Why "the best model" is the wrong question
There's no single best model - just like in "AI vs. automation: what fits where?", the right question isn't "can it do that?" but "does it fit this specific task?". A model that's ideal for a complex one-off request can be the most expensive and slowest choice for a thousand simple requests a day.
Criterion 1: does the task need reasoning?
"Why some AI answers take longer" explains the difference: a reasoning model checks intermediate steps before answering - valuable for multi-step, error-prone tasks, but slower and more expensive per request. For simple, clearly scoped tasks (classification, a short summary), a fast standard model is usually the better choice.
Criterion 2: how high is the volume?
"Token economics: how AI costs actually arise" shows that at low volume, the price difference between models barely matters. At scale - hundreds of thousands of similar requests a day - the smallest model that reliably solves the task often wins, not the most capable one.
Criterion 3: how sensitive is the data, how much control do you need?
"Open source vs. proprietary AI models" settled the procurement question: run an open model yourself (full data sovereignty, more operational effort) or access it via a provider's API (less effort, data leaves your own house). With highly sensitive data, that trade-off often tips toward control, even when the operational effort is higher.
The three criteria combined
A concrete recommendation only emerges from the interplay: a simple, high-volume task with non-critical data argues for a small, fast model via API. A complex, infrequent task with non-critical data argues for a large reasoning model - the cost per request barely matters at low volume. Highly sensitive data often tips the choice toward a self-hosted, open model regardless of task and volume. That's why many companies don't run a single model, but several - matched to the task.
Why this matters for you as a decision-maker
Model choice isn't a one-off decision for the whole company - it's a question that comes up again for every use case. Going through all three criteria systematically, instead of reflexively reaching for the best-known or most expensive model, saves on both fronts: cost at high volume and control with sensitive data.