User Report Highlights Token Efficiency of Qwen 3.8 Max Preview in Multi-Agent Workflows
A hands-on comparison by a Reddit user reveals that Alibaba's Qwen 3.8 Max Preview is significantly more token-efficient and systematic in multi-agent setups than MiniMax M3 and GPT 5.6. The user noted that Qwen 3.8 Max Preview avoids unnecessary file reading and subagent spawning, resulting in a fraction of the input token consumption of its competitors. Token efficiency is crucial for reducing operational costs in agentic workflows, where multiple LLMs continuously interact and process large volumes of data. If these qualitative findings hold true, models like Qwen 3.8 Max could make complex multi-agent systems much more commercially viable. While MiniMax M3 and GPT 5.6 models reportedly over-analyzed tasks and spawned excess subagents, Qwen 3.8 Max Preview executed sequential tasks with high precision. However, a notable limitation is that Qwen 3.8 Max Preview strictly filters and blocks outputs if vulgar language is used in the prompts.
## BACKGROUND
Multi-agent LLM systems deploy multiple specialized AI agents to collaborate on complex tasks, but they often suffer from high token consumption due to constant inter-agent communication. Qwen is a large language model family developed by Alibaba Cloud, while MiniMax M3 is a native multimodal model featuring a 1-million token context window.