Have you ever wondered how AI could take over those tedious tasks on your computer screen, like clicking buttons or filling forms, just by looking at what’s there? That’s where models like Holo1.5 come in. These are specialized vision-language models designed to help create agents that interact with user interfaces in a natural way. In this post, I’ll walk you through what Holo1.5 is all about, why it matters, and how it stacks up against others. We’ll break it down step by step, so even if you’re not a deep AI expert, you’ll get a clear picture. Let’s dive in.
What Are Computer Use Agents and Why Do We Need Them?
Imagine you’re busy with work, and you want an AI to handle opening apps, navigating websites, or even checking if you’re logged in somewhere. Computer use agents, or CU agents, are AI systems that do exactly that. They “see” the screen through screenshots and then decide on actions like clicking at specific spots or typing text.
You might be thinking, “How does the AI know where to click?” That’s a great question. It relies on two key skills: UI element localization and UI visual question answering. Localization means pinpointing exact coordinates on the screen for a task, like “Click here to open Spotify.” Question answering helps the agent understand the screen’s state, answering things like “Is the user signed in?” or “Which tab is active?”
Holo1.5 is a series of models built specifically for these tasks. Released in sizes of 3B, 7B, and 72B parameters, they offer improvements over earlier versions like Holo1, with boosts in accuracy by more than 10%. The 7B version is fully open under Apache 2.0, making it easy to use in projects, while the others have some restrictions for research.
Breaking Down UI Element Localization
Let’s talk about localization first because it’s the foundation for any CU agent. When an agent gets a screenshot and a command, it needs to output precise coordinates—like “Click at X, Y”—to act on it. This is crucial in environments like desktops (macOS, Ubuntu, Windows), web pages, or mobile apps, especially in high-resolution setups where screens are packed with elements.
Why is this tricky? Professional software like Photoshop or VSCode has tiny icons and complex layouts. A small mistake in coordinates could click the wrong thing, messing up the whole task. Holo1.5 tackles this by being trained on diverse data, handling resolutions up to 3840×2160.
As you can see in this chart, Holo1.5 models form a Pareto frontier, balancing size and accuracy better than before.
If you’re curious about how to see this in action, there’s a demo available where you can prompt the model with a screenshot and a task. It shows the agent navigating a UI step by step.
How Holo1.5 Performs on Localization Benchmarks
Performance is key, right? So, how does Holo1.5 measure up? It was tested on benchmarks like ScreenSpot-v2, ScreenSpot-Pro, GroundUI-Web, Showdown, WebClick, and OSWorld-G. These cover web, mobile, and desktop scenarios.
Here’s a table summarizing the results across different models:
| Model | WebClick | Showdown | ScreenSpot-v2 | ScreenSpot-Pro | Ground-UI-1K | OSWorld-G | Average |
|---|---|---|---|---|---|---|---|
| Holo1.5-3B | 81.45 | 67.50 | 91.66 | 51.49 | 83.20 | 61.57 | 72.81 |
| Holo1.5-7B | 90.24 | 72.17 | 93.31 | 57.94 | 84.00 | 66.27 | 77.32 |
| Holo1.5-72B | 92.43 | 76.84 | 94.41 | 63.25 | 84.50 | 71.80 | 80.54 |
| Qwen2.5-VL-3B | 71.20 | 50.30 | 80.00 | 29.30 | 76.40 | 34.31 | 56.92 |
| Qwen2.5-VL-7B | 76.51 | 52.00 | 85.60 | 29.00 | 80.70 | 40.59 | 60.73 |
| Qwen2.5-VL-72B | 88.29 | 41.00 | 93.30 | 55.60 | 85.40 | 61.96 | 70.93 |
| UI-TARS-1.5-7B | 86.10 | 58.00 | 94.00 | 39.00 | 84.20 | 61.40 | 70.45 |
| Holo1-7B | 84.04 | 64.27 | 89.85 | 26.06 | 78.50 | 47.25 | 65.00 |
| Holo1-3B | 79.35 | 59.96 | 88.91 | 23.66 | 74.75 | 42.16 | 61.47 |
| UI-Venus-7B | 84.44 | 67.32 | 94.10 | 50.80 | 82.30 | 58.80 | 72.96 |
| UI-Venus-72B | 77.00 | 75.58 | 95.30 | 61.90 | 75.50 | 70.40 | 75.95 |
| Sonnet 4 | 93.00 | 72.00 | 93.00 | 19.10 | 84.00 | 59.60 | 70.12 |
Look at those numbers—for the 7B size, Holo1.5 hits 77.32% average accuracy, way ahead of Qwen2.5-VL-7B’s 60.73%. On ScreenSpot-Pro, which tests dense professional UIs, it’s 57.94% versus 29.00%. That means fewer errors in real-world tools like AutoCAD.
The 72B model pushes it further to 80.54%, setting new highs in several categories. Even the smaller 3B version outperforms some larger competitors from before.
This benchmark comparison chart highlights how Holo1.5 consistently leads.
Understanding UI Through Visual Question Answering
Now, localization is great for actions, but what if the agent needs to “think” about the screen? That’s where UI-VQA comes in. It lets the model answer questions based on visuals, helping track progress or resolve issues.
For example, after clicking, the agent might ask itself, “Did that open the right menu?” Benchmarks like VisualWebBench, WebSRC, ScreenQA Short, and ScreenQA Complex test this.
Here’s the performance table for UI-VQA:
| Model | VisualWebBench | WebSRC | ScreenQAShort | ScreenQAComplex | Average |
|---|---|---|---|---|---|
| Holo1.5-3B | 78.50 | 94.80 | 87.90 | 81.40 | 85.65 |
| Holo1.5-7B | 82.60 | 95.90 | 91.00 | 83.20 | 88.17 |
| Holo1.5-72B | 83.80 | 97.20 | 91.90 | 87.10 | 90.00 |
| Qwen2.5-VL-3B | 58.00 | 93.00 | 86.00 | 76.00 | 78.25 |
| Qwen2.5-VL-7B | 69.00 | 95.00 | 87.00 | 81.10 | 83.02 |
| Qwen2.5-VL-72B | 76.30 | 97.00 | 87.90 | 83.20 | 86.10 |
| UI-TARS-1.5-7B | 79.70 | 92.90 | 88.70 | 79.20 | 85.12 |
| Holo1-3B | 54.10 | 93.90 | 78.30 | 53.50 | 69.95 |
| Holo1-7B | 38.10 | 95.30 | 83.30 | 65.10 | 70.45 |
| UI-Venus-7B | 60.90 | 96.60 | 86.30 | 82.30 | 81.52 |
| UI-Venus-72B | 74.10 | 96.70 | 88.60 | 83.30 | 85.67 |
| Claude-Sonnet-4 | 58.90 | 96.00 | 87.00 | 75.70 | 79.40 |
Holo1.5-72B tops out at 90.00% average, a 3.9% jump over the best competitors. This means better comprehension, leading to more reliable agents that can verify actions and handle ambiguities.
The Pareto chart here shows the efficiency across sizes.
And this one compares it head-to-head.
How Holo1.5 Differs from General Vision-Language Models
You might ask, “Can’t I just use a general VLM like Qwen for this?” General VLMs are good at broad tasks like captioning images, but CU agents need precision in pointing and understanding interfaces. Holo1.5 is fine-tuned specifically for GUI tasks using supervised fine-tuning and reinforcement learning (GRPO) to sharpen accuracy.
It’s built from Qwen2.5-VL bases but enhanced with proprietary data mixes: open-source, synthetic, and human-annotated. This makes it better at high-res screens and cross-platform use.
In a CU stack, Holo1.5 acts as the perception layer—taking screenshots (maybe with metadata) and outputting coordinates or answers. Then, other components handle the actual clicks or keys.
Training Behind Holo1.5
The training is multi-stage: first, large-scale supervised fine-tuning on UI data for understanding and actions. Then, online reinforcement to refine outputs. The dataset includes a blend to ensure robustness across environments.
This approach results in models that are not just accurate but efficient, fitting different needs—from lightweight 3B for quick tests to 72B for top performance.
Licensing and Availability
Licensing matters for real use. The 7B is Apache 2.0, perfect for commercial projects. The 3B inherits from Qwen, and 72B is research-only—contact for commercial options.
All are on Hugging Face, with collections for easy access.
How to Get Started with Holo1.5
Ready to try it? Here’s a simple how-to guide for using the model, based on the quickstart.
Step-by-Step: Prompting Holo1.5 for Navigation
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「Install Dependencies」: Make sure you have the right libraries. Use Python with transformers from Hugging Face.
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「Load the Model」: Download from Hugging Face, like
Hcompany/Holo1.5-7B. -
「Prepare Input」: Provide a screenshot image and a text prompt, e.g., “Open the Spotify App.”
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「Run Inference」: Use the model’s API to process the image and text, getting coordinates back.
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「Integrate」: Hook it into your agent framework for actions.
For a full example, check the cookbook notebook—it walks through code for a basic demo.
There’s also a live space for testing without setup.
Building Cross-Platform Agents with Holo1.5
The goal is reliable, cost-efficient agents. Holo1.5 steps toward that by improving trust in tech. Upcoming tools and agents will build on this.
It supports web, desktop, mobile—making generalist agents possible.
Higher accuracy means fewer misclicks in apps, better state tracking for logins or tabs.
Potential Applications
Think about automating workflows: an agent using Holo1.5 could navigate IDEs, design tools, or admin panels with confidence.
For developers, embed it in planners for verification loops—act, check, retry if needed.
Challenges and Considerations
No model is perfect. Benchmarks show strengths, but test on your setups. Prompts and resolutions affect results.
Smaller models like 3B are great for edge cases, while 72B bounds what’s possible.
FAQ: Common Questions About Holo1.5
Here are some questions you might have, answered directly.
What is Holo1.5 exactly?
It’s a family of vision-language models for CU agents, focusing on localizing UI elements and answering questions about screens. Sizes: 3B, 7B, 72B.
How does Holo1.5 improve over Holo1?
It adds 10%+ accuracy in localization and strong gains in UI-VQA, across all sizes.
Is Holo1.5 open-source?
Yes, weights are open on Hugging Face. 7B is Apache 2.0; others have base restrictions.
What benchmarks does it excel in?
Localization: ScreenSpot-v2/Pro, GroundUI-Web, Showdown, WebClick, OSWorld-G. UI-VQA: VisualWebBench, WebSRC, ScreenQA Short/Complex.
Can I use Holo1.5 for commercial projects?
The 7B yes, fully. For 72B, it’s research-only—reach out for commercial.
How do I prompt Holo1.5?
Combine image (screenshot) with text task. It outputs coordinates or answers.
What’s the difference between localization and UI-VQA?
Localization finds positions for actions. UI-VQA understands state for reasoning.
Does Holo1.5 work on high-res screens?
Yes, up to 3840×2160, tested on dense UIs.
How was Holo1.5 trained?
Multi-stage: supervised fine-tuning on mixed data, then reinforcement for precision.
Where can I find demos?
On Hugging Face Space for navigation, or the blog for videos.
Is Holo1.5 better than closed models like Sonnet 4?
On these benchmarks, yes—higher averages in localization and VQA.
What are the model sizes good for?
3B: Quick, resource-light. 7B: Balanced for production. 72B: Max performance for research.
How does it fit in an agent architecture?
As perception: inputs screenshots, outputs coords/answers for action policies.
Wrapping Up: The Future of Computer Use Agents
Holo1.5 represents a solid advance in making AI agents that truly understand and act on our digital worlds. By focusing on precise grounding and comprehension, it paves the way for more dependable automation. Whether you’re building agents or just curious, exploring these models can open up new possibilities.
If you’re experimenting, start with the 7B—it’s accessible and powerful. Stay tuned for more developments; this is just the start.
