Best of all, Holo3 achieves this with only 10B active parameters (122B total), so at a fraction of the cost of large-scale proprietary models, such as GPT 5.4 or Opus 4.6. All models are available through our Inference API. Holo3-35B-A3B weights are openly accessible on Hugging Face under the Apache2 license and freely accessible through our inference API under a free tier.

What sets Holo3 apart is its specialized training pipeline—a continuous feedback loop designed to sharpen two core agentic pillars: perception and decision-making.

Our training flywheel is about teaching our model from annotated examples how to execute specific tasks, all while developing generalist skills across a virtually infinite variety of user interfaces. Here is how we build world-class computer use models:

Synthetic Navigation Data: using human and generated instructions, we generate scenario-specific navigation examples.