Qwen3 Coder Next API
Released February 1, 2026 | 262K Tokens context | 79.7B params (3B active) parameters
Qwen3 Coder Next API enables Agentic software development & long-horizon coding, Complex tool use & function orchestration, Execution failure recovery in dynamic workflows, Repository-scale navigation and bug fixing, Automated testing, refactoring & documentation, and CI/CD pipeline integration for code generation. Qwen3-Coder-Next is an open-weight MoE language model designed specifically for coding agents. With only 3B activated parameters out of 79.7B total, it achieves performance comparable to models with 10–20x more active parameters. It features a hybrid Gated Attention + Gated DeltaNet MoE architecture with 512 experts (10 active per token), 262K native context, and achieves 74.2% on SWE-Bench Verified — making it highly cost-effective for production agent deployment. Standout strengths include Only 3B active params from 79.7B total — performs like 30–60B models and 74.2% on SWE-Bench Verified, 63.7% SWE-Bench Multilingual. It is a strong fit for coding copilots, repository workflows, and tool-augmented engineering assistants in production environments.
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-Next", messages=[ { "role": "user", "content": "Write a Python function to calculate fibonacci sequence" } ], max_tokens=8192, temperature=1, 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-Coder-Next", messages=[ { "role": "user", "content": "Write a Python function to calculate fibonacci sequence" } ], max_tokens=8192, temperature=1, 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
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