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About the Provider

Qwen is an AI model family developed by Alibaba Group, a major Chinese technology and cloud computing company. Through its Qwen initiative, Alibaba builds and open-sources advanced language, images and coding models under permissive licenses to support innovation, developer tooling, and scalable AI integration across applications.

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Model Quickstart

This section helps you quickly get started with the Qwen/Qwen3-Coder-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-Coder-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-Coder-Flash",
    messages=[
      {
        "role": "user",
        "content": "Write a Python function to calculate fibonacci sequence"
      }
    ],
    max_tokens=8962,
    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 Coder Flash is a lightweight, fast coding model optimized for speed.
  • Built on a Transformer decoder-only architecture with up to 1M token context, it is designed for quick code snippets, function-level completions, and low-cost automation workflows.
  • It offers very fast inference at low cost, making it ideal for interactive development, editor-style auto-complete, and internal tooling scripts.

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Model at a Glance

FeatureDetails
Model IDQwen/Qwen3-Coder-Flash
ProviderAlibaba Cloud (Qwen Team)
ArchitectureTransformer decoder-only
Model SizeN/A
Parameters4
Context LengthUp to 1M Tokens
Release Date2025
LicenseApache 2.0
Training DataMultilingual code from GitHub and coding platforms

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When to use?

You should consider using Qwen3 Coder Flash if:
  • You need quick code snippets and function-level completions during interactive development
  • Your application requires editor-style auto-complete for common patterns, boilerplate, and API usage
  • You are building small automation scripts, utilities, and glue code for internal tooling

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Inference Parameters

Parameter NameTypeDefaultDescription
StreamingbooleantrueEnable streaming responses for real-time output.
Temperaturenumber0.1Lower temperature for more deterministic code generation.
Max Tokensnumber8962Maximum number of tokens the model can generate.
Top Pnumber1Controls nucleus sampling for more predictable output.

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Key Features

  • Very Fast: Optimized for low-latency inference, ideal for interactive development and real-time code completion.
  • Low Cost: Efficient architecture enabling cost-effective code generation at scale.
  • Up to 1M Token Context: Supports long codebases and extended coding sessions.
  • Multilingual Code: Trained on multilingual code from GitHub and coding platforms.

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Summary

Qwen3 Coder Flash is Alibaba’s lightweight open-source coding model built for speed and low-cost code generation.
  • It uses a Transformer decoder-only architecture with up to 1M token context, trained on multilingual code from GitHub and coding platforms.
  • It is optimized for quick code snippets, function-level completions, boilerplate generation, and small automation scripts.
  • The model delivers very fast inference at low cost for interactive development workflows.
  • Licensed under Apache 2.0 for full commercial use.