---
license: apache-2.0
task_categories:
  - visual-question-answering
tags:
  - mobile-ui
  - gui-automation
  - benchmark
  - QA testing
  - tap-accuracy
---

# UI-TapBench

## 📝 Summary & Intention

<span style="color:#4F46E5"><b>UI-TapBench</b></span> is an open-source benchmark created to evaluate the <span style="color:#4F46E5"><b>spatial precision</b></span> of Large Multimodal Models (LMMs) in mobile environments.

As AI agents move toward _"Actionable AI,"_ the ability to translate a natural language instruction into exact screen coordinates is the most common point of failure. This dataset provides a standardized way to measure and improve how models handle <span style="color:#4F46E5"><b>dense UI layouts</b></span> and <span style="color:#4F46E5"><b>list-based navigation</b></span>, ensuring <span style="color:#4F46E5"><b>tap reliability</b></span> in autonomous agents.

---

## 🚀 About Drizz

> <span style="color:#4F46E5"><b>Reimagining Mobile App Testing with Vision AI.</b></span>

At <span style="color:#4F46E5"><b>[Drizz](https://drizz.dev)</b></span>, we're building the world's fastest AI-powered testing agent for mobile apps — no locators, no scripting, just plain English. Mobile teams today move fast, but testing tools haven't kept up. Drizz replaces brittle, locator-based frameworks with a <span style="color:#4F46E5"><b>vision-based AI engine</b></span> that understands your app like a human.

With Drizz, teams achieve:

- ⚡ <span style="color:#4F46E5"><b>10x Faster Test Cycles</b></span>
- 🎯 <span style="color:#4F46E5"><b>97%+ Test Accuracy</b></span>
- 🛡️ <span style="color:#4F46E5"><b>Zero Flaky Tests</b></span> via our vision-based engine

We are releasing <span style="color:#4F46E5"><b>UI-TapBench</b></span> to help the community move toward a world where UI automation is as simple, reliable, and _"human-like"_ as possible.

---

## 📊 Dataset Structure

Each entry in `metadata.jsonl` follows this schema:

| Key          | Description                                                    |
| ------------ | -------------------------------------------------------------- |
| `id`         | Unique identifier for the sample.                              |
| `image`      | Relative path to the screenshot (e.g., `images/841.png`).      |
| `task`       | The natural language command (e.g., _"Tap on second option"_). |
| `bbox`       | Ground truth coordinates: `[xmin, ymin, xmax, ymax]`.          |
| `app_name`   | The package name of the app being tested.                      |
| `function`   | The targeted action type (default: `tap_call_llm`).            |

### Example Entry

```json
{
  "id": 841,
  "image": "images/841.png",
  "task": "Tap on second option in the list.",
  "bbox": [42, 733, 1038, 901],
  "app_name": "com.duolingo",
  "function": "tap_call_llm"
}
```

---

## 📈 Benchmark Results

We evaluated <span style="color:#4F46E5"><b>UI-TapBench</b></span> across leading Large Multimodal Models (LMMs) to measure <span style="color:#4F46E5"><b>tap accuracy</b></span>, <span style="color:#4F46E5"><b>spatial precision</b></span>, and <span style="color:#4F46E5"><b>reliability</b></span> for mobile UI interactions.

### 🔍 Competitor Comparison

| Model                                                       | Accuracy                                          | Precision                                         | Recall                                            | F1 Score                                          |
| ----------------------------------------------------------- | ------------------------------------------------: | ------------------------------------------------: | ------------------------------------------------: | ------------------------------------------------: |
| 🏆 <span style="color:#4F46E5"><b>Drizz (ours)</b></span>   | <span style="color:#4F46E5"><b>94.51</b></span>   | <span style="color:#4F46E5"><b>96.22</b></span>   | <span style="color:#4F46E5"><b>98.16</b></span>   | <span style="color:#4F46E5"><b>97.18</b></span>   |
| gpt-5.1                                                     | 21.72                                             | 23.35                                             | 75.61                                             | 35.68                                             |
| gpt-5.2                                                     | 44.83                                             | 45.71                                             | 95.88                                             | 61.91                                             |
| gemini-pro                                                  | 89.84                                             | 91.28                                             | 98.28                                             | 94.65                                             |
| gemini-flash                                                | 81.44                                             | 83.78                                             | 96.67                                             | 89.77                                             |
| qwen3.5-27b                                                 | 92.98                                             | 94.98                                             | 97.61                                             | 96.28                                             |

### 💡 Key Takeaway

The results show that while several models perform well on general UI grounding tasks, <span style="color:#4F46E5"><b>Drizz</b></span> demonstrates the <span style="color:#4F46E5"><b>highest benchmark performance</b></span> on <span style="color:#4F46E5"><b>UI-TapBench</b></span>, achieving strong spatial precision and reliable tap execution even in <span style="color:#4F46E5"><b>dense mobile UI layouts</b></span>.

---

## 📜 License

Released under the <span style="color:#4F46E5"><b>Apache 2.0</b></span> License.