Last refreshed 2026-06-29. Next refresh: weekly.
Why use Qwen3-Coder-Next on Vercel AI Gateway?
Vercel AI Gateway offers Qwen3-Coder-Next with pay-as-you-go pricing at $0.50/1M 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-Next 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-nextRequest 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-next",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "alibaba/qwen3-coder-next", not the LLMReference slug "qwen3-coder-next".
- 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-Next Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| AWS Bedrock | $0.50 | $1.20 |
| OpenRouter | $0.12 | $0.80 |
| Vercel AI Gateway | $0.50 | $1.20 |
| Novita AI | $0.20 | $1.50 |
Pricing
| 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%.