is the same model in each case and it is called the same way. You do not need to select a different model for each platform.

Holo4 models improve significantly over their Qwen base. Holo4 trails only the strongest closed models on long workflows: on OSWorld 2.0, Holo4 27B scores 61.7% against 81.8% for Opus 5.5, and Holo4 35B-A3B reaches 30.9%. However, it does so with orders of magnitude fewer parameters and at a much lower cost. We open-source every trajectory behind our scores on public benchmarks: replay each step at trajectories.hcompany.ai or download them from Hugging Face.

Notes on the cost-performance charts

Original source

This story was published by Hugging Face Blog. SyncAI.news shows a preview; the complete article is on the publisher's site.

Read the full story on huggingface.co

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