That's a narrow take. Non-deterministic doesn't mean random; workflows can be reasonably validated and consistent to some known degree.

I work for an education department that serves a chatbot for students, and model changes go through painstaking content safety reviews. I initially assumed it's just a bunch of bureaucratic paranoia. But every other model upgrade has a measurably different adherence to the existing system prompts about not talking to the kids about sex and drugs and mental health issues.

The user cares about the distribution of outputs. That distribution is structurally determined by the distribution of inputs (i.e. prompts), the weights of the model, and (these days) the dynamics of the harness guiding successive generations.

The only way to characterize whether a choice is 'right' is to characterize the output distribution (i.e. evals)! Changing the underlying weights necessarily invalidates whatever characterization may have been done. One may assert that one's harness regularizes outputs back toward the desirable distribution, or one may hope the different weights induce a sufficiently similar output distribution.

But no, one should not be completely agnostic to the choice of weights just because there's some nondeterminism.

They’re nondeterministic at a fine level, but can be “deterministic” at a more general level: e.g. you might know that one model will always return properly formatted json when asked. That might not be true of the replacement, even if it is in general “better” and cheaper.

Just the risk of such a thing means regression testing every time you update the model, and you want to be able to run that testing on your schedule rather than having it forced on you.

Models are not deterministic, but they do have a flavor. When that flavor changes it can change the nature of output in a way that is undesirable.

Sort of like shooting a rifle - where the bullets hit is (to some order of magnitude, no philosophizing please) non-deterministic, but different very similar rifles will group differently and need to be appropriately adjusted to hit anything.

Nope, strong disagree. The model is one small part of the process harness; behaviors are usually routable with expected propensities. Unexpected model changes avoiding change management messes with monitoring and observability thresholds. Stochastic controls are a real thing when you have your distributions defined; your workflows on a new model will throw that expected prior out the window.

Crossing the street and Russian roulette both have non-deterministic risks of injury. And yet I would be bothered to find out that that on my way to work, I was playing Russian roulette by surprise.

A dice roll is non-deterministic.

Replacing a six-sided die for an eight-sided die also keeps rolls non-deterministic.

That doesn't mean it's fine to just replace the dice mid-game.

In deterministic scenarios, this kind of switch can sometimes actually be safe, because we’ve used various engineering techniques to converge from non-deterministic behavior to deterministic decisions. On the other hand, in scenarios that are inherently non-deterministic, the impact of such a change is much harder to predict, so we need comprehensive evaluations to assess the extent of its impact.

These models have specific behavioral characteristics trained into them from reinforcement learning and prompts optimized for one aren't guaranteed to transfer to the new generation. Think if the difference between gpt 5.4 and 5.5 and then 5.5 to 5.6 for example. 5.5 was "better" than 5.4 for struggled more across compaction boundaries and needed much more precise instructions before 5.6 sol recovered some of 5.4's ergonomics. All from the same lab but each model was trained with specific behavioral patterns that were basically product decisions. I would be quite annoyed to find that a model provider was routing a promt optimized for one model to a different one, especially for a dumber/cheaper non frontier model that's not going to be as good at just figuring out what you meant

    > those things are not deterministic

Determinism was an explicit goal of DeepSeek-V4. From their paper: https://arxiv.org/html/2606.19348v1#S3.SS3

    > we implement end-to-end, bitwise batch-invariant, and deterministic kernels with minimal performance overhead

Of course, providers may not implement deterministic inference for various reasons, but it is possible.

It’s like being told one day that one of your teammates will be replaced tomorrow by another teammate who’s more capable (has a higher test score), regardless of how long you’ve already worked together and gotten used to each other.