Alibaba Open-Sources Qwen-AgentWorld — a 'Language World Model' That Simulates 7 Agent Environments

Qwen (GitHub)·high signal

Qwen released Qwen-AgentWorld-35B-A3B (35B total / 3B active MoE, 256K context, Apache 2.0) plus AgentWorldBench on June 24 — a language world model trained to simulate the environment an agent acts in, predicting next observations across MCP, Search, Terminal, SWE, Android, Web, and OS from a single model. A flagship 397B-A17B variant scores 58.71 on AgentWorldBench, edging out GPT-5.4 (58.25). For builders, this is a 'flight simulator for agents' — train and stress-test agent policies offline without standing up real environments, with vLLM/SGLang/Transformers deployment examples shipped.