Yes and no. The models themselves are just a big pile of floating point numbers that represent a compression of the dataset they were trained on. The patterns in that dataset will absolutely dominate the output of the model even if you tweak the inference parameters. Try it. Ask it ten times to make list of 20-30 random words. Each time a new context. The alignment between each of those lists will be uncanny. Hell, you'll even see repeats within the list. Size of the model matters here with the small ones (especially quantized ones) having less patterns or bigger semantic gravity wells. But even the big boys will give you the same slop patterns that are mostly fixed. Unless you are specifically introducing more entropy into the prompt, you can mostly treat a fixed prompt as a function with a somewhat deterministic output (within a given bounds).

This means that the claims in the OP are simply not true. At least not without some caveats and specific work arounds to make it true