Qwen3.5 35B A3B API
Released February 24, 2026 | 256K Tokens (up to 1M) context | 35B params (3B active) parameters
Qwen3.5 35B A3B API enables Consumer and edge device deployment (8GB GPU), Agentic coding and tool-calling workflows, Multimodal chat (text, image, video via early fusion), Cost-efficient enterprise inference at scale, Long-context document analysis, and Complex reasoning with thinking mode. Qwen3.5-35B-A3B is the breakout model of the Qwen3.5 Medium Series and arguably the biggest efficiency breakthrough in recent open-source AI. Despite having only 3B active parameters per token (8.6% of total), it outperforms the previous generation's 235B model on most benchmarks, as well as GPT-5 mini and Claude Sonnet 4.5 on knowledge (MMMLU) and visual reasoning (MMMU-Pro). It runs on an 8GB GPU and supports 256K context natively. Standout strengths include Beats Qwen3-235B-A22B with only 3B active params — historic efficiency and Outperforms GPT-5 mini and Claude Sonnet 4.5 on MMMLU and MMMU-Pro. 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/Qwen3.5-35B-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/Qwen3.5-35B-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
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