[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-test-time-compute::en":3,"gloss-cluster-test-time-compute::en":26,"gloss-next-test-time-compute::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"test-time-compute","core-ai","Test-Time Compute","Test-time compute (also called inference-time compute) is the idea that a model can produce better answers by spending more computation at the moment you ask a question, rather than only by being bigger or trained longer. Instead of writing the first answer that comes to mind, the model generates a longer chain of thought, samples several candidate answers and votes among them, or runs a search over possible solutions. This is the engine behind reasoning models. The striking finding is that a smaller model given a generous test-time-compute budget can match a much larger one on hard tasks — you buy capability with inference cost instead of model size. For SaaS builders, it is a lever: on a hard request you can raise the thinking budget for better quality, and on easy requests keep it minimal to save money and latency. The tradeoff is direct — more test-time compute means more tokens billed and slower responses — so tie it to task difficulty rather than applying it everywhere.","Test-time compute is buying better answers with more computation at inference — longer reasoning, multiple samples, self-checks — instead of a bigger model.",null,[11,14,17,20,23],{"slug":12,"name":13},"chain-of-thought-prompting","Chain-of-Thought Prompting",{"slug":15,"name":16},"inference","Inference",{"slug":18,"name":19},"reasoning-model","Reasoning Model",{"slug":21,"name":22},"scaling-laws","Scaling Laws",{"slug":24,"name":25},"self-consistency","Self-Consistency",[27,31,35,39,42,45,48,51,54,57,60,63],{"slug":28,"category":5,"name":29,"updated_at":30},"agentic","Agentic AI","2026-08-24T02:46:36+00:00",{"slug":32,"category":5,"name":33,"updated_at":34},"alignment-tax","Alignment Tax","2026-08-24T02:46:37+00:00",{"slug":36,"category":5,"name":37,"updated_at":38},"artificial-intelligence","Artificial Intelligence (AI)","2026-08-24T02:46:38+00:00",{"slug":40,"category":5,"name":41,"updated_at":30},"attention","Attention",{"slug":43,"category":5,"name":44,"updated_at":38},"beam-search","Beam Search",{"slug":46,"category":5,"name":47,"updated_at":34},"benchmark-contamination","Benchmark Contamination",{"slug":49,"category":5,"name":50,"updated_at":34},"catastrophic-forgetting","Catastrophic Forgetting",{"slug":52,"category":5,"name":53,"updated_at":38},"computer-vision","Computer Vision",{"slug":55,"category":5,"name":56,"updated_at":34},"constitutional-ai","Constitutional AI",{"slug":58,"category":5,"name":59,"updated_at":30},"context-window","Context Window",{"slug":61,"category":5,"name":62,"updated_at":38},"deep-learning","Deep Learning",{"slug":64,"category":5,"name":65,"updated_at":30},"diffusion-model","Diffusion Model"]