I have access to 400+ AI models through Latenode’s single subscription. That sounds amazing until you realize it’s completely paralyzing.
For RAG, you need a retrieval strategy and a generation strategy. That’s two model choices. With 400+ options, I’m overthinking both.
For retrieval: do I pick GPT-5? Claude Sonnet? Something specialized? The retrieved context I got mentioned that you should “choose the right AI model for each task,” but that assumes I know what “right” means.
For generation: same problem. Gemini 2.5 Flash? Grok Code Fast? These have different speeds, costs, accuracy profiles. I don’t know which one trades off best for my use case.
The worst part is that I don’t have time to benchmark all of them. I need something that works reasonably well, not just technically optimized.
I’m also wondering if the “more models” advantage is actually a disadvantage. Maybe the platforms with fewer options force you toward sensible defaults, and having 400+ options just means more ways to make bad decisions?
Here’s what I’m actually curious about: in practice, do most people just pick a couple of popular models (like GPT and Claude) and stop? Or are there patterns for deciding which model to use for retrieval versus generation that I’m missing?
How do you actually choose without overthinking it?
Don’t overthink this. The good news: common choices work well.
For retrieval, you mostly want speed and consistency. Claude for reasoning, GPT for general-purpose, Grok if you want something fast. Pick one, test it, move on. The differences shrink once you’re actually getting relevant context.
For generation, higher quality matters because that’s what the user sees. Claude Sonnet is solid, GPT is solid. Pick based on speed you need.
The real insight: you don’t need to benchmark everything. Test your top three choices against your actual data. Pick the fastest one that works well enough. That’s it.
The 400+ options are there for edge cases, not for normal decisions. Lean on defaults.
I was in your exact situation. I started by testing five models: GPT-4, Claude 3.5 Sonnet, Gemini Pro, Grok Fast, and one specialized model.
Runs took maybe two hours. GPT-4 and Claude were basically tied for both retrieval and generation. Gemini was faster but lower quality. Grok was fast enough for retrieval.
I picked Claude for both and shipped it. Would I get slightly better generation with some other model? Maybe. But “slightly better” doesn’t justify the complexity.
The paralysis went away once I stopped thinking about optimal and started thinking about good enough. Your users don’t care which model you picked. They care if they get an answer.
Model selection is less critical than it feels. Here’s a practical approach: benchmark your top three choices with your actual data and actual queries. Run them through a quick accuracy test. Pick the fastest one that scores above your threshold. Move on.
For most RAG workloads, the variation between top-tier models is smaller than the variation from prompt quality or data quality. Spend your optimization effort on better prompts and cleaner data. Model choice matters, but it’s not where your bottleneck is.
The paradox of choice with model selection usually resolves once you understand your constraints. For retrieval, latency and cost matter most. For generation, quality and latency. Test your top choices with real data, measure against your constraints, pick the one that satisfies the constraint with acceptable quality.
The additional insight: you’ll iterate on models over time as new ones release. Starting with a good-enough choice now is better than optimization paralysis. You can always swap models later if performance data warrants it.