Agent benchmarks over the past few years have mostly followed one pattern. A goal is handed to the model, and the model tries to reach it within a bounded number of turns, whether that means finding the treasure in a maze, producing a report, or fixing a piece of code. Perform...
We introduce Qwen-Drive-1.0, the first vision-language foundation model for autonomous driving that unifies 3D perception and visual question answering at the pretraining stage and further extends to motion planning, while keeping the pretrained VLM architecture entirely untou...
In this release we are opening the weights of Qwen3.8-Flash-Next, a multimodal MoE model that also serves as an early preview of the architecture used in Qwen4. It plays the same role that Qwen3-Next played for Qwen3.5: the hybrid Gated DeltaNet + Gated Attention design introd...
Today, we are officially releasing Qwen 3.8-Max, the most capable model in the Qwen family to date. This also marks the first time we will open-source the weights of a Qwen-Max-class model — the open weights will be released next week. Built upon the architectural foundation o...
We are launching Qwen-Image-3.0, the third-generation foundational image generation model in the Qwen-Image series. If the keyword for Qwen-Image-1.0 was "Precision", and the keywords for Qwen-Image-2.0 were "Precision, Variety, Completeness, Beauty, and Authenticity", then th...
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 → RL), not a post hoc adaptation on top o...
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 for embodied intelligence. The Qwen-Robo...
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, demonstrating open-ended instruction foll...
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 rich visual priors but lack the ability ...
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 demand fundamentally different context stra...
<style> / Page-level: make tables full-width up to 1100px and centered / table { width: 85% !important; max-width: 1100px; margin: 0 auto; } </style> Today we introduce Qwen3.7-Plus — a multimodal agent model that unifies vision and language into a single, versatile agent foun...
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 solve complex multimodal reasoning ta...
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 autonomous execution across hundreds or...
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 understands visual context to produce more a...
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 toolkit trained on the Qwen3 and Qwen3...
<style> .katex-display > .katex { font-size: 1.1em; } .katex { font-size: 1.1em; } table .katex { font-size: 1.1em; } </style> Following the release of Qwen3-Next, Gated Delta Network (GDN) has become the workhorse attention layer across the Qwen family — from Qwen3-Next-80B-A...
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 thinking and non-thinking modes, Qwen3.6-27...
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 significant agentic coding improvemen...
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 efficiency, Qwen3.6-35B-A3B delivers outstand...
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 notably, we have drastically enhanced th...