On June 24, 2026, the Qianwen Large Model made an official announcement, introducing its inaugural native language world model, Qwen-AgentWorld. This groundbreaking model possesses the capability to simulate agent interaction environments across seven key domains. These domains encompass both text-based (MCP, Search, Terminal, SWE) and GUI-based (Web, OS, Android) categories, thereby facilitating seamless cross-domain knowledge transfer. Environmental modeling has stood as a pivotal objective right from the continued pre-training phase, seamlessly integrated throughout the entire CPT (Continual Pre-Training), SFT (Supervised Fine-Tuning), and RL (Reinforcement Learning) processes. Accompanying this release is the AgentWorldBench evaluation benchmark, which comprehensively covers these seven domains. Each test sample within this benchmark is equipped with real-world environmental observation data, ensuring a high degree of authenticity and practicality. Qwen-AgentWorld is offered in two distinct scales: 35B-A3B and 397B-A17B, catering to diverse needs and applications. The model, along with its evaluation benchmark and accompanying code, has been made openly accessible on Hugging Face, ModelScope, and GitHub, fostering a collaborative and innovative ecosystem for researchers and developers alike.