Recent advances in large language models (LLMs) have enabled strong reasoning capabilities through Chain-of-Thought (CoT) prompting, which elicits step-by-step problem solving, but often at the cost of excessive verbosity in intermediate outputs, leading to increased computational overhead.We propose Sketch-of-Thought (SoT), a prompting framework that integrates cognitively inspired reasoning paradigms with linguistic constraints to reduce token usage while preserving reasoning accuracy.SoT is designed as a flexible, modular approach and is instantiated with three paradigms-Conceptual Chaining, Chunked Symbolism, and Expert Lexicons-each tailored to distinct reasoning tasks and selected dynamically at test-time by a lightweight routing model.Across 18 reasoning datasets spanning multiple domains, languages, and modalities, SoT achieves token reductions of up to 84% with minimal accuracy loss.In tasks such as mathematical and multi-hop reasoning, it even improves accuracy while shortening outputs.