AI API selection

Choose an API by defining the work the application must complete. Use the wizard to create a documented shortlist, then verify its assumptions with representative tasks.

What matters for your API choice

Start with the task, not a provider preference. A coding workflow needs an accepted patch, a retrieval workflow needs relevant evidence and a speech feature needs usable audio. Write down the output that counts as success and the failure state the product should expose. This gives the selection criteria a purpose.

Separate must-have capabilities from preferences. Input type, required output form and a mandatory endpoint feature can eliminate a candidate. Cost sensitivity, convenience and a preference for lower latency can help order the remaining choices. Do not quietly relax a requirement just because the first filter produces an empty result.

Official model catalogs describe available capabilities and identifiers, while price references establish the charge categories used for planning. Official documentation.

Use the monthly budget band as a statement of sensitivity rather than a complete forecast. A monthly amount still depends on traffic, input, output and additional application stages. Keep those workload assumptions for the calculator after a shortlist exists.

Decide what evidence is needed for a fastest or strongest claim. A context value and a price are not performance benchmarks. Comparable latency or quality evidence must describe the same measurement conditions; otherwise treat the suggested candidate as something to evaluate rather than a demonstrated winner.

Ranked candidates

The initial table is a broad text-model price view. Use the wizard’s task and capability requirements to narrow it before evaluating output. Unknown fields cannot satisfy a strict requirement, and a model that is absent from the published reference should not be invented to fill a result slot.

Our three picks

The roles are cheapest, balanced and strongest for evaluation. Cheapest follows supported price evidence for the workload category. Balanced considers the documented fit and price sensitivity. A strongest label requires comparable strength evidence; when that evidence is absent, the tool should state the uncertainty and present a candidate for your own evaluation.

Inspect the reason and source beneath every result. Keep the exact model, host and unresolved fields with the shortlist. If the tool shows fewer distinct candidates than the display roles, preserve that scarcity rather than treating a repeated candidate as independent evidence.

When each pick is wrong

The cheapest candidate is wrong when it fails a mandatory task or needs enough repair work to undermine the apparent saving. The balanced candidate is wrong when the selected weighting does not match the application’s real priorities. The strongest candidate is wrong when the cited benchmark or available fit evidence does not represent your task.

A shortlist is also wrong when the input assumptions are wrong. Revisit the use case if a simple chat feature grows into a tool-using workflow, or if a text pipeline adds image input. Keep the selection criteria connected to the current application rather than the prototype it used to be.

Estimate cost

Use the cost calculator with the surviving candidates and the same workload assumptions. Include repair calls, optional features and any category the tool cannot price.

Run safe fixtures and retain accepted outputs, completion state and actual usage. Choose the final route only after the application evidence and account controls fit the operating plan.

Last verified 12 Sep 2026 · Source ↗

Frequently asked questions

Sources

• OpenAI model catalog ↗
• Claude models ↗
• Gemini models ↗
• OpenAI pricing ↗

Last verified 12 Sep 2026 · Source ↗