sophnet/Qwen3-Next-80B-A3B-Thinking

Qwen3-Next-80B-A3B-Thinking is the next-generation efficient Mixture-of-Experts (MoE) inference model released by Alibaba’s Tongyi Lab. Its core positioning is as a new-generation general-purpose thinking engine characterized by “high intelligence density, low activation cost, and strong chain-of-thought capabilities.”

• Advanced MoE Architecture: With a total of 80 billion parameters, it activates only 3 billion parameters (A3B), significantly reducing computational overhead and inference latency while maintaining powerful overall performance.
• Deep Integration with Thinking Mode: It natively supports automatic step-by-step reasoning (Chain-of-Thought), enabling planning, verification, and integrated output for complex problems.
• Ultra-long Context of 128K Tokens: It supports extremely long text inputs, making it ideal for multi-document analysis, complex instruction following, and deep dialogue scenarios.
• Multi-language and Code Enhancement: It excels in tasks involving Chinese, English, and mainstream programming languages, balancing natural language understanding with structured generation.

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Core Capabilities

🧠 Efficient Deep Reasoning: With computational costs close to those of a 7-billion-parameter dense model, it achieves logic decomposition and multi-hop question-answering capabilities at the 80-billion-parameter scale. ⚡ Ultimate Energy Efficiency: Featuring few activated parameters and high throughput, it is well-suited for high-concurrency agents, customer service systems, or edge deployments on mobile devices. 🧩 Agent-Ready Architecture: It supports Function Calling and structured outputs, serving as a core engine for autonomous task planning and tool invocation. 🌍 Expert-Level Bilingual Expression in Chinese and English: In scenarios such as technical writing, policy analysis, and creative generation, it produces content that is rigorous, fluent, and contextually appropriate.