Model selectionGuide6 min

Choose an OpenAI model for an AI SaaS workflow

A practical selection framework for balancing quality, latency, cost, and tool-use requirements.

Real example

Example: use different models for extraction and reasoning

An AI SaaS product extracts invoice fields and also evaluates unusual payment disputes. One task is repetitive; the other requires reasoning over context.

Use a faster, cheaper model for structured extraction that is heavily validated by schema. Use a stronger model for dispute analysis where it must compare evidence and explain uncertainty.

The workflow controls cost without forcing every step onto the largest model.

Tutorial path

How to implement it

Step 01

Classify the task as extraction, classification, drafting, reasoning, tool orchestration, or voice.

Step 02

Measure acceptable latency and cost for the user-facing moment.

Step 03

Run representative examples against candidate models with the same schema and tools.

Step 04

Choose the cheapest model that passes quality and safety requirements.

Step 05

Keep model choice configurable for future testing.

Checklist

Ready when these are true

Task category defined

Latency budget known

Cost budget estimated

Representative evals run

Model is configurable

Field notes

What matters in practice

Model choice should be tied to workflow risk, not hype.

Use smaller or faster models where the task is bounded and validated by code.

Use stronger reasoning where the model must compare evidence, plan, or handle ambiguity.

Avoid these mistakes

Common failure modes

Do not choose a model based only on benchmark headlines.

Do not use the same model for every task by default.

Do not ignore latency at user-facing moments.

Practical tip

Model selection is a workflow decision. Evaluate the full path: quality, latency, validation, cost, and fallback.

Apply this to a build