Model Marketplace

Qwen3-Open-Source

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Overview

The Instruct version of the 8B Dense model in the Qwen3-VL series requires less GPU memory and provides comprehensively upgraded image/video understanding, support for extremely long contexts like lengthy videos and documents, spatial awareness, and object recognition capabilities, making it suitable for tackling complex real-world tasks.

Input

TextImageVideo

Output

Text

Features

Prefix Completion

Enable Partial Mode when calling the Qwen API to make the model continue strictly from your provided prefix text.View docs

Function Calling

Use function calling to connect large language models with external tools and systems.View docs

Cache

Context Cache stores shared prefixes for long-context requests to reduce repeated computation, improve latency, and lower cost.View docs

Structured Outputs

Structured Outputs help ensure the model returns a JSON string in the expected format.View docs

Batches

Asynchronously process requests in batches to reduce costs.View docs

Web Search

Enable web search so the model can answer with real-time retrieved data.View docs

Fine-tuning

Train models on sample data to better adapt them to specific tasks.View docs

Pricing

  • Input
    $0.18Per 1M tokens
  • Output
    $0.7Per 1M tokens

Rate Limits & Context

  • Max Input
    129K
  • Max Output
    32K
  • Context
    131K
  • TPMTokens Per Minute
    100K
  • RPMRequests Per Minute
    60

API Reference

Call API
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import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)

completion = client.chat.completions.create(
    model="qwen3-vl-8b-instruct",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/ctdzex/biaozhun.jpg"
                    },
                },
                {"type": "text", "text": "Output the text in the image only."},
            ],
        },
    ],
)
print(completion.choices[0].message.content)