Qwen3.5 122B A10B API
Released February 24, 2026 | 256K Tokens (up to 1M) context | 122B params (10B active) parameters
Qwen3.5 122B A10B API enables Advanced multimodal reasoning (text + image + video), Enterprise-grade document understanding and OCR, Complex agentic workflows with function calling, Long-horizon planning and analysis (256K context), GUI automation (ScreenSpot Pro: 70.4 vs Claude Sonnet 4.5: 36.2), Scientific and research-grade problem solving, and RAG over massive document repositories. Qwen3.5-122B-A10B is the most powerful open-source model in the Qwen3.5 Medium Series. With 122B total parameters and 10B active per token across a 48-layer hybrid architecture, it delivers the strongest knowledge, vision, and function-calling performance in the medium class — scoring 86.6% on GPQA Diamond (beating GPT-5 mini's 82.8%), 72.2% on BFCL-V4 tool calling (vs GPT-5 mini's 55.5%), 92.1% on OCRBench, and 83.9% on MMMU. Supports text, image, and video input natively via early fusion. Standout strengths include 86.6% GPQA Diamond — beats GPT-5 mini (82.8%) by 4 points and 72.2% BFCL-V4 function calling — 30% ahead of GPT-5 mini (55.5%). 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/Qwen3.5-122B-A10B", 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=16384, temperature=1, top_p=0.95, stream=True, presence_penalty=1.5 ) 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/Qwen3.5-122B-A10B", 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=16384, temperature=1, top_p=0.95, stream=True, presence_penalty=1.5 ) 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
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Research & Clinical Intelligence