Which model for fast, cheap, high-volume tasks

The decision

High-volume tasks need predictable output, low latency, and controlled cost. They also need humility about task difficulty. Classification, normalization, routing, short extraction, and canned rewrite tasks are good candidates. Ambiguous judgment calls are not.

Model fit

Use each provider’s fast or smaller model tier when the task has a tight schema and low consequence. OpenAI, Claude, Gemini, and Mistral all publish model families with different capability and cost positions, but exact prices and names change. Verify current pricing before making a production routing decision. If the task needs machine-readable output, start with structured output prompting.

Prompting pattern

Classify this support ticket.

Categories: billing, login, bug, cancellation, other.
Rules:
- Use billing only for charges, invoices, refunds, plans, or prices.
- Use login only for authentication, passwords, MFA, or account access.
- Use other when no category clearly applies.

Return only JSON with category and confidence.

What to avoid

• Using a cheap model for a task where a wrong answer creates real damage.
• Asking for long explanations in a high-volume path.
• Leaving output unconstrained when downstream code expects known fields.
• Forgetting to sample failed cases after deployment.