Choosing an LLM in 2026: Stop Asking Which Is Biggest

There's always a new model at the top of some leaderboard, and it barely matters. Public benchmarks tell you how a model does on someone else's tasks. What you actually need to know is how it does on yours — at a price and latency you can live with.

Think in tiers, not champions

Most real workloads split cleanly: a lot of cheap, easy calls (classify this, extract that, summarise the other) and a few genuinely hard ones (reason across these ten documents). Send the easy ones to a fast, cheap model and reserve the frontier model for the hard tail. Routing like this often cuts cost by an order of magnitude with no visible quality drop.

Keep the provider swappable

Wrap model calls behind a thin interface so switching providers is a config change, not a rewrite. The landscape moves monthly; the team that can A/B a new model in an afternoon wins, and the team that hard-coded one vendor's SDK everywhere does not.

Let evals decide

Pick the model with data, not vibes. Run your candidate models through the same eval set and choose the smallest one that clears the bar. Bigger is not a strategy — 'passes our tests, fast enough, cheap enough' is.