LLMs Keep Thinking When Told Not To

Large Language Models (LLMs) increasingly ship with “thinking modes”, yet their counterpart, the “no-thinking” has received far less exploration. In this work, we revisit language model “no-thinking” behavior along the following efforts: **(a) Explore the definition of no-thinking.** Rather than relying on proxies like thinking-mode control, we split each response into pre-answer text and a final answer, scoring both answer-only compliance and the question–pre-answer relevance. **(b) Broad experimental coverage.** We evaluate multiple no-think controls (thinking-mode toggles, prompt instructions, *etc.*) across boolean, multiple-choice, and open-ended tasks, on open-source models with scaled variants, and proprietary models. With extensive experiments, we found that **(i)** Explicit no-think controls do not reliably eliminate visible reasoning-like content. Instead, models exhibit **(ii)** *“thinking inertia”*: residual content is systematically organized by the task's answer space, with boolean verification compressing most easily, multiple-choice tasks forming an intermediate regime, and open-ended tasks remaining the most resistant. **(iii)** We further identify what we call a no-thinking and performance trade-off in open-ended: stronger answer-only constraints on complex tasks either fail to suppress the intermediate payload or succeed by removing computation needed for accuracy. **(iv)** Finally, we show that compressibility depends strongly on the structural support provided by the question itself. Thus, no-think controls are not direct guarantees of no-thinking; they must be evaluated jointly through answer-only success, visible payload, and task accuracy. We hope our work moves LLMs closer to a human-like ability to stop thinking on demand.