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Qwen Research · 实时热榜

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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, and QwenTeam
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 with QwenTeam
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 series QwenTeam
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 Image QwenTeam
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