Qwen3 Coder 30B A3B Instruct
v1Qwen3-Coder-30B-A3B-Instruct is a sparse Mixture-of-Experts (MoE) model with around 30.5B total parameters (3.3B active per inference), 48 layers, supporting extremely long context (native 262,144 tokens — extendable to 1M in some deployments).
Input <= 32k
Save 20%input tokens
output tokens
Cached $0.56$0.45/1M cached tokens
32k < Input <= 128k
Save 20%input tokens
output tokens
Cached $0.94$0.75/1M cached tokens
128k < Input <= 256k
Save 20%input tokens
output tokens
Cached $1.50$1.20/1M cached tokens
256k < Input <= 1m
Save 20%input tokens
output tokens
Cached $3.00$2.40/1M 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-30B-A3B-Instruct", messages=[ { "role": "user", "content": "Write a Python function to calculate fibonacci sequence" } ], max_tokens=65536, temperature=0.7, top_p=0.8, 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
Don't let your AI control you. Control your AI the Qubrid way!
Have questions? Want to Partner with us? Looking for larger deployments or custom fine-tuning? Let's collaborate on the right setup for your workloads.
"Qubrid's medical OCR and research parsing cut our document extraction time in half. We now have traceable pipelines and reproducible outputs that meet our compliance requirements."
Clinical AI Team
Research & Clinical Intelligence