title: Qwen3 Technical Report
summary: 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.
paper_authors._id: 68299e3128752b51372d31ea
paper_authors.name: An Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-19T06:43:00.733Z
paper_authors._id: 62088594a5943c8a8fc94560
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1644733028938-62088594a5943c8a8fc94560.png
paper_authors.fullname: An Yang
paper_authors.type: user
paper_authors.user: yangapku
paper_authors._id: 68299e3128752b51372d31eb
paper_authors.name: Anfeng Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T07:15:44.771Z
paper_authors._id: 6799128b9da39716ab1ebd95
paper_authors.avatarUrl: /avatars/677d8ae2087137134c3f0e58f4cf769f.svg
paper_authors.fullname: Anfeng Li
paper_authors.type: user
paper_authors.user: laf070810
paper_authors._id: 68299e3128752b51372d31ec
paper_authors.name: Baosong Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T07:15:37.853Z
paper_authors._id: 64b0a77df12b47366663884c
paper_authors.avatarUrl: /avatars/a212ea862abb5966060e439dd0e7656f.svg
paper_authors.fullname: Baosong Yang
paper_authors.type: user
paper_authors.user: Baosong
paper_authors._id: 68299e3128752b51372d31ed
paper_authors.name: Beichen Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T07:16:13.672Z
paper_authors._id: 64b93578ee257c3a4cfceed1
paper_authors.avatarUrl: /avatars/e6188562254f75a09b4048b800860016.svg
paper_authors.fullname: Beichen Zhang
paper_authors.type: user
paper_authors.user: BeichenZhang
paper_authors._id: 68299e3128752b51372d31ee
paper_authors.name: Binyuan Hui
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T07:16:22.151Z
paper_authors._id: 61e4c4ca1ab24785ac11ba69
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/61e4c4ca1ab24785ac11ba69/1Q1zhhyGSJ9RJG9MzwxVv.jpeg
paper_authors.fullname: Binyuan Hui
paper_authors.type: user
paper_authors.user: huybery
paper_authors._id: 68299e3128752b51372d31ef
paper_authors.name: Bo Zheng
paper_authors._id: 68299e3128752b51372d31f0
paper_authors.name: Bowen Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T07:16:31.453Z
paper_authors._id: 6583ab7983a9e1460c67d876
paper_authors.avatarUrl: /avatars/74400bc448c3f07e23a4cd53d68a6af7.svg
paper_authors.fullname: bowen
paper_authors.type: user
paper_authors.user: bowenYu
paper_authors._id: 68299e3128752b51372d31f1
paper_authors.name: Chang Gao
paper_authors._id: 68299e3128752b51372d31f2
paper_authors.name: Chengen Huang
paper_authors._id: 68299e3128752b51372d31f3
paper_authors.name: Chenxu Lv
paper_authors._id: 68299e3128752b51372d31f4
paper_authors.name: Chujie Zheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-19T06:43:04.798Z
paper_authors._id: 610b70452719facd4ea85e28
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/610b70452719facd4ea85e28/S7nMy7D0Rxq0VIVblhYDG.jpeg
paper_authors.fullname: Chujie Zheng
paper_authors.type: user
paper_authors.user: chujiezheng
paper_authors._id: 68299e3128752b51372d31f5
paper_authors.name: Dayiheng Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T07:17:32.677Z
paper_authors._id: 6434d4989bd5a84b5dd0b0f5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6434d4989bd5a84b5dd0b0f5/0Elf9qbfG9Hkgypm9pTGm.jpeg
paper_authors.fullname: Dayiheng Liu
paper_authors.type: user
paper_authors.user: Losin94
paper_authors._id: 68299e3128752b51372d31f6
paper_authors.name: Fan Zhou
paper_authors._id: 68299e3128752b51372d31f7
paper_authors.name: Fei Huang
paper_authors._id: 68299e3128752b51372d31f8
paper_authors.name: Feng Hu
paper_authors._id: 68299e3128752b51372d31f9
paper_authors.name: Hao Ge
paper_authors._id: 68299e3128752b51372d31fa
paper_authors.name: Haoran Wei
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T07:17:56.110Z
paper_authors._id: 6436618aeef1f55654a9f458
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6436618aeef1f55654a9f458/OvxGtuDg2GAFG9As-2hzW.jpeg
paper_authors.fullname: Haoran Wei
paper_authors.type: user
paper_authors.user: HaoranWei
paper_authors._id: 68299e3128752b51372d31fb
paper_authors.name: Huan Lin
paper_authors._id: 68299e3128752b51372d31fc
paper_authors.name: Jialong Tang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T07:18:16.959Z
paper_authors._id: 63281d05ac205d01918b5fc7
paper_authors.avatarUrl: /avatars/fc3e0f7285bb2869a92670f764dfc535.svg
paper_authors.fullname: Jialong Tang
paper_authors.type: user
paper_authors.user: Jialong
paper_authors._id: 68299e3128752b51372d31fd
paper_authors.name: Jian Yang
paper_authors._id: 68299e3128752b51372d31fe
paper_authors.name: Jianhong Tu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T07:18:30.045Z
paper_authors._id: 654bead777401b47e6424f88
paper_authors.avatarUrl: /avatars/7bcbdbb051c93b004f0dc3ad36c4a0ce.svg
paper_authors.fullname: Jianhong Tu
paper_authors.type: user
paper_authors.user: ToviTu
paper_authors._id: 68299e3128752b51372d31ff
paper_authors.name: Jianwei Zhang
paper_authors._id: 68299e3128752b51372d3200
paper_authors.name: Jianxin Yang
paper_authors._id: 68299e3128752b51372d3201
paper_authors.name: Jiaxi Yang
paper_authors._id: 68299e3128752b51372d3202
paper_authors.name: Jing Zhou
paper_authors._id: 68299e3128752b51372d3203
paper_authors.name: Jingren Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T07:20:51.253Z
paper_authors._id: 602f88f5e8149a962412a667
paper_authors.avatarUrl: /avatars/b78f0e583df8e5d5e3365934fe5f4900.svg
paper_authors.fullname: Zhou
paper_authors.type: user
paper_authors.user: Jingren
paper_authors._id: 68299e3128752b51372d3204
paper_authors.name: Junyang Lin
paper_authors._id: 68299e3128752b51372d3205
paper_authors.name: Kai Dang
paper_authors._id: 68299e3128752b51372d3206
paper_authors.name: Keqin Bao
paper_authors._id: 68299e3128752b51372d3207
paper_authors.name: Kexin Yang
paper_authors._id: 68299e3128752b51372d3208
paper_authors.name: Le Yu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T09:04:46.635Z
paper_authors._id: 6455056fe4952d1c6cb45e49
paper_authors.avatarUrl: /avatars/730f61ebc4f2df7489499ad7471a426e.svg
paper_authors.fullname: Le Yu
paper_authors.type: user
paper_authors.user: vanillaOVO
paper_authors._id: 68299e3128752b51372d3209
paper_authors.name: Lianghao Deng
paper_authors._id: 68299e3128752b51372d320a
paper_authors.name: Mei Li
paper_authors._id: 68299e3128752b51372d320b
paper_authors.name: Mingfeng Xue
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T07:21:56.048Z
paper_authors._id: 5f8946925d083370c711f296
paper_authors.avatarUrl: /avatars/14246aae3b1f8b7ad050f8ff2c8b260e.svg
paper_authors.fullname: Mingfeng Xue
paper_authors.type: user
paper_authors.user: mingfengxue
paper_authors._id: 68299e3128752b51372d320c
paper_authors.name: Mingze Li
paper_authors._id: 68299e3128752b51372d320d
paper_authors.name: Pei Zhang
paper_authors._id: 68299e3128752b51372d320e
paper_authors.name: Peng Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-19T06:43:02.813Z
paper_authors._id: 62f220ccee7d7af44979efc7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62f220ccee7d7af44979efc7/RImNglMumGCpAKB5gin6k.jpeg
paper_authors.fullname: Peng Wang
paper_authors.type: user
paper_authors.user: ZJUPeng
paper_authors._id: 68299e3128752b51372d320f
paper_authors.name: Qin Zhu
paper_authors._id: 68299e3128752b51372d3210
paper_authors.name: Rui Men
paper_authors._id: 68299e3128752b51372d3211
paper_authors.name: Ruize Gao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T07:21:46.295Z
paper_authors._id: 6629ed94aabce1b25c3db90c
paper_authors.avatarUrl: /avatars/cbc39db81c8e8f950d3bd2c2e03f71c8.svg
paper_authors.fullname: Ruize Gao
paper_authors.type: user
paper_authors.user: gaoruize
paper_authors._id: 68299e3128752b51372d3212
paper_authors.name: Shixuan Liu
paper_authors._id: 68299e3128752b51372d3213
paper_authors.name: Shuang Luo
paper_authors._id: 68299e3128752b51372d3214
paper_authors.name: Tianhao Li
paper_authors._id: 68299e3128752b51372d3215
paper_authors.name: Tianyi Tang
paper_authors._id: 68299e3128752b51372d3216
paper_authors.name: Wenbiao Yin
paper_authors._id: 68299e3128752b51372d3217
paper_authors.name: Xingzhang Ren
paper_authors._id: 68299e3128752b51372d3218
paper_authors.name: Xinyu Wang
paper_authors._id: 68299e3128752b51372d3219
paper_authors.name: Xinyu Zhang
paper_authors._id: 68299e3128752b51372d321a
paper_authors.name: Xuancheng Ren
paper_authors._id: 68299e3128752b51372d321b
paper_authors.name: Yang Fan
paper_authors._id: 68299e3128752b51372d321c
paper_authors.name: Yang Su
paper_authors._id: 68299e3128752b51372d321d
paper_authors.name: Yichang Zhang
paper_authors._id: 68299e3128752b51372d321e
paper_authors.name: Yinger Zhang
paper_authors._id: 68299e3128752b51372d321f
paper_authors.name: Yu Wan
paper_authors._id: 68299e3128752b51372d3220
paper_authors.name: Yuqiong Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T07:20:06.363Z
paper_authors._id: 666aacfb918ba11c7c598194
paper_authors.avatarUrl: /avatars/45bee8f1fdbdd256ee47d25e4bf01a7a.svg
paper_authors.fullname: Yuqiong Liu
paper_authors.type: user
paper_authors.user: lyq333
paper_authors._id: 68299e3128752b51372d3221
paper_authors.name: Zekun Wang
paper_authors._id: 68299e3128752b51372d3222
paper_authors.name: Zeyu Cui
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T07:19:43.843Z
paper_authors._id: 672c25ca8cfb61188128eb6f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/FJWy9Tt7UQmu9KcTOx3Rt.png
paper_authors.fullname: Zeyu Cui
paper_authors.type: user
paper_authors.user: misakamage
paper_authors._id: 68299e3128752b51372d3223
paper_authors.name: Zhenru Zhang
paper_authors._id: 68299e3128752b51372d3224
paper_authors.name: Zhipeng Zhou
paper_authors._id: 68299e3128752b51372d3225
paper_authors.name: Zihan Qiu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T07:18:58.545Z
paper_authors._id: 647ccbd6e07cf9bb2d485244
paper_authors.avatarUrl: /avatars/e8915abaff04f6762247e196b7cf84df.svg
paper_authors.fullname: Zihan Qiu
paper_authors.type: user
paper_authors.user: QwQZh
paper_title: Qwen3 Technical Report
paper_summary: 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.
paper_ai_summary: Qwen3, a unified series of large language models, integrates thinking and non-thinking modes, reduces computational resources, and achieves state-of-the-art performance across various tasks and languages.
paper_ai_keywords: large language models dense architecture Mixture-of-Expert thinking mode non-thinking mode thinking budget mechanism knowledge transfer multilingual capabilities code generation mathematical reasoning agent tasks cross-lingual understanding generation capabilities