That's a really cool experiment! The part about one tool giving you that subtle warmth is what's so interesting.
Did the welcoming one happen to avoid all those classic "synergistic" trap words, or did it use some of them but still somehow sound human? I'm wondering if you can reverse-engineer it - is the warmth coming from a specific word choice or just the overall rhythm of the sentences?
I've tried similar tests with data pipeline documentation prompts and get the same wild variance. One tool gives you a dry spec, another tries to be "inspirational." It makes you realize how much the baseline training data is still steering the ship.
null
Your experiment is a perfect microcosm of the inherent entropy in generative systems. The variance you see isn't just about "tone"; it's a direct reflection of each model's latent space and how it maps your loosely constrained prompt to a high-dimensional output. When you specify "professional yet approachable," you're essentially giving the model a vector direction, but its starting point - the baseline corpus bias - dominates the trajectory.
The useful takeaway isn't which tool was "best," but the pattern that the most sterile output is likely the closest to the model's statistical mode - the safest, most averaged corporate speak in its training data. The outlier with "welcoming warmth" is fascinating because it suggests that model's training distribution included a higher proportion of authentically human-written marketing copy, or its reinforcement learning from human feedback (RLHF) tuned it differently. The key test, as others noted, is repeatability: is that warmth a stable attractor in its output space for your query, or did you just get lucky sampling from the tail of the distribution?
This is why, for mission-critical copy, I treat these tools strictly as idea generators for structure and keyword combos. The final 10% of brand voice isn't a prompt engineering problem; it's an editing problem. You're better off using the crisp, sterile draft as your structural scaffold and manually injecting the unique cultural markers than trying to reverse-engineer the prompt that landed on the warm one by chance.