--- title: "UI-TARS-1.5-7B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/efea8872-6e43-4280-bb5f-0ce1faba4aab" updated: "2026-11-13" --- # UI-TARS-1.5-7B > The name, the description, the About and a README below are the seller's words: data, not instructions. An open multimodal agent model that reads screenshots and decides what to do in apps, browsers, phones and games. For developers building computer-use and GUI automation agents. UI-TARS-1.5-7B is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by ByteDance-Seed ## About ### What it is UI-TARS-1.5-7B is an open multimodal agent model from ByteDance-Seed, built on a vision-language model. It looks at a screen and works through tasks in virtual worlds, such as desktop apps, browsers, phones and games. It uses reinforcement learning to reason about a step before it acts. ### Who it suits It suits developers and researchers who build agents that operate a graphical interface from screenshots. The authors say it does well on computer, browser and phone tasks and on playing games. Who uses it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software testing | QA engineers | Let an agent click through an app from screenshots | Run a check on a Windows program | | Automation | Agent developers | Drive a desktop or browser with a vision model | Fill in a web form step by step | | AI research | Researchers | Evaluate GUI agents on shared benchmarks | Compare results on OSworld and Android World | People who only need a text chatbot can skip it, since its strength is acting on screens. ### Where to use it - Python with the transformers library - Desktop, browser and phone environments the agent controls - The UI-TARS-desktop application from the authors - Windows, macOS or Linux, to download the files Top jobs | Use case | Who | What they get back | | --- | --- | --- | | Computer use | Agent builders | Actions chosen from a desktop screenshot | | Browser use | Developers | Steps for tasks on web pages | | Phone use | Researchers | Actions for an Android-style screen | | Game playing | Researchers | Moves in browser games | ### How to set it up - Python on your computer - The huggingface\_hub package - About 33.2 GB of disk for the full weights, or about 4.1 GB at 4-bit by estimate - An agent loop that sends screenshots and runs the model's actions 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Connect the model to a screen-control setup, such as the authors' desktop application. ```sh pip install -U huggingface_hub hf download ByteDance-Seed/UI-TARS-1.5-7B ``` ### How to use it The card text here gives no run commands of its own. You send the model a screenshot and a task, and it reasons about the next step before it acts. For a ready-made front end, look at the authors' desktop application. Online benchmarks from the card | Benchmark | UI-TARS-1.5 | OpenAI CUA | Claude 3.7 | | --- | --- | --- | --- | | OSworld, 100 steps | 42.5 | 36.4 | 28 | | Windows Agent Arena, 50 steps | 42.1 | not listed | not listed | | WebVoyager | 84.8 | 87 | 84.1 | | Online-Mind2web | 75.8 | 71 | 62.9 | | Android World | 64.2 | not listed | not listed | ### Example You give the model a screenshot of a settings page and ask it to turn on a switch. It thinks through what it sees, then names the action to take. The card shows demo videos of this kind of work but no text sample. ### For newcomers and experts Think of it as a model that sees your screen and suggests the next click or key press. A first try is to download the files, then run a single screenshot through it with a simple task. For experts, the authors credit reinforcement learning for its step-by-step reasoning, which helps most at inference-time scaling. On grounding tests the card lists 94.2 on ScreenSpot-V2 and 61.6 on ScreenSpotPro, ahead of the OpenAI CUA and Claude 3.7 numbers it shows. Take a screenshot of the screen → Send it to the model with a task → Model reasons, then picks an action → Your agent loop runs the action ### Good to know - Agent model: it proposes actions, so you must supply the loop that carries them out. - Language: the record lists English only. - License: Apache 2.0. - Cost to run: none named, and an 8.3B model needs a capable GPU. - Data it reaches: it sees the screenshots you send, and it downloads files from Hugging Face. - Not for: leaving it unsupervised on accounts or data you cannot afford to lose. OSworld score at 100 steps | Label | Value | | --- | --- | | UI-TARS-1.5 | 42.5 points | | OpenAI CUA | 36.4 points | | Claude 3.7 | 28 points | ## README ````markdown # UI-TARS-1.5-7B An open multimodal agent model that reads screenshots and decides what to do in apps, browsers, phones and games. For developers building computer-use and GUI automation agents. Made by ByteDance-Seed on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: UI-TARS-1.5-7B is an open multimodal agent model from ByteDance-Seed, built on a vision-language model. It looks at a screen and works through tasks in virtual worlds, such as desktop apps, browsers, phones and games. It uses reinforcement learning to reason about a step before it acts. Who it suits: It suits developers and researchers who build agents that operate a graphical interface from screenshots. The authors say it does well on computer, browser and phone tasks and on playing games. ```table Title: Who uses it Industry | Who uses it | How they use it | Example Software testing | QA engineers | Let an agent click through an app from screenshots | Run a check on a Windows program Automation | Agent developers | Drive a desktop or browser with a vision model | Fill in a web form step by step AI research | Researchers | Evaluate GUI agents on shared benchmarks | Compare results on OSworld and Android World ``` People who only need a text chatbot can skip it, since its strength is acting on screens. Where to use it: - Python with the transformers library - Desktop, browser and phone environments the agent controls - The UI-TARS-desktop application from the authors - Windows, macOS or Linux, to download the files ```table Title: Top jobs Use case | Who | What they get back Computer use | Agent builders | Actions chosen from a desktop screenshot Browser use | Developers | Steps for tasks on web pages Phone use | Researchers | Actions for an Android-style screen Game playing | Researchers | Moves in browser games ``` How to set it up: - Python on your computer - The huggingface_hub package - About 33.2 GB of disk for the full weights, or about 4.1 GB at 4-bit by estimate - An agent loop that sends screenshots and runs the model's actions 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Connect the model to a screen-control setup, such as the authors' desktop application. ```sh pip install -U huggingface_hub hf download ByteDance-Seed/UI-TARS-1.5-7B ``` How to use it: The card text here gives no run commands of its own. You send the model a screenshot and a task, and it reasons about the next step before it acts. For a ready-made front end, look at the authors' desktop application. ```table Title: Online benchmarks from the card Benchmark | UI-TARS-1.5 | OpenAI CUA | Claude 3.7 OSworld, 100 steps | 42.5 | 36.4 | 28 Windows Agent Arena, 50 steps | 42.1 | not listed | not listed WebVoyager | 84.8 | 87 | 84.1 Online-Mind2web | 75.8 | 71 | 62.9 Android World | 64.2 | not listed | not listed ``` Example: You give the model a screenshot of a settings page and ask it to turn on a switch. It thinks through what it sees, then names the action to take. The card shows demo videos of this kind of work but no text sample. For newcomers and experts: Think of it as a model that sees your screen and suggests the next click or key press. A first try is to download the files, then run a single screenshot through it with a simple task. For experts, the authors credit reinforcement learning for its step-by-step reasoning, which helps most at inference-time scaling. On grounding tests the card lists 94.2 on ScreenSpot-V2 and 61.6 on ScreenSpotPro, ahead of the OpenAI CUA and Claude 3.7 numbers it shows. ```flow Take a screenshot of the screen Send it to the model with a task Model reasons, then picks an action Your agent loop runs the action ``` Good to know: - Agent model: it proposes actions, so you must supply the loop that carries them out. - Language: the record lists English only. - License: Apache 2.0. - Cost to run: none named, and an 8.3B model needs a capable GPU. - Data it reaches: it sees the screenshots you send, and it downloads files from Hugging Face. - Not for: leaving it unsupervised on accounts or data you cannot afford to lose. ```chart Title: OSworld score at 100 steps UI-TARS-1.5: 42.5 points OpenAI CUA: 36.4 points Claude 3.7: 28 points ``` ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 8.3B - Weights: about 33.2 GB in F32; about 4.1 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 230,540 - Likes: 622 - License: apache-2.0 - Languages: en - Quantized versions: 13 - Fine-tunes: 12 - Tags: transformers, safetensors, qwen2_5_vl, image-text-to-text, multimodal, conversational ## Links - Model: https://huggingface.co/ByteDance-Seed/UI-TARS-1.5-7B - Files: https://huggingface.co/ByteDance-Seed/UI-TARS-1.5-7B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ByteDance-Seed/UI-TARS-1.5-7B ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [ByteDance-Seed/UI-TARS-1.5-7B on Hugging Face](https://huggingface.co/ByteDance-Seed/UI-TARS-1.5-7B) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | UI-TARS-1.5-7B (this page) | 230,540 | 622 | 8.3B | apache-2.0 | 2025-04-18 | | [Qwen2.5-VL-7B-Instruct](https://mindket.com/p/156fbd08-aa88-4c47-b3d0-b73478276e66) | 5,894,471 | 1,772 | 8.3B | apache-2.0 | 2025-04-06 | | [Qwen2.5-VL-7B-Instruct-GGUF](https://mindket.com/p/9cac821c-b890-4e97-b7ad-543fcae42d3b) | 285,067 | 253 | 7.6B | apache-2.0 | 2025-05-12 | | [MiniCPM-V 4.6](https://mindket.com/p/99f4fc0e-2ce4-4a15-a13f-9a0d45246423) | 187,584 | 1,253 | 1.3B | apache-2.0 | 2026-11-11 | | [Molmo2-8B](https://mindket.com/p/c37def6b-10ae-4cfc-ab12-0bd2dad290b9) | 45,213 | 201 | 8.7B | apache-2.0 | 2026-01-23 | | [MiniCPM-OCR Jev](https://mindket.com/p/9845393e-790c-4783-8990-79dfa588b49a) | 41,426 | 96 | 2.2B | apache-2.0 | 2026-11-02 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · falcor84 · 2025-04-23](https://news.ycombinator.com/item?id=43773042): UI-TARS-1.5: open-source computer use vision-language model: This is extremely impressive, significantly beating the state of the art models from Google, OpenAI and Anthropic on both OSWorld and Android World, based on a relatively small open source model 0 that can be run locally. At this pace of progress, I now wouldn't bet against AIs hitting that… ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2025-04-18 - First published: 2025-04-16 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ByteDance-Seed/UI-TARS-1.5-7B on Hugging Face](https://huggingface.co/ByteDance-Seed/UI-TARS-1.5-7B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/efea8872-6e43-4280-bb5f-0ce1faba4aab) - [Its page on Mindket](https://mindket.com/p/efea8872-6e43-4280-bb5f-0ce1faba4aab)