This is something I haven’t seen explained well, and it’s been bugging me. When platforms offer access to OpenAI, Claude, Deepseek, and dozens of other models all in one subscription, how do you actually make the choice for a RAG workflow?
I understand that different models have different strengths. Some are fast, some are accurate, some are cheap, some are good at reasoning. But how do you factor that into a RAG decision where you’re making TWO choices - one for retrieval and one for generation - and those choices interact with each other?
Like, if you use a fast model for retrieval, does that force you to use a smarter model for generation to compensate? Or can you use a capable model for both and be fine? How much does cost matter when you’re running this on a volume basis?
I’ve been thinking about this from first principles and it’s confusing. The retrieval step needs to be relevant - finding sources that actually contain useful information. The generation step needs to be coherent - turning those sources into clear answers. Those feel like different requirements that might call for different models.
Has anyone actually built RAG with this many model options available? How did you make the choice, and would you change it now that you’ve seen results?
I run three different RAG systems with different model combinations, and here’s what I’ve learned: retrieval quality matters more than retrieval model choice. You can use a smaller, cheaper model for retrieval as long as it understands context relevant to your domain. Generation needs the smarter model because that’s where mistakes show up in the user-facing answer.
My setup: Claude for generation because it handles nuance and source attribution well. A smaller model for retrieval because the job is just understanding whether a document matches a query - you don’t need reasoning depth for that. In one subscription with 400+ models available, I can experiment without worrying about per-API costs.
The real advantage is iteration. I tested three different retrieval models in one afternoon, watched how results changed, and picked the one that found the most relevant sources. Then I tested two generation models. The combination that worked best wasn’t the most expensive - it was the most reliable.
Latenode’s integration of 400+ models means you stop thinking about licensing separate APIs and start thinking about optimization. Use what works for your specific retrieval strategy and your specific output requirements. Test them side by side in the visual builder.
The decision comes down to what you’re optimizing for. If cost is primary, use smaller models for retrieval and a capable model for generation. If accuracy is primary, use strong models for both. If speed matters, you want models that respond quickly at both stages.
What changes your calculus is being able to test different combinations easily. I tried a fast, cheap model for retrieval paired with a stronger model for generation. Results were good. Then I tried a stronger model for both. Cost went up but accuracy improved marginally. I went left because the improvement wasn’t worth the cost increase.
You need to actually measure. Run some queries through your RAG system with different model combinations, check retrieval quality and generation quality separately, compare costs. The theoretically best models aren’t always the practically best models for your use case.
Having access to many models forces you to think clearly about what each stage of RAG actually requires. Retrieval needs semantic understanding and relevance judgment. Generation needs language quality, coherence, and source handling. These are different capabilities. Use the most appropriate model for each function rather than using the same powerful model everywhere. That approach typically costs less and performs better than trying to be uniform.