How to Choose an AI Model
AI pricing guide · updated 2026-08-07
Most model selection starts with benchmark tables and ends in a frontier model doing work a cheap one could handle. A more reliable order runs the other way: eliminate on hard constraints first, shortlist on price, and use benchmarks only to break a tie between options you have already costed.
First, rule out on hard constraints
Some requirements are binary and settle the question quickly. Does the model accept the modalities you need, images or audio or video? Is its context window larger than your biggest realistic request? Can it be deployed in the jurisdiction your data must stay in? Does it support the tool-calling or structured-output format your system depends on?
Each of these removes candidates without any judgement, which is why they belong first. A model that cannot read a PDF is not a cheap option for document processing; it is not an option.
Second, shortlist on price for your workload shape
Price the survivors against your own token mix rather than a single blended figure. Retrieval-shaped work weights the input rate; generation and agent loops weight the output rate. The catalogue spans roughly 1,600 times from cheapest to dearest, so this step moves more money than anything after it.
Take a handful of candidates spread across price tiers rather than only the affordable ones. The point of the next step is to find out where quality actually starts to matter, and that requires something to compare against.
Third, use benchmarks to break the tie
Benchmarks are useful and narrow. SWE-bench Verified indicates autonomous coding ability, GPQA Diamond hard reasoning, LMArena Elo general helpfulness as people perceive it. Check that any two scores you compare come from the identical benchmark and variant, because scores from different variants are not comparable and can invert a ranking.
Treat a score as one data point about one task rather than a capability rating. Latency, output formatting, instruction-following and rate limits all decide whether a model works in practice, and none appear in a score.
Fourth, verify on your own traffic
Run a real sample through the shortlist and compare outputs on the task you actually have. This is the step that gets skipped and the only one that answers the question, because it measures your workload rather than somebody else's.
What usually emerges is that most volume never needed the premium model. The pattern that follows is routing: a cheap model for the routine majority, a strong one reserved for the cases that genuinely need it.
Then revisit it
A choice made six months ago is probably no longer optimal. New models launch cheaper than the ones they replace, and rates move in both directions, so a periodic re-check is worth scheduling rather than leaving to chance.
Keep the model behind a configuration value so acting on that re-check is cheap. A decision you can reverse in an afternoon is one you can afford to make quickly.
Frequently asked questions
How do I choose which AI model to use?
Rule out candidates on hard constraints first (modalities, context window, deployment region, tool-calling support), shortlist the survivors on price for your specific token mix, use benchmarks only to break a tie, then verify on a real sample of your own traffic. That order puts the largest cost decision before the least reliable evidence.
Should I just use the best model available?
Rarely the best value. Classification, extraction, routing and straightforward summarisation are frequently indistinguishable between a frontier model and one many times cheaper. The common pattern is routing: a cheap model for most volume, a strong one for the cases that need it.
How often should I revisit my model choice?
Often enough that a cheaper option does not go unnoticed for long, and with the model kept behind a configuration value so switching is quick. New models frequently launch below the ones they replace, and existing rates move in both directions.
See it in the data
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Published by Tokenando. Last updated 2026-08-07. Figures in this guide are computed from our own pricing index and dated where they can move; see the methodology and corrections policy.