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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-480B-A35B-Instruct 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-480B-A35B-Instruct 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-480B-A35B-Instruct",
    messages=[
      {
        "role": "user",
        "content": "Write a Python function to calculate fibonacci sequence"
      }
    ],
    max_tokens=8192,
    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-480B-A35B-Instruct is Alibaba’s flagship open-source coding model powered by a sparse Mixture-of-Experts (MoE) architecture with 480B total parameters and 35B activated per forward pass.
  • It achieves state-of-the-art performance among open-source models, supporting up to 256K context across 100+ programming languages.
  • Ideal for agentic coding, complex refactoring, and large-scale software engineering workflows.

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Model at a Glance

FeatureDetails
Model IDQwen/Qwen3-Coder-480B-A35B-Instruct
ProviderAlibaba Cloud (Qwen Team)
ArchitectureSparse Mixture-of-Experts (MoE) Transformer
Model Size480B params (35B active)
Parameters4
Context Length256K Tokens
Release Date2025
LicenseApache 2.0
Training DataCode-focused datasets with instruction tuning across 100+ programming languages

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When to use?

You should consider using Qwen3 Coder 480B A35B Instruct if:
  • You need large codebase refactoring across multiple files
  • Your application requires multi-file code generation
  • You are working on complex algorithm design and system architecture
  • Your use case involves advanced debugging
  • You need tool-calling agent workflows for software engineering tasks

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Inference Parameters

Parameter NameTypeDefaultDescription
StreamingbooleantrueEnable streaming responses for real-time output.
Temperaturenumber0.1Lower temperature for more deterministic code generation.
Max Tokensnumber8192Maximum number of tokens the model can generate.
Top Pnumber1Controls nucleus sampling for more predictable output.

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Key Features

  • SOTA Open-Source Coding Model: Achieves state-of-the-art performance among open-source models on coding benchmarks.
  • 480B MoE with 35B Active: Frontier-level coding capacity with only 35B parameters active per token.
  • 256K Context Window: Supports large codebases, multi-file reasoning, and complex long-horizon tasks.
  • 100+ Programming Languages: Instruction-tuned across a wide range of programming languages.
  • Strong Agentic Capabilities: Built for tool-calling agent workflows and complex software engineering automation.

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Summary

Qwen3-Coder-480B-A35B-Instruct is Alibaba’s flagship open-source coding model built for large-scale software engineering.
  • It uses a Sparse MoE Transformer with 480B total and 35B active parameters, instruction-tuned across 100+ programming languages.
  • It achieves SOTA performance among open-source models with a 256K context window and strong agentic tool-calling capabilities.
  • The model is designed for large codebase refactoring, complex algorithm design, system architecture, and agent workflows.
  • Licensed under Apache 2.0 for full commercial use.