Real-world benchmark data from a Reddit user on an M5 Max laptop is puncturing a key illusion about locally deploying open-source large models: the so-called "deep thinking" mode consumes 5.5x more tokens and takes 6x longer to infer than normal mode.
What This Is
The story originates with user u/DerTomsn on r/LocalLLaMA, who ran benchmarks on a MacBook Pro M5 Max (Apple's 2025 flagship laptop chip) using oMLX (a local inference framework optimized for Apple Silicon). The model is Qwen3.8-27B from the Tongyi Qianwen team; the test compared the model's "thinking mode" (xhigh tier, which forces the model to emit a long chain of reasoning before delivering its final answer) turned on versus off.
The numbers are blunt: with thinking mode on, token consumption jumps 5.5x and inference time jumps 6x. Turn it off? Output quality drops noticeably, sliding behind the same-generation MoE model (MoE = Mixture of Experts, an architecture that activates only a subset of parameters for faster inference) Qwen3.6-35B-A3B. In other words: for this 27B-class model, "thinking" is its core competitive edge—but the price tag is a 5–6x compute bill.
Industry View
This is worth examining at the intersection of two trends. First, reasoning models (i.e., models that perform long chain-of-thought before delivering a final answer) have become industry standard since OpenAI o1, with DeepSeek R1, Tongyi Qwen3, Wenxin X1, and Claude all adding similar mechanisms. Second, local deployment (self-hosting, where enterprises run models on their own servers) has long been pitched as the optimal path to cutting AI costs and protecting data privacy.
But this benchmark punctures a layer of that narrative: the premise of "local deployment saves money" is that the model doesn't think. Once users actually engage "deep thinking," the token cost curve isn't a linear climb—it's a 5x step function. For enterprise IT budget planners, this is a seriously underestimated hidden cost.
Counterpoints deserve recording. An infrastructure engineer in our reader community pointed out: under API mode (cloud-based calling), even with per-token billing, a single thinking-mode call still costs far less than the electricity bill and depreciation of a self-built GPU cluster—"local deployment saves money" doesn't hold up in most scenarios. What truly fits self-hosting are lightweight tasks that don't require deep reasoning; if enterprises insist on bringing reasoning capabilities on-prem, they need to redo the math.
Impact on Regular People
For enterprise IT: When evaluating self-built AI clusters, the 5–6x compute overhead from a model's "thinking" features must be folded into TCO (Total Cost of Ownership, the full-lifecycle spend from hardware procurement to electricity and maintenance), or actual bills will deviate sharply from budgets.
For individual professionals: Today's consumer laptops like the M5 Max can indeed run 27B-class models locally, but once you switch on "deep thinking," the machine runs visibly hot and fans roar—treat it as a productivity tool, not a demo toy. Adjust your expectations accordingly.
For the consumer market: AI product pricing will further fragment ahead. "Standard" and "deep thinking" editions may split into two subscription tiers, with price gaps potentially far exceeding today's typical Pro/Plus structure.