Skip to main content
Available now on ModelsLab · Language Model

Qwen: Qwen2.5 VL 32B InstructVision Meets Reasoning

Try Qwen: Qwen2.5 VL 32B InstructAPI Documentation

Process Multimodal Data

Image Analysis

Parse Charts Documents

Handles image-text reasoning, charts, UI, and document understanding with Qwen: Qwen2.5 VL 32B Instruct model.

Video Comprehension

Understand Long Videos

Analyzes videos over 1 hour for event detection using Qwen Qwen2 5 VL 32B Instruct API.

Agentic Tools

Visual Grounding Outputs

Generates bounding boxes, points, JSON for objects in Qwen: Qwen2.5 VL 32B Instruct alternative.

Examples

See what Qwen: Qwen2.5 VL 32B Instruct can create

Copy any prompt below and try it yourself in the playground.

Chart Analysis

“Analyze this sales chart image. Extract key trends, totals, and comparisons in structured JSON format.”

Invoice Extraction

“Extract all fields from this invoice scan: date, items, totals, vendor details in JSON.”

Video Events

“From this video clip of a city timelapse, detect and describe traffic peaks and weather changes.”

UI Navigation

“Describe this app screenshot UI. Suggest steps to book a flight using visual elements.”

For Developers

A few lines of code.
Multimodal inference. One call.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Qwen: Qwen2.5 VL 32B Instruct

Read the docs

Qwen: Qwen2.5 VL 32B Instruct is a 33.5B parameter multimodal vision-language model from Alibaba Cloud. It excels in image reasoning, video understanding, and agentic tasks. Supports 128k token context.

The qwen qwen2 5 vl 32b instruct api integrates via OpenAI-compatible endpoints. Send text and image/video inputs for reasoning outputs. No function calling or embeddings supported.

Superior in MMMU, MathVista, MM-MT-Bench benchmarks. Outperforms larger models like Qwen2-VL-72B in multimodal reasoning. Strong pure text performance too.

Yes, Qwen: Qwen2.5 VL 32B Instruct alternative beats Mistral-Small-3.1-24B and Gemma-3-27B-IT. Optimized via RL for math and structured outputs.

Up to 128k tokens on platforms like Fireworks. Default 32k with YaRN for extension to 64k or 131k.

Processes long videos over 1 hour for event detection. Supports visual grounding in JSON format.

Ready to create?

Start generating with Qwen: Qwen2.5 VL 32B Instruct on ModelsLab.

Try Qwen: Qwen2.5 VL 32B InstructAPI Documentation