Unverified50% confidenceFactExact time
In the cost structure of large model companies, training costs occur before the model is released and are a one-time but enormous computing investment; inference costs, by contrast, occur continuously with user call volume, and the more users there are and the more frequent the calls, the greater the expenditure.
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10/16/2026
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UnverifiedReasoning models are extremely costly to train, typically requiring a combination of large-scale reinforcement learning and carefully designed reward models to acquire the capability.80% similarVerifiedInference costs are continuously rising relative to training costs, because training is a one-time cost while inference is executed repeatedly.80% similarUnverifiedModel training is a one-time investment, whereas inference is a continuous cost incurred with every user interaction.80% similarUnverifiedPausing large model training is costly, involving enormous compute expenditures and potentially affecting the pace of product iteration and competitive market position.79% similarUnverifiedIn production environments, inference costs are typically far higher than the amortized cumulative cost of training, especially in high-concurrency consumer-facing scenarios.79% similar
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