Qwen-AgentWorld-35B-A3B: a 3B-active MoE trained to simulate MCP, terminal, SWE, Android, web and OS environments

Qwen just released Qwen-AgentWorld-35B-A3B — a 35B-parameter MoE with only ~3B active parameters per token.

The interesting part: this is not positioned as a standard chat/instruction model or a full autonomous agent. It is a language world model trained to predict what an environment would return after an agent takes an action.

It covers seven agent interaction domains:

MCP / tool calling

Search

Terminal

Software engineering

Android

Web

Operating-system GUI interactions

The intended use seems to be simulating the environment side of an agent loop: given the action history and a new tool/GUI action, predict the next observation/state. That could be useful for agent training, offline evaluation, synthetic trajectories, testing tool-use workflows, or building sandbox-like environments without constantly running the real tools.

huggingface link