[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-reasoning-model::en":3,"gloss-cluster-reasoning-model::en":26,"gloss-next-reasoning-model::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"reasoning-model","core-ai","Reasoning Model","A reasoning model is an LLM trained — usually with reinforcement learning on verifiable problems — to spend extra computation \"thinking\" before it answers. Instead of emitting the first response, it generates a chain of intermediate steps, checks its own work, and backtracks when a path fails. OpenAI's o-series, DeepSeek-R1, and Claude's extended-thinking modes are examples. Compared with standard chat models, reasoning models are markedly better at math, code, multi-step logic, and planning, but they cost more and respond slower because they emit far more tokens. For SaaS builders, the practical rule is to route only genuinely hard tasks — complex code generation, data analysis, agentic planning — to a reasoning model, and keep cheap, fast models for classification, extraction, and simple chat. Many APIs let you dial a \"thinking budget\" up or down per request, trading latency and cost for accuracy. Don't reach for a reasoning model reflexively: on simple extraction or formatting jobs it is just slower and pricier with no quality gain.","A reasoning model is an LLM trained to spend extra compute thinking before it answers — generating steps, checking itself, and backtracking when a path fails.",null,[11,14,17,20,23],{"slug":12,"name":13},"agentic","Agentic AI",{"slug":15,"name":16},"chain-of-thought-prompting","Chain-of-Thought Prompting",{"slug":18,"name":19},"llm","Large Language Model (LLM)",{"slug":21,"name":22},"model-router","Model Router",{"slug":24,"name":25},"test-time-compute","Test-Time Compute",[27,29,33,37,40,43,46,49,52,55,58,61],{"slug":12,"category":5,"name":13,"updated_at":28},"2026-08-24T02:46:36+00:00",{"slug":30,"category":5,"name":31,"updated_at":32},"alignment-tax","Alignment Tax","2026-08-24T02:46:37+00:00",{"slug":34,"category":5,"name":35,"updated_at":36},"artificial-intelligence","Artificial Intelligence (AI)","2026-08-24T02:46:38+00:00",{"slug":38,"category":5,"name":39,"updated_at":28},"attention","Attention",{"slug":41,"category":5,"name":42,"updated_at":36},"beam-search","Beam Search",{"slug":44,"category":5,"name":45,"updated_at":32},"benchmark-contamination","Benchmark Contamination",{"slug":47,"category":5,"name":48,"updated_at":32},"catastrophic-forgetting","Catastrophic Forgetting",{"slug":50,"category":5,"name":51,"updated_at":36},"computer-vision","Computer Vision",{"slug":53,"category":5,"name":54,"updated_at":32},"constitutional-ai","Constitutional AI",{"slug":56,"category":5,"name":57,"updated_at":28},"context-window","Context Window",{"slug":59,"category":5,"name":60,"updated_at":36},"deep-learning","Deep Learning",{"slug":62,"category":5,"name":63,"updated_at":28},"diffusion-model","Diffusion Model"]