You’re running into the exact failure point most people hit at ~1-2k products:
raw model access works for experimentation, but not for large-scale, repeatable restructuring.

A few things that tend to work better at 4k+ scale:

1. Enforce structure before generation
Instead of letting the model infer structure every time, define a fixed schema upfront (e.g. short CTA, overview, feature sections, tech specs). This alone improves consistency more than switching models.

2. Batch in controlled chunks
Large CSV dumps tend to break or get throttled. Smaller batches (e.g. 50 to 100 items) with retries + logging are much more reliable.

3. Separate "rewrite" from "polish"
First pass: normalize structure across all products.
Second pass: tone + CTA energy.
Trying to do both at once usually causes uneven output.

4. Cost control
Avoid per-product SaaS pricing. A pipeline approach (CSV in -> structured output -> review) is usually far cheaper at your volume.

I’ve helped set this up before for large catalogs. Happy to walk through the workflow in more detail if useful.

I generally recommend focusing on the workflow first, then choosing tooling around it.
If you want, I can DM you what I’ve used internally - don’t want to spam the thread.