Model size vs. reasoning tokens: is there a conversion rate?

You can increase the amount an LLM thinks in two axes: by increasing the size of the model, or by increasing the number of tokens it outputs. Can we quantify the mathematical equivalence between the two? Given an architecture (depth, embed dim, param count), what's the equation that establishes the relationship between model size and reasoning effort (test-time compute)?