So I got access to a bunch of AI models through a single subscription and I’m kind of paralyzed. There’s GPT-4, Claude, Deepseek, and a ton of others. They all seem like they could work for what I’m building. How do I actually pick one without just guessing?
I started by testing a few models on sample tasks from my automation workflow. Like, I ran the same data transformation through different models and compared speed, accuracy, and cost per request. Turns out some models are way faster but less accurate, others are slower but more reliable.
For JavaScript code analysis in my workflows, I found that Claude handles edge cases better, but GPT-4 is faster for straightforward parsing. Different models really do have different strengths.
The game changer was testing within the actual workflow context. Running models against your real data instead of generic tests showed me which ones actually worked best for my use case. A model that looks great in benchmarks might not be ideal for your specific problem.
Are you testing models on your actual workflow data or using other criteria to pick?
Latenode lets you swap models without rebuilding anything. I test different models on the same workflow step and compare results side by side. You can A/B test them on real data before committing to one.
What works for us is starting with the cheaper model and if the output quality isn’t good enough, upgrade to a premium one. The single subscription means you’re not paying individual API costs, so switching models is just a configuration change.
I’ve found that different parts of my workflow use different models. One agent uses Claude for reasoning, another uses a faster model for data parsing. You can mix and match in the same workflow.
My approach is looking at what the model was trained for. If you need code generation or analysis, some models are explicitly better at that. Reading the model documentation saves time instead of trial and error. Also consider latency—if your workflow has tight time constraints, response speed matters as much as accuracy.
Model selection depends heavily on task complexity. Simple classification tasks work fine with cheaper models. Complex reasoning or nuanced analysis needs premium models. We segment our workflow steps—use budget models for prep work, premium for critical logic. This balanced approach keeps costs reasonable while maintaining quality.
Start with the cheapest, measure output quality, upgrade if needed. Test on your actual data not benchmarks. Cheaper models often work fine for simpler tasks.