Qwen3 Coder Next
latestQwen3-Coder-Next is an open-weight MoE language model designed specifically for coding agents. With only 3B activated parameters out of 79.7B total, it achieves performance comparable to models with 10–20x more active parameters. It features a hybrid Gated Attention + Gated DeltaNet MoE architecture with 512 experts (10 active per token), 262K native context, and achieves 74.2% on SWE-Bench Verified — making it highly cost-effective for production agent deployment.
Input <= 32k
Save 20%input tokens
output tokens
Cached $0.37$0.30/1M cached tokens
32k < Input <= 128k
Save 20%input tokens
output tokens
Cached $0.63$0.50/1M cached tokens
128k < Input <= 256k
Save 20%input tokens
output tokens
Cached $1.00$0.80/1M cached tokens
from openai import OpenAI # Initialize the OpenAI client with Qubrid base URL client = OpenAI( base_url="https://platform.qubrid.com/v1", api_key="QUBRID_API_KEY", ) stream = client.chat.completions.create( model="Qwen/Qwen3-Coder-Next", messages=[ { "role": "user", "content": "Write a Python function to calculate fibonacci sequence" } ], max_tokens=8192, temperature=1, top_p=0.95, stream=True ) for chunk in stream: if chunk.choices and chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="", flush=True) print("\n") Enterprise
Platform Integration
Docker Support
Official Docker images for containerized deployments
Kubernetes Ready
Production-grade KBS manifests and Helm charts
SDK Libraries
Official SDKs for Python, Javascript, Go, and Java
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