So I just started working with a platform that gives me access to like 400 different AI models, and honestly, it’s overwhelming. I know OpenAI’s GPT models are solid, Claude is great for reasoning, but then there’s Deepseek, Mistral, and all these others I’ve barely heard of.
The problem is, I don’t want to just default to the expensive models every time. I’ve got this automation that needs to analyze customer support tickets and categorize them. Some of these models are probably overkill for that task, but others might struggle to get it right.
How do you actually decide which model to use for a specific task? Do you test them first? Is there a mental framework that helps, or is it mostly trial and error? And when you’re building an automation that multiple AI agents might use, do you stick with one model or route different tasks to different ones based on what they’re good at?
This is one of the best parts of having 400 models available. You don’t have to commit to one.
What I do is think about the task type first. Classification tasks like your support tickets? Claude handles those well and doesn’t need the most expensive model. For creative work, GPT is solid. For reasoning-heavy stuff, Deepseek has been surprising me lately.
Latenode lets you route tasks to different models based on complexity. So simple categorization hits Claude, complex analysis hits GPT. Saves money and often gets better results because you’re using the right tool.
I’d recommend testing your ticket categorization with two or three models, measure accuracy, and go with what works best. You might surprise yourself—the cheaper option often wins.
I went through this same decision paralysis. What actually helped was categorizing tasks by complexity and speed requirements.
For simple categorization, I use lighter models—they’re fast and cheap. For anything requiring nuanced understanding or multi-step reasoning, I go heavier. Your ticket categorization probably doesn’t need reasoning power, just pattern matching.
I built a small test suite where I ran the same task through three models, measured accuracy and latency, then chose based on what mattered most. Took an afternoon but saved weeks of second-guessing.
The honest answer is you learn through experience. I started by defaulting to the “best” model for everything, then realized I was burning credits on overkill.
Now I route based on context. High-stakes decisions? Premium model. Bulk processing? Cheaper option. Speed-critical? Fast model. Your ticket categorization sounds like it could work with a mid-tier model fine.
When building automations across multiple AI agents, I’ve learned that matching model capability to task complexity matters significantly. Start by documenting what each task actually requires: simple classification, complex reasoning, creative generation, etc. Then profile a subset of your models against these requirements using real data. This approach revealed that my support categorization worked perfectly fine with smaller, faster models, while my analysis tasks benefited from larger models.