You are not sure which model to pick. The benchmark tables are long. The prices change. Every provider claims to be the best at something. And you need to ship.
This is a reasonable place to be. Here is a framework that cuts through it.
Start with the task, not the model
Before comparing models, define what your application actually needs. Four questions:
Does it need to be fast? Real-time features require low latency. Background processing does not.
Does it need to understand long documents? Context window size is a hard constraint.
Does it need structured output? Function calling or JSON mode support is not optional if you parse output programmatically.
What does failure look like? For some applications, an occasional wrong answer is acceptable. For others, accuracy requirements change which models are viable.
Price is not the most important variable
The cheapest model that meets your quality requirements is the right model. But price difference between models is large enough that it is worth testing quality explicitly before choosing based on benchmarks alone.
Benchmark scores measure what benchmark designers thought to measure. Your task is probably not one of them. Test your actual inputs on two or three candidate models.
The practical selection process
- Define your hard constraints: latency, context size, structured output, EU data residency
- Filter to models that meet all constraints
- Test the top 3 on 20 real examples from your use case
- Choose the cheapest one that passes your quality bar
Start here: Write down your three hardest constraints before looking at any model. The list of viable options will be shorter than you expect, and the decision will be clearer.