Qwen3.7-Max benchmark data reveals an overlooked issue: on Artificial Analysis's Intelligence Index evaluation, the model generated about 97 million output tokens, while the average for participating models was only 24 million. The source of this 4× gap is not content redundancy, but the Extended-Thinking mode — reasoning models first generate a complete internal reasoning chain before outputting the answer, which is valuable for complex tasks but a latency burden for simple Q&A.
Qwen3.7-Max scored 56.6, ranking fifth, ahead of Gemini 3.5 Flash's 55.3. But 56.6 still trails GPT-5.5's 60.2 and Claude Opus 4.7's 57.3. More interesting is the cost beyond the benchmark: generating 97M tokens means longer latency and higher inference consumption.
This points to a core issue: reasoning models are not a universal accelerator. For tasks like code debugging, multi-step planning, and long-document analysis, the model thinking longer really is valuable; but for quick Q&A, turning off thinking mode and using the non-reasoning version is often more efficient. Qwen3.7-Max's million-token context combined with reasoning capability gives agent tasks a bigger stage, but users need to first judge task complexity before enabling Extended-Thinking. Used right, it's an efficiency lever; used wrong, it's a latency amplifier.