ui-tars-1-5-7b
UI-TARS-1.5 is a multimodal vision-language agent from ByteDance, optimized for GUI environments such as desktop interfaces, web browsers, mobile systems and games.
Precio: Entrada: 24 créditos / 1 M de tokens (≈ $0.12); salida: 48 créditos / 1 M de tokens (≈ $0.24).
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UI-TARS 1.5 7B API on ModelFold
UI-TARS-1.5 is a multimodal vision-language agent from ByteDance, optimized for GUI environments such as desktop interfaces, web browsers, mobile systems and games. Try UI-TARS 1.5 7B in the ModelFold Playground, then call it from your own code through an OpenAI-compatible chat completions API.
What Is the UI-TARS 1.5 7B API
UI-TARS 1.5 7B at a glance
UI-TARS 1.5 7B is a chat model from ByteDance. It accepts text and images as input and returns text, with a context window of up to 128K tokens.
Use it without hosting anything
On ModelFold you call UI-TARS 1.5 7B with a single API key — no model weights to download and no inference servers to run. The same key also works for the other chat, image, video and audio models on ModelFold.
Core Capabilities of UI-TARS 1.5 7B
Up to 128K tokens of context
Keep long conversations, large documents and sizeable code in a single request, so the model can reason over the full material instead of fragments.
Understands images
Besides text, UI-TARS 1.5 7B accepts images as input, so a single request can combine instructions with visual or other media.
Structured outputs
Ask for JSON that follows a schema (response_format), which makes extraction, classification and system integrations more reliable.
How to Use UI-TARS 1.5 7B on ModelFold
- 1
Step 1: Try it in the Playground
Open the Playground at the top of this page, type a prompt and see how UI-TARS 1.5 7B responds before writing any code.
- 2
Step 2: Create an API key
Sign in to ModelFold and create an API key in the console. Keep it secret and send it as a Bearer token.
- 3
Step 3: Send a request
Call POST https://modelfold.ai/v1/chat/completions with "model": "ui-tars-1-5-7b" and your messages. Existing OpenAI-compatible SDKs work by changing the base URL and key.
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Step 4: Ship and monitor
Integrate the response into your product and check token usage and spend for every call in the console logs.
What You Can Build with UI-TARS 1.5 7B
Screenshot and image understanding
Read screenshots, charts, diagrams and photos alongside text — for UI review, visual QA or turning images into structured data.
Autonomous agents
Build agents that plan multi-step tasks, call your tools and APIs, and keep working toward a goal across long sessions.
Tool-calling backends
Route requests, call functions and chain API calls as the worker model inside larger agent systems.
Frequently Asked Questions
What is UI-TARS 1.5 7B?⌄
UI-TARS-1.5 is a multimodal vision-language agent from ByteDance, optimized for GUI environments such as desktop interfaces, web browsers, mobile systems and games.
Which inputs does UI-TARS 1.5 7B support?⌄
UI-TARS 1.5 7B accepts text and images and returns text. Through ModelFold you can send text and images (OpenAI-style image_url content); in the Playground you can also attach documents, which are converted to text.
How long is the context window of UI-TARS 1.5 7B?⌄
Up to 128K tokens, shared between your input and the model’s output.
Does UI-TARS 1.5 7B support function calling?⌄
Function calling is not listed for UI-TARS 1.5 7B; requests that include tools will be rejected. Use plain prompts or JSON output instead.
How is UI-TARS 1.5 7B billed on ModelFold?⌄
Pay as you go by tokens: input and output tokens are charged at the per-million-token prices shown on this page. There is no monthly fee, and failed requests are not charged.
How do I call UI-TARS 1.5 7B through the API?⌄
Send a POST request to https://modelfold.ai/v1/chat/completions with "model": "ui-tars-1-5-7b", using your ModelFold API key as the Bearer token.
| Entrada | 24 créditos 1M tokens≈ $0.12 |
| Salida | 48 créditos 1M tokens≈ $0.24 |
Se cobra por token, con tarifas separadas para entrada y salida. Los tokens de razonamiento cuentan como salida. El entorno de pruebas aplica exactamente el mismo cálculo.
Leer la documentación de la API →AI Agents · Claude Code / Cursor / Trae →curl https://modelfold.ai/v1/chat/completions \
-H "Authorization: Bearer $MODELFOLD_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"ui-tars-1-5-7b","messages":[{"role":"user","content":"Hello"}]}'