Comment by simonw

1 day ago

> all requests to the Pro model will be routed to V4.1 Flash and billed at Flash's price

If I'd carefully tested and optimized prompts against Pro I wouldn't be keen on this particular news. I feel like API model providers should lean towards not swapping out models on their paying customers, no matter how much "better" the new model is meant to be.

Seems to be lots of people in here worried about “if i had”, and nobody who actually has done this.

Anybody actually using deepseek in a production system affected by this want to share their experience?

  • Absolutely nobody commenting that has done that, it's just roleplay. Flash-0731 and Pro-0813 replaced previous models too, and the V4-preview models replaced V3.2 before that. You have your official API from a tiny cutting edge research lab that can only realistically host one model at a time, which you know from every model they released before, but they fully openly provide every model so if you want that infinite stability you can easily host it yourself for eternity. So those commenters want to pretend they require RHEL-like stability for their prompts with enterprise budgets, but somehow can't host those models, yet at the same time offload their entire RHEL-stability requirements to the research lab. Even Google and OpenAI retire models that were still relevant a year ago, but open models actually last forever.

I imagine they're doing this due to capacity issues or somesuch. They can always relaunch Pro later, meanwhile a little ricered benchmaxxing of their existing flash model provides a temporary cover story. They certainly aren't silly enough to think this won't impact existing Pro users </paranoia>

  • They could raise prices, if capacity is problem

    • They can't increase capacity by raising prices. Right now they calculate that they're at an optimal revenue curve. Simply reducing demand by increasing prices doesn't mean they get more money. The limit is in the ability to buy hardware.

Sure, but if a company decided to place a remote chinese hedge fund's API at the center of a critical business workflow, this is a lesson better learned sooner rather than later.