H Company has released Holo4, a family of generalist computer-use models for AI agents. One set of weights clicks and types on screens. It also writes code and calls MCP or API tools. Holo4 ships in 2 sizes: Holo4 27B (dense) and Holo4 35B-A3B (Mixture of Experts, 3B active). Both serve a 256K context on the H Models API.
Is it deployable? Yes. Holo4 35B-A3B ships Apache 2.0 weights for commercial self-hosting. Holo4 27B weights are CC BY-NC 4.0, so commercial use of 27B runs through the H Models API.
What is Holo4
Holo4 is a vision-language model for computer use. Holo4 27B is fine-tuned from Qwen3.8-27B. Holo4 35B-A3B is built on Qwen3.6-35B-A3B. Both pair with H’s open hai-agents harness. The harness sends screenshots and tool results to the model. It then executes the requested clicks, typing, code and tool calls.
H company targets a known gap. GUI-only agents fail without a screen. Tool-calling agents stall when an application has no API. Holo4 runs on desktop, web, Android, code sandboxes and business APIs. It is the same model, called the same way, on every platform.
Benchmarks: Close to the Frontier, at a Fraction of the Cost
Per H Company’s benchmark table, Holo4 27B scores 85.2% on OSWorld at $0.08 per task. Its Qwen3.8 27B base scores 84.3% at $0.22. On AndroidWorld, Holo4 27B reaches 85.1%.
Long workflows show the remaining gap. On OSWorld 2.0, Holo4 27B scores 61.7% at $1.22 per task. Claude Opus 5.5 scores 81.8% at $8.48, per H’s figures. On AutomationBench, Holo4 27B scores 45.4% at $0.05 per task.
It is important to note that frontier scores come from different harnesses and effort levels. Also, 480 of AutomationBench’s 600 public tasks sit in the split H Company collected training data from. On the 120 held-out tasks, Holo4 27B scores 49.3%. H Company publishes every trajectory at trajectories.hcompany.ai and on Hugging Face.
How H Company Built Holo4
Agentic Task Factory: H’s internal pipelines build environments and verifiable tasks from documentation, screenshots and real software. The factory has produced about 10,000 tasks: 4k web apps, 3k MCP servers, 3k desktop and OS. A task survives only if its verifier rejects near misses. An agent must also solve it through the real interface.
Supervised fine-tuning: The SFT set holds 127B tokens. About three quarters are successful agentic trajectories: desktop 45%, web 14%, MCP and API 12%, mobile 3%.
2 RL experts, 1 merge: Asynchronous online RL trains 2 LoRA experts. One handles desktop and web. The other handles terminal, MCP and API. Both merge back with equal weight and no further training.
Harness: H Company rebuilt its agent loop using OSWorld 2.0 failure analysis. The largest changes were reliable memory across hundreds of steps and a shell on the desktop machine.
Holotron4 Nano
H Company also released Holotron4 Nano, built on NVIDIA’s Nemotron 3 Nano Omni through the Nemotron Coalition. The same combination lifts OSWorld from 21.0% to 76.3% over the base model.
Pricing and Deployment
Holo4 27B costs $0.40 input and $3.00 output per 1M tokens. Holo4 35B-A3B costs $0.30 and $2.00. The API is OpenAI-compatible at https://api.hcompany.ai/v1; see the quickstart. Weights on the Hugging Face collection come in BF16, FP8, NVFP4 and 4-bit GGUF. H Company documents local inference with vLLM and llama.cpp. H says DSpark drafter checkpoints for faster inference arrive in the coming days.
Interactive Explainer: How Holo4 Works
Holo4 vs. Closest Competitors
*OSWorld 2.0 figures from H Company’s newsroom table. Opus 5.5 ran at max effort in Anthropic’s harness. GPT-6 Astra ran at max effort on the 82-task offline subset. Harnesses differ, so treat cross-vendor rows as directional.
Key Takeaways
- Holo4 uses 1 model for GUI clicks, code, MCP and API calls.
- Holo4 27B scores 85.2% on OSWorld at $0.08 per task.
- 35B-A3B is Apache 2.0; 27B weights are non-commercial only.
- Opus 5.5 still leads OSWorld 2.0: 81.8% vs 61.7%.
- API pricing starts at $0.30 input and $2.00 output per 1M tokens.
Check out the Technical details, H Company’s HuggingFace Collection and Model API. All credit goes to the researcher of this project. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.
