Qwen3-Max-Thinking

Qwen3-Max-Thinking is designed for high-stakes cognitive work where deep, multi-step reasoning matters more than speed. The model leverages a scaled mixture-of-experts architecture to handle the heavy lifting of complex problem-solving, and its thinking mode allows it to strengthen reasoning steps before committing to an answer. One of its defining features is adaptive tool-use: rather than waiting for explicit user commands, it autonomously decides when to call a web search or code interpreter based on what the conversation demands, weaving those tools into its reasoning flow as needed.

The model's capabilities stem from large-scale reinforcement learning applied during training, a process that consumes substantial compute to cultivate self-correction and multi-round verification behaviors. It demonstrates particular strength on rigorous mathematical and technical reasoning tasks, achieving near-perfect scores on benchmarks like AIME 25 and HMMT when paired with test-time scaling and a code interpreter. Compared against leading models like GPT-5.2-Thinking and Claude-Opus-4.5 across nineteen benchmarks, it holds its own across factual knowledge, instruction following, alignment, and agent tasks. This makes it well-suited for technical problem-solving, research assistance, and applications where logically traceable answers are essential.