Qwen3.6 35B A3B API
Released 2026 | 256K Tokens (up to 1M) context | 35B params (A3B active) parameters
Qwen3.6 35B A3B API enables Cost-efficient enterprise inference at scale, Agentic coding and tool-calling workflows, Multimodal chat (text, image, video), Long-context document analysis, Complex reasoning with optional thinking mode, and Edge and cloud deployment scenarios. Qwen3.6-35B-A3B is an efficient MoE variant in the Qwen 3.6 family aimed at strong multimodal reasoning and cost-effective deployment. Standout strengths include MoE efficiency profile with strong capability-per-cost and Supports multimodal inputs and reasoning-heavy workloads. It is well suited for multimodal assistants that combine image understanding with grounded text reasoning in real-time workflows.
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/Qwen3.6-35B-A3B", messages=[ { "role": "user", "content": [ { "type": "text", "text": "What is in this image? Describe the main elements." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ], max_tokens=8192, temperature=0.6, 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")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/Qwen3.6-35B-A3B", messages=[ { "role": "user", "content": [ { "type": "text", "text": "What is in this image? Describe the main elements." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ], max_tokens=8192, temperature=0.6, 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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