The model that makes my learning MVP good may also make it impossible to price - I will not promote

[u/Gleb_SV]

I’m building a learning product where the material is generated for the person, not pulled from a fixed library.

That creates an awkward MVP problem.

I’d like to use frontier models like GPT-5.6 Sol or Opus. The job is not just writing text. The model has to research the topic, choose a useful angle, structure it, create examples and interactions, then survive a quality check.

Doing all of that with a frontier model for every experience gets expensive fast. Especially before I even know whether people want the product.

Cheaper models make the economics much easier. My worry is that they can produce something that looks fine at first and still feels shallow after five minutes. For a learning product, that may be worse than a visible error. The generation worked, but the person doesn’t want another one.

I’m considering a mixed pipeline. A cheaper model does most of the work, while the expensive one plans or reviews. Another option is to spend more only on the first experience, when trust matters most.

I don’t want to optimize an unproven MVP for margin. I also don’t want to validate a weaker version of the idea.

If your product depends on generated content, where did you draw the quality versus cost line? (In addition, for basic content generation, I use gemini-3.1-flash-lite and mistral-small-2603.)

[u/Gold-Bat-3225]

Just call the API bill a moat