I’ve started exploring platforms that give you access to tons of different AI models—OpenAI, Claude, various open-source ones, etc.—all under one subscription. That sounds useful in theory, but in practice, I’m spinning my wheels trying to figure out which model to use for different parts of my automation.
Like, if I’m building a workflow that needs to extract text from images, analyze sentiment in customer feedback, and generate personalized recommendations, do I use the same model for all three? Do I pick one that’s best at each specific task? How do I even benchmark different models without spending a week testing?
I’m also wondering about cost. Are some models significantly cheaper than others for the same quality? And if I pick the wrong model, does it actually matter, or are the differences negligible for most tasks?
How do other people handle this? Do you have a strategy for matching models to specific tasks, or do you just pick one and stick with it?
This is where having multiple models under one subscription really pays off. You don’t have to commit to one model upfront because you can experiment with different models for different steps.
For image text extraction, a vision-capable model like Claude or GPT-4 Vision makes sense. For sentiment analysis on feedback, a smaller, faster model like GPT-3.5 often works just as well and costs less. For personalized recommendations, you might want something with stronger reasoning like Claude.
The practical approach is to start with a good general-purpose model, test it on your use case, then optimize. If response time matters, swap in a faster model. If accuracy is critical, use a stronger model.
With Latenode, you can A/B test different models by running the same step with multiple models and seeing which produces better results. You’re not locked into a decision—you can switch models as you learn what works.
I went through this same decision paralysis. Here’s what I learned: for most text tasks, the smaller models are surprisingly good and way cheaper. I was using GPT-4 for everything, but when I tested GPT-3.5, it handled 80% of my use cases just fine.
So now my strategy is: start with a cheaper, faster model and only upgrade if it fails. For sentiment analysis, I use a smaller model. For complex reasoning or creative tasks, I use Claude. For image work, GPT-4 Vision makes sense.
Cost difference is significant too. Using the right-sized model for each task cuts my API costs in half compared to always using the strongest model.
The key is understanding what each model is optimized for. Vision models are best for image tasks. Language models with fine-tuning are better at specific domains. Reasoning models like Claude or o1 are better at multi-step logical tasks.
Start by categorizing your automation steps: which are image-related, which need reasoning, which are simple text classification? Then match models to categories. For most routine tasks, you don’t need the fanciest model. Save expensive models for where they actually add value.
Model selection should be driven by three factors: accuracy for the specific task, latency requirements, and cost. Set up simple test cases for each step and try two or three models. Measure accuracy and cost. This takes an hour per step but saves money and frustration later.
Also consider that model capabilities change. A cheaper model might have been upgraded recently and could now handle tasks it couldn’t before. Revisit your choices quarterly.
Test smaller models first, upgrade only if needed. Vision tasks need vision models. Complex reasoning needs Claude or stronger. Don’t assume bigger is better.