Last refreshed 2026-06-29. Next refresh: weekly.
Why use Qwen3-Coder-30B-A3B-Instruct on Vercel AI Gateway?
Vercel AI Gateway offers Qwen3-Coder-30B-A3B-Instruct with pay-as-you-go pricing at $0.15/1M input tokens. Vercel AI Gateway is a unified AI proxy providing a single OpenAI-compatible API endpoint to 275+ models from 25+ providers including Anthropic, OpenAI, Google, Meta, Mistral, DeepSeek, xAI, Alibaba, Amazon, ByteDance, Cohere, MiniMax, MoonshotAI, KwaiPilot, Black Forest Labs, Recraft, Voyage AI, NVIDIA, and more.
Compare Qwen3-Coder-30B-A3B-Instruct across 4 providers to find the best fit for your use caseSetup recipe
Python + curlpip install openaiexport AI_GATEWAY_API_KEY=...import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["AI_GATEWAY_API_KEY"],alibaba/qwen3-coder-30b-a3bRequest example
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["AI_GATEWAY_API_KEY"],
base_url="https://ai-gateway.vercel.sh/v1"
)
response = client.chat.completions.create(
model="alibaba/qwen3-coder-30b-a3b",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "alibaba/qwen3-coder-30b-a3b", not the LLMReference slug "qwen3-coder-30b-a3b".
- creator/model-name e.g. kwaipilot/kat-coder-pro-v2
- The examples expect AI_GATEWAY_API_KEY; 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 | $0.60 |
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.