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About the Provider

Alibaba Cloud is the cloud computing arm of Alibaba Group and the creator of the Qwen model family. Through its open-source initiative, Alibaba has released state-of-the-art language and multimodal models under permissive licenses, enabling developers and enterprises to build powerful AI applications across diverse domains and languages.

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Model Quickstart

This section helps you quickly get started with the Qwen/Qwen3-VL-Flash model on the Qubrid AI inferencing platform. To use this model, you need:
  • A valid Qubrid API key
  • Access to the Qubrid inference API
  • Basic knowledge of making API requests in your preferred language
Once authenticated with your API key, you can send inference requests to the Qwen/Qwen3-VL-Flash model and receive responses based on your input prompts. Below are example placeholders showing how the model can be accessed using different programming environments.
You can choose the one that best fits your workflow. 
from openai import OpenAI

# Initialize the OpenAI client with Qubrid base URL
client = OpenAI(
    base_url="https://platform.qubrid.com/v1",
    api_key="QUBRID_API_KEY",
)

# Create a streaming chat completion
stream = client.chat.completions.create(
    model="Qwen/Qwen3-VL-Flash",
    messages=[
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What is in this image? Describe the main elements."
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
            }
          }
        ]
      }
    ],
    max_tokens=16384,
    temperature=0.1,
    top_p=1,
    stream=True
)

# If stream = False comment this out
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)
print("\n")

# If stream = True comment this out
print(stream.choices[0].message.content)

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Model Overview

Qwen3 VL Flash is a faster, lighter vision model for real-time use cases.
  • Built on the same Transformer decoder-only architecture with ViT visual encoder as Qwen3 VL Plus, it is optimized for low-latency inference with up to 256K token context.
  • Designed for live document reading, quick image analysis, and production pipelines where speed and cost efficiency are the priority.

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Model at a Glance

FeatureDetails
Model IDQwen/Qwen3-VL-Flash
ProviderAlibaba Cloud (Qwen Team)
ArchitectureTransformer decoder-only (Qwen3-VL with ViT visual encoder)
Model SizeN/A
Parameters5
Context LengthUp to 256K Tokens
Release Date2025
LicenseApache 2.0
Training DataMultilingual multimodal dataset (text + images)

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When to use?

You should consider using Qwen3 VL Flash if:
  • You need live document reading for receipts, forms, or on-screen content streamed from cameras
  • Your application requires quick image analysis in dashboards, monitoring tools, and low-latency user interfaces

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Inference Parameters

Parameter NameTypeDefaultDescription
StreamingbooleantrueEnable streaming responses for real-time output.
Temperaturenumber0.1Lower temperature for more deterministic output.
Max Tokensnumber16384Maximum number of tokens the model can generate.
Top Pnumber1Controls nucleus sampling for more predictable output.
Reasoning EffortselectmediumAdjusts the depth of reasoning and problem-solving effort. Higher settings yield more thorough responses at the cost of latency.

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Key Features

  • Fast and Low Latency: Optimized for real-time image analysis and low-latency production pipelines.
  • ViT Visual Encoder: Dedicated vision transformer encoder for accurate image feature extraction.
  • Up to 256K Context: Supports extended multimodal conversations and document processing.
  • Low Cost: 0.05input/0.05 input / 0.40 output per 1M tokens — highly cost-effective for high-volume vision workloads.
  • Apache 2.0 License: Fully open-source with unrestricted commercial use.

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Summary

Qwen3 VL Flash is Alibaba’s fastest vision-language model, built for real-time and cost-efficient multimodal inference.
  • It uses a Transformer decoder-only architecture with a ViT visual encoder, trained on a multilingual multimodal dataset.
  • It is optimized for live document reading, quick image analysis, and low-latency dashboards and monitoring tools.
  • The model supports up to 256K context with configurable reasoning effort at minimal cost (0.05/0.05/0.40 per 1M tokens).
  • Licensed under Apache 2.0 for full commercial use.