Last refreshed 2026-06-19. Next refresh: weekly.
Why use Qwen3-Coder-30B-A3B-Instruct on AWS Bedrock?
AWS Bedrock offers Qwen3-Coder-30B-A3B-Instruct with pay-as-you-go pricing at $0.15/1M input tokens. 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.
Compare Qwen3-Coder-30B-A3B-Instruct across 4 providers to find the best fit for your use caseSetup recipe
Python + curlpip install boto3export AWS_ACCESS_KEY_ID=...import boto3
client = boto3.client("bedrock-runtime", region_name="us-east-1")
response = client.converse(
modelId="qwen3-coder-30b-a3b",qwen3-coder-30b-a3bRequest example
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"])Gotchas
- Use 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.
- The endpoint template includes a region segment; set the same region in your SDK/client configuration.
- The examples expect AWS_ACCESS_KEY_ID; rename it only if your application config maps the new variable.
Compare Qwen3-Coder-30B-A3B-Instruct Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| AWS Bedrock | $0.15 | $0.62 |
| Vercel AI Gateway | $0.15 | $0.60 |
| Novita AI | $0.07 | $0.27 |
| OpenRouter | $0.07 | $0.28 |
Pricing
| 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.