Qwen3.6 27B API
Released 2026 | 256K Tokens (up to 1M) context | 27B params parameters
Qwen3.6 27B API enables Agentic coding and software development, Multimodal chat (text, images, video), Complex reasoning and analysis, Long-context document processing, Enterprise assistants and workflows, and Fine-tuning for specialized domains. Qwen3.6-27B is a medium-tier Qwen 3.6 vision-language model for multimodal chat and reasoning workloads across text, image, and video inputs. Standout strengths include Strong multimodal capability for text, image, and video inputs and Balanced latency and quality for production chat 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/Qwen3.6-27B", 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/Qwen3.6-27B", 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
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