Qwen/Qwen3-Max

Qwen3-Max is Alibaba Cloud's most powerful closed-source model in the Qwen3 series, featuring a 235B Mixture-of-Experts architecture with 22B parameters active per forward pass. It delivers frontier-level performance in complex reasoning, multilingual tasks, long-context understanding, and advanced coding — rivaling GPT-4o and Claude Sonnet on major benchmarks. Accessible exclusively via the DashScope API.

Alibaba Cloud (Qwen) Chat 128K Tokens
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api_example.sh

curl -X POST "https://platform.qubrid.com/v1/chat/completions" \
  -H "Authorization: Bearer QUBRID_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "Qwen/Qwen3-Max",
  "messages": [
    {
      "role": "user",
      "content": "Explain quantum computing in simple terms"
    }
  ],
  "temperature": 0.7,
  "max_tokens": 4096,
  "stream": true,
  "top_p": 1
}'

Technical Specifications

Model Architecture & Performance

Variant Instruct
Model Size 235B params (22B active)
Context Length 128K Tokens
Quantization Proprietary
Tokens/Second 80
Architecture Sparse Mixture-of-Experts (MoE) Transformer — 235B total / 22B active parameters per token
Precision Proprietary (served via DashScope inference infrastructure)
License Proprietary — Alibaba Cloud DashScope API only
Release Date April 2025
Developers Alibaba Cloud (QwenLM)

Pricing

Pay-per-use, no commitments

Input Tokens $0.0012/1K Tokens
Output Tokens $0.006/1K Tokens

API Reference

Complete parameter documentation

Parameter Type Default Description
stream boolean true Enable streaming responses for real-time output.
temperature number 0.7 Controls creativity and randomness. Higher values produce more diverse output.
max_tokens number 4096 Maximum number of tokens the model can generate.
top_p number 1 Controls nucleus sampling for more predictable output.

Explore the full request and response schema in our external API documentation

Performance

Strengths & considerations

Strengths Considerations
235B MoE architecture — frontier-level intelligence
Up to 128K context window
Strong multilingual performance (29+ languages)
Excellent instruction following and structured output
Hybrid thinking mode for complex reasoning tasks
Competitive with GPT-4o and Claude Sonnet on key benchmarks
Closed-source — no self-hosting or weight access
Higher latency than smaller Qwen models
Requires DashScope API access
Higher cost per token vs open-source alternatives

Use cases

Recommended applications for this model

Complex multi-step reasoning
Advanced coding and debugging
Research and analytical writing
Long-document summarization
Multilingual chat and translation
Enterprise chatbots and assistants

Enterprise
Platform Integration

Docker

Docker Support

Official Docker images for containerized deployments

Kubernetes

Kubernetes Ready

Production-grade KBS manifests and Helm charts

SDK

SDK Libraries

Official SDKs for Python, Javascript, Go, and Java

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