qwen/qwen3-coder-next
Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE design with 80B total parameters and only 3B activated per...
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Synced from the active catalog
Validated for gateway requests
Charged from your UAI balance
Same API key and billing flow
This page exists for direct linking, onboarding, and SEO. The same model row powers /v1/models, the public catalog, the app pricing screen, and request validation for /v1/chat/completions.
The same model route, but with one account, one balance, and one API shape.
Qwen: Qwen3 Coder Next exposes 262K tokens, which makes it a good fit for large documents, transcripts, and retrieval-heavy chat flows.
Tool and function-style parameters stay available through the same UAI request surface, so agent stacks do not need a separate provider integration.
Call qwen/qwen3-coder-next with the same UAI API key, the same /v1/chat/completions shape, and the same request validation used across the rest of the catalog.
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Use this model with your UAI key over the standard OpenAI-compatible route.
Create a key in API Keys, then send requests to https://uai.sh/v1/chat/completions.
curl https://uai.sh/v1/chat/completions \
-H "Authorization: Bearer uai-YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen/qwen3-coder-next",
"messages": [{"role": "user", "content": "Say hi in one short sentence."}],
"stream": false
}'
from openai import OpenAI
client = OpenAI(base_url="https://uai.sh/v1", api_key="uai-YOUR_API_KEY")
response = client.chat.completions.create(
model="qwen/qwen3-coder-next",
messages=[{"role": "user", "content": "Say hi in one short sentence."}],
stream=False
)
print(response.choices[0].message.content)
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://uai.sh/v1",
apiKey: "uai-YOUR_API_KEY"
});
const response = await client.chat.completions.create({
model: "qwen/qwen3-coder-next",
messages: [{ role: "user", content: "Say hi in one short sentence." }],
stream: false
});
console.log(response.choices[0].message.content);
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