Using Qwen3-Coder-30B-A3B-Instruct on AWS Bedrock
Implementation guide · Qwen3-Coder · Alibaba
Quick Start
- 1
- 2Use the AWS Bedrock SDK or REST API to call
qwen3-coder-30b-a3b— see the documentation for request format. - 3
Code Examples
pip install boto3AWS_ACCESS_KEY_IDqwen3-coder-30b-a3bUse Amazon Bedrock model IDs, e.g. "anthropic.claude-3-opus-20240229-v1:0" for on-demand, or cross-region inference profile IDs like "us.anthropic.claude-opus-4-7-20251101-v1:0". These differ from the public model slug.
import boto3
# Reads AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_DEFAULT_REGION from env
client = boto3.client("bedrock-runtime", region_name="us-east-1")
response = client.converse(
modelId="qwen3-coder-30b-a3b",
messages=[{
"role": "user",
"content": [{"text": "Hello"}]
}]
)
print(response["output"]["message"]["content"][0]["text"])About AWS Bedrock
Amazon Bedrock is a comprehensive, fully managed service for building and scaling generative AI applications. The platform provides access to a diverse array of high-performing foundation models (FMs) from leading AI companies through a unified API, enabling users to select the most suitable models for their specific use cases. Key features include model customization using proprietary data through techniques like fine-tuning and Retrieval Augmented Generation (RAG), which significantly enhances the relevance and accuracy of AI outputs. Additionally, the platform supports the automation of complex tasks with agents capable of executing multi-step operations, making it versatile for applications ranging from text generation and image creation to conversational AI. Beyond its robust technical capabilities, Amazon Bedrock offers a serverless experience that streamlines infrastructure management, allowing developers to focus on application development without the burden of managing underlying resources. The platform prioritizes security and compliance, ensuring that data remains within the AWS ecosystem and adheres to industry standards. Bedrock's flexible pricing models, including pay-as-you-go options, enable organizations to effectively manage costs while scaling their AI initiatives. This combination of advanced features, ease of use, and cost-effectiveness positions Amazon Bedrock as a powerful tool for businesses looking to innovate rapidly in the generative AI space, ultimately enhancing productivity and operational efficiency.
AWS Bedrock is Amazon's fully managed foundation-model service, providing unified API access to top models from Anthropic, Meta, Mistral, and other leading AI labs with built-in tools for RAG, fine-tuning, and AI agent development.
Pricing on AWS Bedrock
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.15 |
| Output tokens | $0.62 |
Capabilities
About Qwen3-Coder-30B-A3B-Instruct
Qwen3-Coder-30B-A3B-Instruct is Alibaba's efficient open-source code generation model in the Qwen3-Coder family, released December 3, 2025 under the Apache 2.0 license. The model has 30.5 billion total parameters with 3.3 billion active per forward pass, organized across 48 transformer layers with 128 experts and 8 activated per token. It uses Grouped Query Attention (GQA) with 32 query heads and 4 key-value heads. Native context window is 262,144 tokens, extendable to 1 million tokens via YaRN. The model supports multi-turn tool calling, function calling, repository-level code understanding, and structured outputs. It is compatible with vLLM, SGLang, Ollama, LM Studio, llama.cpp, and HuggingFace Transformers. Available via AWS Bedrock, Novita AI, and Vercel AI Gateway.