One thing that’s been bugging me about platforms that give you access to tons of AI models is the decision paralysis. When you have 400+ models available through one subscription, how do you actually choose?
I’m working on a workflow that involves:
- Scraping unstructured text from a website
- Parsing it to decide if it matches our criteria
- Extracting specific fields and standardizing them
- Generating a summary for our team
That’s potentially four different steps where I could use different models. Some people say “just use GPT-4 for everything” but that seems wasteful. Others talk about using Claude for certain things and local models for others.
I don’t have a deep understanding of what makes each model actually different in practice. I know the specs—token limits, training dates, specializations—but when you’re building an automation workflow, how do you actually decide?
Do you pick one model and stick with it to keep things simple? Do you experiment with different models for different steps? Is there a framework for actually thinking through this, or is it mostly trial and error?
Also, when you’re paying a subscription that covers all the models, is there any reason not to experiment with multiple ones?
I spent way too long overthinking this before I realized the answer is simpler than it looks. For most browser automation workflows, you don’t need model diversity. Pick one solid model and use it consistently.
Here’s why: your workflow isn’t doing cutting-edge reasoning. It’s parsing, deciding, extracting. A decent mid-tier model handles all of that fine. Claude works for most of my stuff because it’s reliable with structured extraction. I don’t switch models mid-workflow.
The only time I switch is when I’m genuinely uncertain about performance. Then I test two models on a sample of 20-30 records and see which one actually extracts more accurately. Takes an hour, gives you real data.
If you’re using a platform like Latenode where multiple models are included, yeah, experiment. But don’t do it on every step of your automation—just on the step where accuracy matters most.
The approach that actually worked for me was mapping each step to what the model needs to do. Text scraping? Any modern model is fine. Decision logic where accuracy matters? That’s where I test. Extraction? I use Claude because it’s better at structured output. Summary generation? Anything that’s decent will work.
I stopped trying to optimize every step and focused on the steps that actually affect quality. For parsing and deciding, I tested GPT-4 and Claude on real data from our clients. Claude was 5% more accurate. Cost difference was minimal with a subscription, so I use Claude for that step specifically.
The other steps use a faster model. It actually made my workflow cheaper and faster without sacrificing quality. The key is not overthinking it—test what matters, keep the rest simple.
When you have subscription access to multiple models, the practical approach is to standardize where you can and differentiate where it matters. Most automation workflows have one or two critical steps where model choice actually impacts results. Those are worth testing.
For your scenario: scraping and standardizing? Any model. The decision-making step where accuracy impacts your business? That’s where you test Claude versus GPT-4 or whatever else is available. Likely you’ll find one performs noticeably better on your specific data.
Generation is usually fine with a smaller model. The real value from having model choice is testing against your actual use case, not trying every model on every step. Pick one for the critical path, optimize it, then use faster models for the rest.