The Origins of Price Sensitivity in Large Language Models

Price sensitivity is where an LLM's numeracy becomes an economic preference: acting on a price means representing it, comparing it, and deciding how much it counts against everything else. Models differ in their price sensitivity, which shapes the purchase advice they give; little research has investigated how training produces those preferences. OLMo 2 7B's public checkpoints and training data let us follow representation, comparison and weighting through pretraining, supervised fine-tuning, and direct preference optimization (DPO). Our central finding: accuracy in comparing prices does not reveal how strongly price influences a choice. Internally, magnitude sits closer to a logarithmic scale than a linear one, the same shape the price response takes. By the earliest checkpoint probed, the model already distinguishes numbers of equal length, which digit count alone cannot. A lower-price preference develops during pretraining, alongside improving comparison accuracy and before preference optimization. The training text offers a plausible source: an estimated third of genuine detected prices in sampled web text occur in choice-resolving comparisons. In the plain prompt, annealing and supervised fine-tuning reduce price weight relative to decisiveness about non-price attributes. DPO partly reverses this decline; its effect is smaller in the chat template and in four other models we checked. Separating numerical knowledge from its influence on decisions gives a framework for auditing purchasing agents' economic preferences.