Using Qwen3-Coder-Next on Vercel AI Gateway
Implementation guide · Qwen3-Coder · Alibaba
Vercel AI Gateway exposes Qwen3-Coder-Next through model ID alibaba/qwen3-coder-next. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.
Last refreshed 2026-06-29. Next refresh: weekly.
Quick Start
- 1
- 2Use the Vercel AI Gateway SDK or REST API to call
alibaba/qwen3-coder-next— see the documentation for request format. - 3
Code Examples
pip install openaiAI_GATEWAY_API_KEYalibaba/qwen3-coder-nextcreator/model-name e.g. kwaipilot/kat-coder-pro-v2
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-next",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Pricing on Vercel AI Gateway
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.50 |
| Output tokens | $1.20 |
Capabilities
About Qwen3-Coder-Next
Qwen3-Coder-Next is an ultra-sparse Mixture-of-Experts coding agent model from Alibaba's Qwen team, released February 3, 2026 under Apache 2.0. It has 80B total parameters with 3B active at inference, delivering substantially higher throughput than comparable dense models. It supports a native 256K context window, function calling, structured outputs, Claude Code, Qwen Code, Cline, Kilo, and other scaffold templates. Benchmarks reported in the DAT-3724 datapack include SWE-Bench Pro 44.3%, SWE-Bench Resolved 70.6%, and TerminalBench 2 36.2%.