Qwen

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  • 01
    Qwen-AgentWorld: Language World Models for General Agents
    Today we release Qwen-AgentWorld, a native language world model that simulates agent environments across seven domains: Native world modeling: environment modeling is the training objective from continual pre-training onward (CPT → SFT → RLQwenTeam
  • 02
    Qwen-Robot Suite: A Foundation Model Suite for Physical World Intelligence
    The Qwen family of foundation models already gives strong perception and reasoning about the physical world. But seeing is not acting: the gap between vision and language understanding and physical control remains the central bottleneck forQwenTeam
  • 03
    Qwen-RobotNav: A Scalable Navigation Model Designed for an Agentic Navigation System
    Agentic navigation systems require a base navigation model with a configurable navigation context protocol: instruction following, object search, target tracking, and autonomous driving share the same perception-planning backbone yet demandQwenTeam
  • 04
    Qwen-RobotWorld: Boundless Worlds for Embodied Agents
    Embodied intelligence requires agents to perceive, reason about, and act within physical environments. World models offer a scalable path forward — but current approaches face a fundamental tension. General video generation models learn ricQwenTeam
  • 05
    Qwen-RobotManip: Alignment Unlocks Scale for Robotic Manipulation Foundation Models
    Qwen-Omni × Qwen-RobotManip — Qwen-Omni observes the scene, randomly proposes manipulation tasks via speech, and judges execution in real time. Each video shows Qwen-RobotManip completing tasks on the fly with no pre-defined task list, demQwenTeam
  • 06
    Qwen3.7-Plus: Multimodal Agent Intelligence
    Today we introduce Qwen3.7-Plus — a multimodal agent model that unifies vision and language into a single, versatile agent foundation. Building on Qwen3.7's strong text backbone, Qwen3.7-Plus delivers a comprehensive upgrade in vision-languQwenTeam
  • 07
    Qwen-VLA: From Understanding the World to Acting in It
    Over the past few years, multimodal large language models have become increasingly capable of understanding images, videos, and real-world scenes. They can recognize objects, reason about spatial relationships, answer visual questions, andQwenTeam
  • 08
    Qwen3.7: The Agent Frontier
    Today we introduce Qwen3.7-Max, our latest proprietary model designed for the agent era. Qwen3.7-Max is built to be a versatile agent foundation — equally capable of writing and debugging code, automating office workflows, and sustaining auQwenTeam
  • 09
    Qwen3.5-LiveTranslate: From Sound to Sight, From Word to Right
    Qwen3.5-LiveTranslate-Flash is the latest simultaneous interpretation model in the Qwen family, built on top of Qwen3.5-Omni. It delivers real-time, multimodal translation that not only hears and translates speech, but also sees and understQwenTeam
  • 10
    Qwen-Scope: Decoding Intelligence, Unleashing Potential
    Interpretability research has emerged as a critical area for understanding LLM behaviors, informing performance optimization, and enabling more controllable model outputs. Today, we are excited to introduce Qwen-Scope, an interpretability tQwenTeam
  • 11
    FlashQLA: CP-/Bwd-Friendly Fused Linear Attention Kernels for GDN
    Following the release of Qwen3-Next, Gated Delta Network (GDN) has become the workhorse attention layer across the Qwen family — from Qwen3-Next-80B-A3B all the way to the subsequent Qwen3.5 / Qwen3.6 series. As models scale to 397A17B / 12QwenTeam
  • 12
    Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model
    Following the launch of Qwen3.6-Plus and Qwen3.6-35B-A3B, we are excited to open-source Qwen3.6-27B — a dense 27-billion-parameter multimodal model at the scale the community has been asking for most. Still supporting both multimodal thinkiQwenTeam
  • 13
    Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolving
    Following the release of Qwen3.6-Plus, we are sharing an early preview of our next proprietary model: Qwen3.6-Max-Preview. Compared to Qwen3.6-Plus, this preview release brings stronger world knowledge and instruction following, along withQwenTeam
  • 14
    Qwen3.6-35B-A3B: Agentic Coding Power, Now Open to All
    Following the launch of Qwen3.6-Plus, we are excited to open-source Qwen3.6-35B-A3B — a sparse yet remarkably capable mixture-of-experts (MoE) model with 35 billion total parameters and only 3 billion active parameters. Despite its efficienQwenTeam
  • 15
    Qwen3.6-Plus: Towards Real World Agents
    Following the release of the Qwen3.5 series in February, we are thrilled to announce the official launch of Qwen3.6-Plus. Available immediately via our API, this release represents a massive capability upgrade over its predecessor. Most notQwenTeam
  • 16
    Qwen3.5-Omni: Scaling Up, Toward Native Omni-Modal AGI
    Qwen3.5-Omni is Qwen’s latest generation of fully omnimodal LLM, supporting the understanding of text, images, audio, and audio-visual content. Both the Thinker and Talker in Qwen3.5-Omni adopt the Hybrid-Attention MoE. Qwen3.5-Omni seriesQwenTeam
  • 17
    Qwen3.5-Max-Preview Now Available on Arena
    We are pleased to announce the deployment of Qwen3.5-Max-Preview on Arena, where it has demonstrated exceptional performance during the preliminary evaluations. As we proceed with final optimizations ahead of the release within the next twoQwenTeam
  • 18
    Qwen3.5: Towards Native Multimodal Agents
    We are delighted to announce the official release of Qwen3.5, introducing the open-weight of the first model in the Qwen3.5 series, namely Qwen3.5-397B-A17B. As a native vision-language model, Qwen3.5-397B-A17B demonstrates outstanding resuQwenTeam
  • 19
    Qwen-Image-2.0: Professional infographics, exquisite photorealism
    We are launching Qwen-Image-2.0, a next-generation foundational image generation model. The key highlights of Qwen-Image-2.0 include: Professional Typography Rendering: Supports 1k-token instructions for direct generation of professional inQwenTeam
  • 20
    Qwen3-Coder-Next: Pushing Small Hybrid Models on Agentic Coding
    --- We introduce Qwen3-Coder-Next, an open-weight language model designed specifically for coding agents and local development. Built on top of Qwen3-Next-80B-A3B-Base, which adopts a novel architecture with hybrid attention and MoE, Qwen3-QwenTeam
  • 21
    Qwen3-ASR & Qwen3-ForcedAligner is Now Open Sourced: Robust, Streaming and Multilingual!
    Qwen3-ASR family includes two powerful all-in-one speech recognition models and a novel non-autoregressive speech forced alignment model. Qwen3-ASR-1.7B and Qwen3-ASR-0.6B are ASR models that support language identifiQwenTeam
  • 22
    Pushing Qwen3-Max-Thinking Beyond its Limits
    We present Qwen3-Max-Thinking, our latest flagship reasoning model. By scaling up model parameters and leveraging substantial computational resources for reinforcement learning, Qwen3-Max-Thinking achieves significant performance improvemenQwenTeam
  • 23
    Qwen3-TTS Family is Now Open Sourced: Voice Design, Clone, and Generation!
    Qwen3-TTS is a series of powerful speech generation capabilities developed by Qwen, offering comprehensive support for voice clone, voice design, ultra-high-quality human-like speech generation, and natural language-QwenTeam
  • 24
    Qwen3-VL-Embedding and Qwen3-VL-Reranker: For the Next Generation of Multimodal Retrieval
    In June 2025, we open-sourced the text-oriented Qwen3-Embedding and Qwen3-ReRanker model series, providing best-in-class performance across a variety of downstream tasks, including multilingual text retrieval, clustering, and classificationQwenTeam
  • 25
    Qwen-Image-2512: Finer Details, Greater Realism
    We are excited to introduce Qwen-Image-2512, the December update of Qwen-Image’s text-to-image foundational model. You are welcome to try the latest model at Qwen Chat. Compared to the base Qwen-Image model released in August, Qwen-Image-25QwenTeam
  • 26
    Qwen-Image-Edit-2511: Improve Consistency
    We are excited to introduce Qwen-Image-Edit-2511, an enhanced version over Qwen-Image-Edit-2509, featuring multiple improvements—including notably better consistency. To try out the latest model, please visit Qwen Chat and select the ImageQwenTeam
  • 27
    Qwen3-TTS Steps Up: Voice Cloning and Voice Design!
    Qwen3-TTS family has launched two new models: the voice design model Qwen3-TTS-VD-Flash (accessible via the Qwen API) and the voice cloning model Qwen3-TTS-VC-Flash (accessible via the Qwen API). Key Features: Voice Design：Qwen3-TTS-VD-FlasQwenTeam
  • 28
    Qwen-Image-Layered: Layered Decomposition for Inherent Editablity
    Today, we are excited to introduce Qwen-Image-Layered, a model capable of decomposing an image into multiple RGBA layers. This layered representation unlocks inherent editability: each layer can be independently manipulated without affectinQwenTeam
  • 29
    Qwen3-Omni-Flash-2025-12-01：Hear You. See You. Follow Smarter!
    Qwen3-Omni is a next-generation native multimodal large model capable of seamlessly processing multiple input modalities—including text, images, audio, and video—and generating both text and natural-sounding speech outputs simultaneously viQwenTeam
  • 30
    SAPO: A Stable and Performant Reinforcement Learning Method for Training Large Language Models
    Reinforcement learning (RL) has become a core ingredient in advancing the reasoning capabilities of large language models (LLMs). Modern RL pipelines enable models to solve harder mathematical problems, write complex code, and reason over mQwenTeam
  • 31
    Qwen3-TTS Update! 49 Timbres + 10 Languages + 9 Dialects
    Qwen3-TTS-Flash is a flagship text-to-speech model that supports multi-timbre, multi-lingual, and multi-dialect speech synthesis. It aims to produce natural and expressive speech and is available via Qwen API. Major Improvements: Richer TimQwenTeam
  • 32
    Qwen DeepResearch: When Inspiration Becomes Its Own Reason
    Click here to experience the latest Qwen DeepResearch _How does inspiration die?_ It usually doesn’t die from “not being good enough”, but from being “too much trouble”. When a thought flashes, it’s still fragile and unverified. After a briQwenTeam
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  • 01
    Qwen3Guard: Real-time Safety for Your Token Stream
    Tech Report GitHub Hugging Face ModelScope DISCORD Introduction We are excited to introduce Qwen3Guard, the first safety guardrail model in the Qwen family. Built upon the powerful Qwen3 foundation models and fine-tuned specifically for safQwen Team
  • 02
    Qwen-Image-Edit: Image Editing with Higher Quality and Efficiency
    QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DISCORD We are excited to introduce Qwen-Image-Edit, the image editing version of Qwen-Image. Built upon our 20B Qwen-Image model, Qwen-Image-Edit successfully extends Qwen-Image’s unique text renderQwen Team
  • 03
    Qwen-Image: Crafting with Native Text Rendering
    GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD We are thrilled to release Qwen-Image, a 20B MMDiT image foundation model that achieves significant advances in complex text rendering and precise image editing. To try the latest model, feel freeQwen Team
  • 04
    GSPO: Towards Scalable Reinforcement Learning for Language Models
    PAPER DISCORD Introduction Reinforcement Learning (RL) has emerged as a pivotal paradigm for scaling language models and enhancing their deep reasoning and problem-solving capabilities. To scale RL, the foremost prerequisite is maintainingQwen Team
  • 05
    Qwen-MT: Where Speed Meets Smart Translation
    DEMO API DISCORD Introduction Here we introduce the latest update of Qwen-MT (qwen-mt-turbo) via Qwen API. This update builds upon the powerful Qwen3, leveraging trillions multilingual and translation tokens to comprehensively enhance the mQwen Team
  • 06
    Qwen3-Coder: Agentic Coding in the World
    GITHUB HUGGING FACE MODELSCOPE DISCORD Today, we’re announcing Qwen3-Coder, our most agentic code model to date. Qwen3-Coder is available in multiple sizes, but we’re excited to introduce its most powerful variant first: Qwen3-Coder-480B-A3Qwen Team
  • 07
    Time to Speak Some Dialects, Qwen-TTS!
    API DISCORD Introduction Here we introduce the latest update of Qwen-TTS (qwen-tts-latest or qwen-tts-2025-05-22) through Qwen API . Trained on a large-scale dataset encompassing over millions of hours of speech, Qwen-TTS achieves human-levQwen Team
  • 08
    Qwen VLo: From "Understanding" the World to "Depicting" It
    QWEN CHAT DISCORD Introduction The evolution of multimodal large models is continually pushing the boundaries of what we believe technology can achieve. From the initial QwenVL to the latest Qwen2.5 VL, we have made progress in enhancing thQwen Team
  • 09
    Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
    GITHUB HUGGING FACE MODELSCOPE DISCORD We release Qwen3 Embedding series, a new proprietary model of the Qwen model family. These models are specifically designed for text embedding, retrieval, and reranking tasks, built on the Qwen3 foundaQwen Team
  • 10
    Qwen3: Think Deeper, Act Faster
    QWEN CHAT GitHub Hugging Face ModelScope Kaggle DEMO DISCORD Introduction Today, we are excited to announce the release of Qwen3, the latest addition to the Qwen family of large language models. Our flagship model, Qwen3-235B-A22B, achievesQwen Team
  • 11
    QVQ-Max: Think with Evidence
    QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DISCORD Introduction Last December, we launched QVQ-72B-Preview as an exploratory model, but it had many issues. Today, we are officially releasing the first version of QVQ-Max, our visual reasoningQwen Team
  • 12
    Qwen2.5 Omni: See, Hear, Talk, Write, Do It All!
    QWEN CHAT HUGGING FACE MODELSCOPE DASHSCOPE GITHUB PAPER DEMO DISCORD We release Qwen2.5-Omni, the new flagship end-to-end multimodal model in the Qwen series. Designed for comprehensive multimodal perception, it seamlessly processes diversQwen Team
  • 13
    Qwen2.5-VL-32B: Smarter and Lighter
    QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DISCORD Introduction At the end of January this year, we launched the Qwen2.5-VL series of models, which received widespread attention and positive feedback from the community. Building on the Qwen2.Qwen Team
  • 14
    QwQ-32B: Embracing the Power of Reinforcement Learning
    QWEN CHAT Hugging Face ModelScope DEMO DISCORD Scaling Reinforcement Learning (RL) has the potential to enhance model performance beyond conventional pretraining and post-training methods. Recent studies have demonstrated that RL can signifQwen Team
  • 15
    <think>...</think> QwQ-Max-Preview
    QWEN CHAT DISCORD This is a blog created by QwQ-Max-Preview. We hope you enjoy it! Introduction <think> Okay, the user wants me to create a title and introduction for their blog announcing the release of QwQ-Max-Preview. Let me start by undQwen Team
  • 16
    Qwen2.5-Max: Exploring the Intelligence of Large-scale MoE Model
    QWEN CHAT API DEMO DISCORD It is widely recognized that continuously scaling both data size and model size can lead to significant improvements in model intelligence. However, the research and industry community has limited experience in efQwen Team
  • 17
    Qwen2.5-1M: Deploy Your Own Qwen with Context Length up to 1M Tokens
    Tech Report HuggingFace ModelScope Qwen Chat HuggingFace Demo ModelScope Demo DISCORD Introduction Two months after upgrading Qwen2.5-Turbo to support context length up to one million tokens, we are back with the open-source Qwen2.5-1M modeQwen Team
  • 18
    Qwen2.5 VL! Qwen2.5 VL! Qwen2.5 VL!
    QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DISCORD We release Qwen2.5-VL, the new flagship vision-language model of Qwen and also a significant leap from the previous Qwen2-VL. To try the latest model, feel free to visit Qwen Chat and chooseQwen Team
  • 19
    Global-batch load balance almost free lunch to improve your MoE LLM training
    GITHUB HUGGING FACE MODELSCOPE DISCORD Background The Mixture-of-Experts (MoEs) architecture has become a popular model-parameter-scale-up technique. Typically, one MoE layer consists of a router (often parameterized as one single Linear laQwen Team
  • 20
    Towards Effective Process Supervision in Mathematical Reasoning
    GITHUB HUGGING FACE MODELSCOPE DISCORD Introduction In recent years, Large Language Models (LLMs) have made remarkable advances in mathematical reasoning, yet they can make mistakes, such as miscalculations or logical errors, leading to wroQwen Team
  • 21
    QVQ: To See the World with Wisdom
    GITHUB HUGGING FACE MODELSCOPE KAGGLE DEMO DISCORD Language and vision intertwine in the human mind, shaping how we perceive and understand the world around us. Our ability to reason is deeply rooted in both linguistic thought and visual meQwen Team
  • 22
    QwQ: Reflect Deeply on the Boundaries of the Unknown
    GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Note: This is the pronunciation of QwQ: /kwju:/ , similar to the word “quill”. What does it mean to think, to question, to understand? These are the deep waters that QwQ (Qwen with Questions) wadeQwen Team
  • 23
    Extending the Context Length to 1M Tokens!
    API Documentation (Chinese) HuggingFace Demo ModelScope Demo Introduction After the release of Qwen2.5, we heard the community’s demand for processing longer contexts. In recent months, we have made many optimizations for the model capabiliQwen Team
  • 24
    Qwen2.5-Coder Series: Powerful, Diverse, Practical.
    GITHUB HUGGING FACE MODELSCOPE KAGGLE DEMO DISCORD Introduction Today, we are excited to open source the “Powerful”, “Diverse”, and “Practical” Qwen2.5-Coder series, dedicated to continuously promoting the development of Open CodeLLMs. PoweQwen Team
  • 25
    Qwen2.5: A Party of Foundation Models!
    GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction In the past three months since Qwen2’s release, numerous developers have built new models on the Qwen2 language models, providing us with valuable feedback. During this period, we havQwen Team
  • 26
    Qwen2.5-LLM: Extending the boundary of LLMs
    GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction In this blog, we delve into the details of our latest Qwen2.5 series language models. We have developed a range of decoder-only dense models, with seven of them open-sourced, spanningQwen Team
  • 27
    Qwen2.5-Coder: Code More, Learn More!
    GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction In early April, we introduced CodeQwen1.5, which garnered significant attention from the community. Since then, we have been working to enhance the coding model. Today, we are excitedQwen Team
  • 28
    Qwen2.5-Math: The world's leading open-sourced mathematical LLMs
    GITHUB HUGGING FACE MODELSCOPE DISCORD 🚨 Qwen2.5-Math mainly supports solving English and Chinese math problems through CoT and TIR. We do not recommend using this series of models for other tasks. Introduction A month ago, we released theQwen Team
  • 29
    Qwen2-VL: To See the World More Clearly
    DEMO GITHUB HUGGING FACE MODELSCOPE API DISCORD After a year’s relentless efforts, today we are thrilled to release Qwen2-VL! Qwen2-VL is the latest version of the vision language models based on Qwen2 in the Qwen model familities. ComparedQwen Team
  • 30
    Qwen2-Audio: Chat with Your Voice!
    DEMO PAPER GITHUB HUGGING FACE MODELSCOPE DISCORD To achieve the objective of building an AGI system, the model should be capable of understanding information from different modalities. Thanks to the rapid development of large language modeQwen Team
  • 31
    Introducing Qwen2-Math
    GITHUB HUGGING FACE MODELSCOPE DISCORD 🚨 This model mainly supports English. We will release bilingual (English and Chinese) math models soon. Introduction Over the past year, we have dedicated significant effort to researching and enhancinQwen Team
  • 32
    Hello Qwen2
    GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction After months of efforts, we are pleased to announce the evolution from Qwen1.5 to Qwen2. This time, we bring to you: Pretrained and instruction-tuned models of 5 sizes, including QwenQwen Team
  • 33
    Generalizing an LLM from 8k to 1M Context using Qwen-Agent
    We’ve created an agent using Qwen2 models with an 8k context size to understand documents with 1M tokens, surpassing RAG and native long-context models. This agent was also used to generate data for training new long-context Qwen models.Qwen Team
  • 34
    Notes on Qwen-Max-0428
    API DEMO DISCORD Previously, we opensourced a series of Qwen1.5 model ranging from 0.5 to 110 billion parameters. Now, we release a larger model, Qwen-Max-0428. Qwen-Max-0428 is an instruction-tuned model for chat service. Very recently, itQwen Team
  • 35
    Qwen1.5-110B: The First 100B+ Model of the Qwen1.5 Series
    GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction Recently we have witnessed a burst of large-scale models with over 100 billion parameters in the opensource community. These models have demonstrated remarkable performance in both beQwen Team
  • 36
    Code with CodeQwen1.5
    GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction The advent of advanced programming tools, which harnesses the power of large language models (LLMs), has significantly enhanced programmer productivity and accuracy. Notwithstanding tQwen Team
  • 37
    Qwen1.5-32B: Fitting the Capstone of the Qwen1.5 Language Model Series
    GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction The open-source community has long sought a model that strikes an ideal balance between performance, efficiency, and memory footprint. Despite the emergence of cutting-edge models likQwen Team
  • 38
    Qwen1.5-MoE: Matching 7B Model Performance with 1/3 Activated Parameters
    GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction Since the surge in interest sparked by Mixtral, research on mixture-of-expert (MoE) models has gained significant momentum. Both researchers and practitioners are keenly interested inQwen Team
  • 39
    Introducing Qwen1.5
    GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction In recent months, our focus has been on developing a “good” model while optimizing the developer experience. As we progress towards Qwen1.5, the next iteration in our Qwen series, thiQwen Team
  • 40
    Introducing Qwen-VL
    Along with the rapid development of our large language model Qwen, we leveraged Qwen’s capabilities and unified multimodal pretraining to address the limitations of multimodal models in generalization, and we opensourced multimodal model QwQwen Team
  • 41
    Introducing Qwen
    4 months after our first release of Qwen-7B, which is the starting point of our opensource journey of large language models (LLM), we now provide an introduction to the Qwen series to give you a whole picture of our work as well as our objeQwen Team
  • 42
    OFASys: Enabling Multitask Learning with One Line of Code!
    Intro Generalist Models are hot! We all see an opportunity towards a real generalist model by multimodal multitask learning. We previously release an opensourced unified multimodal pretrained model OFA for this goal. However, we actually meQwen Team
  • 43
    Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese
    CLIP1 is a phenomenal playmaker in vision and multimodal representation learning. It plays not only as a foundation model but also a bridge between vision and language. It has triggered a series of research in different fields, especially tQwen Team
  • 44
    OFA: Towards Building a One-For-All Model
    2022 is a year of generalist models! With the bloom of multimodal pretraining, especially the unified model, we have witnessed the opportunity to building a generalist model that is capable of processing tasks of different modalities or mulQwen Team
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