Paris-based H Company has officially launched its Computer-Use Agents API out of early access and into open beta. The pitch is simple: you describe a task in plain English, H provisions a cloud browser or desktop environment, runs the agent, and hands you back a result. No sandbox to spin up, no agent loop to write, no model to host.
The model behind it
The API is powered by Holo3.1, H's latest vision-language model family built specifically for GUI control. With a score of 78.85% on the OSWorld-Verified benchmark, Holo3 establishes a new state of the art on the leading desktop computer-use benchmark. OSWorld-Verified is worth understanding: it's the leading benchmark for evaluating AI computer use, and unlike benchmarks that score on output text, OSWorld tests execution -- the agent must complete real tasks on a real computer, and success is verified by checking the actual state of the system afterward. To put the score in context: scores above 40% were considered state-of-the-art until recently, and previous leading models from Anthropic and OpenAI sat in the 60-65% range.
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. The architecture is a sparse Mixture-of-Experts (MoE) model -- meaning only a subset of the model's parameters activate per token, keeping inference cheap despite the large total parameter count. Holo3.1 also delivers more than a 25% improvement over Holo3 when evaluated inside H's Holotab product harness.
How it was built
Holo3 was built using an agentic flywheel, trained to execute real-world workflows within synthetic enterprise environments. The key training innovation is what H calls the Synthetic Environment Factory: this proprietary factory reproduces the reality of enterprise systems, and environments are automatically built using coding agents that program websites from scratch based on scenario specifications, producing verifiable tasks of varying difficulty that are validated end-to-end with verification scripts. The training pipeline also applies out-of-domain augmentation to programmatically extend scenarios and ensure Holo3 can handle the unexpected, plus curated reinforcement learning where every data sample is carefully filtered to maximize performance.
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