There are two additional aspects that are even more critical than the implementation details here:

- Typical LLM usage involves the accretion of context tokens from previous conversation turns. The likelihood that you will type prompt A twice but all of your previous context will be the same is low. You could reset the context, but accretion of context is often considered a feature of LLM interaction.

- Maybe more importantly, because the LLM abstraction is statistical, getting the correct output for e.g. "3 + 5 = ?" does not guarantee you will get the correct output for any other pair of numbers, even if all of the outputs are invariant and deterministic. So even if the individual prompt + output relationship is deterministic, the usefulness of the model output may "feel" nondeterministic between inputs, or have many of the same bad effects as nondeterminism. For the article's list of characteristics of deterministic systems, per-input determinism only solves "caching", and leaves "testing", "compliance", and "debuggability" largely unsolved.