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 workflow example

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.

Implementation approach

This guide is anchored in OpenAI models guide. Use the official API behavior as the boundary, then design the surrounding product state so the feature can be reviewed, retried, and improved.

Classify the task as extraction, classification, drafting, reasoning, tool orchestration, or voice.
Measure acceptable latency and cost for the user-facing moment.
Run representative examples against candidate models with the same schema and tools.
Choose the cheapest model that passes quality and safety requirements.
Keep model choice configurable for future testing.

Code or config snippet when useful

type choose_openai_model_ai_saas_workflow_workflow_state = {
  sourceId: string;
  status: "draft" | "needs_review" | "approved" | "blocked";
  evidence: Array<{ source: string; summary: string }>;
  nextAction: string;
};

Field notes

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.

Mistakes to avoid

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.

Ready checklist

Task category defined
Latency budget known
Cost budget estimated
Representative evals run
Model is configurable

Practical tip

Practical note

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

Use this as an implementation constraint, not just advice. The interface, server code, and validation path should make the same behavior true.

Apply this to a build