Reasoning Models Can Be Effective Without Thinking

Recent LLMs have significantly improved reasoning capabilities, primarily by including an explicit, lengthy Thinking process as part of generation. In this paper, we question whether this explicit thinking is necessary. Using the state-of-the-art DeepSeek-R1-Distill-Qwen, we find that bypassing the thinking process via simple prompting, denoted as NoThinking, can be surprisingly effective. When controlling for the number of tokens, NoThinking outperforms Thinking across a diverse set of seven challenging reasoning datasets—including mathematical problem solving, formal theorem proving, and coding—especially in low-budget settings, e.g., 51.3 vs. 28.9 on ACM 23 with 700 tokens. Notably, the performance of NoThinking becomes more competitive with pass@k as k increases. Building on this observation, we demonstrate that a parallel scaling approach that uses NoThinking to generate N outputs independently and aggregates them is highly effective. For aggregation, we use task-specific verifiers when available, or we apply simple best-of-N strategies such as confidence-based selection. Our method outperforms a range of baselines with similar latency using Thinking, and is comparable to Thinking with significantly longer latency (up to 9x).

Introduction. Recent large reasoning models, such as DeepSeek-R1 (DeepSeek-AI et al., 2025), OpenAI o1 (OpenAI, 2024), and others (Qwen Team, 2025; Muennighoff et al., 2025; Li et al., 2025a; Ye et al., 2025), have significantly improved performance by increasing inference-time compute (Snell et al., 2024; Brown et al., 2024). As shown in Figure 1, these models approach complex tasks by first generating long chains of thought as a search process with reflection, backtracking, and self-validation (“Thinking”, upper half of the blue box), followed by final solution steps and a final answer (“Final Solution”, lower half). These reasoning abilities are typically acquired through reinforcement learning with verified rewards or fine-tuning on distilled reasoning traces. These models are generally believed to benefit from this explicit reasoning process, which has led to the popular paradigm of inference-time compute scaling—enhancing reasoning at the cost of significantly increased token usage and latency.

Discussion / Conclusion. Large language models generating a lengthy “thinking” process before producing solutions have achieved strong results on reasoning tasks. In this paper, we question the necessity of this process by introducing NoThinking, a simple yet effective prompting approach that bypasses the explicit thinking process. We show that the same models, without long thinking chains, can perform comparably to or better than their Thinking counterparts in pass@k as k increases, while using significantly fewer tokens. Under comparable token budgets, NoThinking consistently outperforms the traditional thinking results at most values of k. Furthermore, we show that NoThinking can be combined with best-of-N selection methods to achieve better accuracy–latency tradeoffs than are possible with standard Thinking.