Holo4: powering generalist computer-use agents
Holo4 is a new series of agentic models, available in 27B dense and 35B-A3B Mixture of Experts sizes on the H Models API. An updated Holotron4 Nano is also being released. Holo4's costs are estimated from input and output tokens of each agentic run, priced at H Models API rates. Comparisons are made using OSWorld 2.0, with Qwen3.8 27B and Qwen3.6 35B-A3B costs based on Alibaba Cloud list prices. Optimized DSpark drafter checkpoints will be released to accelerate inference.
Unlike previous models, Holo4's cost estimates are based on OSWorld 2.0, providing a standardized comparison against models like Qwen3.8 27B and Qwen3.6 35B-A3B.
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PublishedOffset at this time: UTC+0Sep 28, 2026, 09:44 UTC
IngestedOffset at this time: UTC+0Sep 28, 2026, 10:00 UTC
- Published
- Sep 28, 2026, 09:44
- Ingested
- Sep 28, 2026, 10:00
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- Official
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- First-party
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- Healthy
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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 .
Competitive with the frontier, at a fraction of the cost
On the hardest academic benchmarks for desktop control (OSWorld 2.0) and API use (AutomationBench), Holo4 competes with frontier models at a much lower cost per task.
Notes on the cost-performance charts
OSWorld 2.0. Costs are estimated from the input and output tokens of each agentic run. Holo4 is priced at H Models API rates (single run). Qwen3.8 27B: model card score, cost from the tokens of our run at Alibaba Cloud list prices. Qwen3.6 35B-A3B: single run in our harness, at Alibaba Cloud list prices with cache hits at 20% of the input price. OpenAI launch data supplies the GPT and Opus effort sweeps; other closed and open-weight points use the official OSWorld 2.0 leaderboard . Releases, harnesses and task subsets differ. The line connects non-dominated score and cost pairs among the closed models; Holo4 is excluded.
AutomationBench. Holo4, Qwen3.8 27B and Qwen3.6 35B-A3B: AutomationBench v1.0.6, scores and costs measured in our internal harness. Other models: public-set scores from the AutomationBench README , cost per task from the official leaderboard , which runs on the private set. We will report Holo4 on the private set once it is evaluated.
AI that does work
Trained on environments and tasks from our Agentic Task Factory, Holo4 models excel on professional software. The examples below show Holo4 27B alongside Qwen3.8 27B, its base model. Same prompt and harness for both models.
3D modeling · Eiffel tower
Build a 3D model of the Eiffel Tower in FreeCAD, at a scale of 1 mm to 1 metre, centred on the origin and aligned to the X and Y axes. Work to this design.
The tower is square in plan at every height, never round. Its half-width, measured from the central axis out to the corner, is 62.5 mm at ground level, 32.5 mm at height 57, 17.5 mm at height 115, and 9.35 mm at height 276. Between those heights the half-width follows a smooth curve that falls steeply near the ground and gently higher up, never a straight line.
Four identical legs, one per quadrant, each a square column whose outer corner follows that profile. Each leg is 14 mm across at the ground and tapers to 4 mm at height 276. The legs stand apart from the ground up to the first platform, and converge as they rise. Nothing fills the space between them: the tower is open, and you can see straight through it from every side.
Three platforms, each a solid square slab centred on the axis: 72 mm across and 4 mm thick at height 57; 40 mm across and 3 mm thick at height 115; 22 mm across and 3 mm thick at height 276.