Qwen3 Technical Report
打开互动全文版（逐段中英对照 + 图/公式 + 论文问答）→本文介绍了 Qwen3，这是 Qwen 模型系列的最新版本。Qwen3 包含一系列大语言模型，旨在提升性能、效率和多语言能力。该系列包括密集和混合专家（MoE）架构的模型，参数规模从 0.6 亿到 2350 亿不等。Qwen3 的一个关键创新是将思考模式（用于复杂多步推理）和非思考模式（用于快速、上下文驱动的响应）整合到一个统一框架中，消除了在不同模型之间切换的需要，并支持根据用户查询或聊天模板动态切换模式。同时，Qwen3 引入了思考预算机制，允许用户在推理过程中自适应地分配计算资源，从而根据任务复杂度平衡延迟和性能。此外，通过利用旗舰模型的知识，我们显著减少了构建较小规模模型所需的计算资源，同时确保其具有高度竞争力的性能。实证评估表明，Qwen3 在代码生成、数学推理、智能体任务等多种基准测试中取得了领先结果，与更大的 MoE 模型和专有模型相比具有竞争力。与上一代 Qwen2.5 相比，Qwen3 将多语言支持从 29 种扩展到 119 种语言和方言，通过改进的跨语言理解和生成能力增强了全球可访问性。为了促进可重复性和社区驱动的研究与开发，所有 Qwen3 模型均在 Apache 2.0 许可下公开提供。
In this work, we present Qwen3, the latest version of the Qwen model family. Qwen3 comprises a series of large language models (LLMs) designed to advance performance, efficiency, and multilingual capabilities. The Qwen3 series includes models of both dense and Mixture-of-Expert (MoE) architectures, with parameter scales ranging from 0.6 to 235 billion. A key innovation in Qwen3 is the integration of thinking mode (for complex, multi-step reasoning) and non-thinking mode (for rapid, context-driven responses) into a unified framework. This eliminates the need to switch between different models--such as chat-optimized models (e.g., GPT-4o) and dedicated reasoning models (e.g., QwQ-32B)--and enables dynamic mode switching based on user queries or chat templates. Meanwhile, Qwen3 introduces a thinking budget mechanism, allowing users to allocate computational resources adaptively during inference, thereby balancing latency and performance based on task complexity. Moreover, by leveraging the knowledge from the flagship models, we significantly reduce the computational resources required to build smaller-scale models, while ensuring their highly competitive performance. Empirical evaluations demonstrate that Qwen3 achieves state-of-the-art results across diverse benchmarks, including tasks in code generation, mathematical reasoning, agent tasks, etc., competitive against larger MoE models and proprietary models. Compared to its predecessor Qwen2.5, Qwen3 expands multilingual support from 29 to 119 languages and dialects, enhancing global accessibility through improved cross-lingual understanding and generation capabilities. To facilitate reproducibility and community-driven research and development, all Qwen3 models are publicly accessible under Apache 2.0.