The amount of inference compute isn't baked-in at pretraining time, so there is no tradeoff. You train the strongest model, then offer different ways of doing inference with it. Expensive inference probably wasn't offered before OpenAI o1 because it didn't work well enough to expect even a minimal viable number of customers who are willing to pay the inference premium. Many inference setups have significant fixed costs, you need sufficient demand for price per request to settle. The plots show scaling across 2 orders of magnitude with no diminishing returns. Train-time compute is likely post-training, so it might still be much cheaper than pretraining, feasible to scale further if it doesn't crucially depend on the amount of human labeling. Test-time compute on one trace comes with a recommendation to cap reasoning tokens at 25K, so there might be 1-2 orders of magnitude more there with better context lengths. They are still not offering repeated sampling filtered by consensus or a reward model. If o1 proves sufficiently popular given its price, they might offer even more expensive options.