Qwen3 VL Plus API

Alibaba's Qwen 3 VL Plus is a powerful vision-language model with hybrid thinking capabilities. Supports image understanding with up to 16,384 tokens per image and high-resolution mode for detailed analysis.

Modalities: TextText
In / Out price: $0.2/$1.6per 1M
Context window: 256K tokens
Released: Oct 19, 2025

Qwen3 VL Plus overview

Qwen3 VL Plus by Alibaba Cloud

It is optimized for real-time dialog systems, analytics platforms, and visual assistant applications.

Qwen3 VL Plus specs

Model ID: alibaba/qwen3-vl-plus
Developer: Alibaba Cloud
Context window: 262,144 tokens
Max output: 32,768 tokens
Input: Text
Output: Text
API: OpenAI-compatible, base URL https://api.aimlapi.com/v1

Qwen3 VL Plus API Overview

Qwen3 VL Plus is a state-of-the-art multimodal model from the third generation Qwen series, designed to integrate deep understanding of both text and images. It excels at visual question answering, scene description, object recognition, OCR text reading, and reasoning based on visual input, making it ideal for analytics, dialog assistants, and diverse visual scenarios.

Technical Specifications

Architecture: Dense and Mixture-of-Experts (MoE) variants with Instruct and Thinking editions
Context Length: Native support for 262.144K tokens
Multimodal Inputs: Text, images, video (enhanced spatial & temporal reasoning)
OCR Support: Robust recognition in 32 languages, including low light, blur, and tilt conditions
Enhanced Image-Text Alignment: DeepStack feature fusion for fine-grained details and sharper multimodal correspondence

Performance Benchmarks

Holds a leading position in global multimodal benchmarks, outperforming competitors such as Gemini 2.5 Flash and Claude Sonnet 4.5
Demonstrates state-of-the-art results in visual question answering, object detection, and video understanding tasks
Achieves competitive or superior scores on multimodal reasoning and perception tests compared to proprietary baselines

Key Features

Superior visual perception supporting complex scene interpretation and spatial reasoning, including 3D grounding
Seamless text-vision fusion enabling lossless understanding and generation of multimodal content
Advanced OCR capable of detecting rare and specialized characters in various languages
Long context and video comprehension supporting multi-hour content analysis with high recall accuracy
Multimodal reasoning enhanced for STEM, math, and logical causal analysis tasks
Visual agent functionality allows operating graphical interfaces and invoking tools programmatically

Use Cases

Visual question answering and interactive dialog systems combining text and image inputs
Scene recognition and description for analytics and surveillance applications
OCR and document parsing across multiple languages and challenging imaging conditions
Multimodal reasoning tasks in education, research, and technical domains like STEM
Automated UI operations and complex task execution in PC/mobile environments

Comparison with Other Models

vs Gemini 2.5 Flash: Qwen3 VL Plus outperforms Gemini 2.5 Flash on key perception benchmarks and offers broader language and OCR support.

vs Claude Sonnet 4.5: Qwen3-VL-Plus achieves superior visual question answering accuracy and better video temporal localization capabilities.

vs Qwen3 32B: Qwen3 VL Plus provides enhanced multimodal reasoning and substantially longer context windows for complex tasks.

vs Claude Opus 4.1: Claude Opus 4.1 is priced much higher (30x-60x) than Qwen3-VL-Plus and is optimized for conservative multi-file software engineering workflows. Qwen3-VL-Plus offers superior visual question answering, scene analysis, and long video reasoning, making it more versatile for multimodal analytic and dialog assistant scenarios.

Use cases

Document parsing and OCR, including extracting structured data from scanned PDFs, invoices, receipts, contracts, or forms.

Chart and dashboard analysis, including reading charts, graphs, and financial or research visualizations for summaries or data extraction.

Visual agent workflows for GUI-based tasks on desktop and mobile screens.

Long video understanding and analysis, including interpreting visual content across time.

Capabilities

Function calling
Streaming
Structured outputs
Reasoning
Web search

Qwen3 VL Plus API pricing

Qwen3 VL Plus pricing in US dollars
Usage	Price	Unit
Input	$0.2	1M tokens
Output	$1.6	1M tokens

Prices from the AI/ML API price list, checked on Nov 4, 2026.

Qwen3 VL Plus vs other models: token prices

AI/ML API input and output rates for 5 models, per million tokens.

AI/ML API model prices in USD per million tokens
Model	Input	Output
Qwen3 VL Plus	$0.2	$1.6
Qwen3.8 Omni Flash	$0.15	$0.47
Qwen3.8-Flash	$0.16	$0.47
Qwen 3.8 27B	$0.4761	$3.39
Qwen3.8 Max Prime	$4.23	$12.7

How to use the Qwen3 VL Plus API

View docs

Build with your agent

Give your agent the model details, code and documentation.

1Get your API key

Create API key

export AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"

2Make your first request

curl 'https://api.aimlapi.com/v1/chat/completions' \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
  "model": "alibaba/qwen3-vl-plus",
  "messages": [
    {
      "role": "user",
      "content": "Write a haiku about the ocean."
    }
  ]
}'

import os
import requests

headers = {
    "Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
    "Content-Type": "application/json",
}
payload = {
    "model": "alibaba/qwen3-vl-plus",
    "messages": [
        {
            "role": "user",
            "content": "Write a haiku about the ocean."
        }
    ]
}

response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())

const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "model": "alibaba/qwen3-vl-plus",
    "messages": [
      {
        "role": "user",
        "content": "Write a haiku about the ocean."
      }
    ]
  }),
});

if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());

All posts

Qwen3 VL Plus FAQ

What is the context window of Qwen3 VL Plus?

Qwen3 VL Plus has a 262,144 tokens context window and can return up to 32,768 tokens.

What inputs and outputs does Qwen3 VL Plus support?

Qwen3 VL Plus takes image, text as input and returns text.

What is the model ID for Qwen3 VL Plus?

Use alibaba/qwen3-vl-plus as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.

When was Qwen3 VL Plus released?

Qwen3 VL Plus became available on October 19, 2025.

How much does Qwen3 VL Plus cost?

Qwen3 VL Plus costs $0.2 input and $1.6 output per 1M tokens on AI/ML API, billed as you go with no subscription.

Does Qwen3 VL Plus support streaming?

Yes, Qwen3 VL Plus can stream responses as they are generated.

Can Qwen3 VL Plus read images?

Yes, Qwen3 VL Plus accepts image input alongside text.

Who created Qwen3 VL Plus?

Qwen3 VL Plus was built by Alibaba Cloud.

Start building with Qwen3 VL Plus

Get API key

Qwen3 VL Plus API

alibaba/qwen3-vl-plus

Build with your agent

Give your agent the model details, code and documentation.

1Get your API key

Create API key

export AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"

2Make your first request

curl 'https://api.aimlapi.com/v1/chat/completions' \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
  "model": "alibaba/qwen3-vl-plus",
  "messages": [
    {
      "role": "user",
      "content": "Write a haiku about the ocean."
    }
  ]
}'

import os
import requests

headers = {
    "Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
    "Content-Type": "application/json",
}
payload = {
    "model": "alibaba/qwen3-vl-plus",
    "messages": [
        {
            "role": "user",
            "content": "Write a haiku about the ocean."
        }
    ]
}

response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())

const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "model": "alibaba/qwen3-vl-plus",
    "messages": [
      {
        "role": "user",
        "content": "Write a haiku about the ocean."
      }
    ]
  }),
});

if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());

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Qwen3 VL Plus API

Qwen3 VL Plus overview

Qwen3 VL Plus by Alibaba Cloud

Qwen3 VL Plus specs

Qwen3 VL Plus API Overview

Technical Specifications

Performance Benchmarks

Key Features

Use Cases

Comparison with Other Models

Use cases

Capabilities

Qwen3 VL Plus API pricing

Qwen3 VL Plus vs other models: token prices

How to use the Qwen3 VL Plus API

Build with your agent

1Get your API key

2Make your first request

Related articles

Qwen3 VL Plus FAQ

Start building with Qwen3 VL Plus

Qwen3 VL Plus API

Build with your agent

1Get your API key

2Make your first request

Qwen3 VL Plus API

Qwen3 VL Plus overview

Qwen3 VL Plus by Alibaba Cloud

Qwen3 VL Plus specs

Qwen3 VL Plus API Overview

Technical Specifications

Performance Benchmarks

Key Features

Use Cases

Comparison with Other Models

Use cases

Capabilities

Qwen3 VL Plus API pricing

Qwen3 VL Plus vs other models: token prices

How to use the Qwen3 VL Plus API

Build with your agent

1Get your API key

2Make your first request

Models similar to Qwen3 VL Plus

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Qwen3.8 Omni Flash

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