Decision Guide

Choosing a Low-Cost AI API

The cheapest per-token rate rarely produces the cheapest system. This guide shows how to lower AI spend while protecting quality — and how to spot the false economies that quietly make a 'cheap' model expensive.

Define your quality bar first

"Low cost" is only meaningful relative to a quality target. Before comparing prices, write down what a good-enough answer looks like for your task and build a small evaluation set of real examples. Then the question becomes concrete: what is the cheapest model that clears this bar? — which is far more useful than "what is the cheapest model?"

Right-size the model

Smaller models can be several times cheaper per token than flagships and are often sufficient for well-defined work. Match the model to the task rather than defaulting to the most capable option:

  • Classification, extraction, routing, short replies: usually a small model's territory.
  • Drafting, summarisation, general assistance: often a mid-tier model.
  • Hard reasoning, code, high-stakes output: a flagship may be cheaper overall by reducing retries and human fixes.

Model routing: many systems get the best of both by sending easy requests to a cheap model and escalating only the hard ones to a more capable model.

Cost levers that preserve quality

  • Trim prompts: remove boilerplate and retrieve fewer, more relevant documents to cut input tokens on every call.
  • Cap output length: shorter responses cost less on the pricier output side and are often clearer.
  • Cache repeated content: static system prompts and reference text can qualify for cached-input discounts.
  • Use batch mode for anything that tolerates asynchronous results — it is commonly discounted.

False economies to avoid

  • Ignoring output length: a cheaper model that writes twice as much can cost more per task.
  • Retry loops: a weak model that fails and gets called again (or requires human correction) is rarely the cheapest.
  • Prompt padding: stuffing huge context "just in case" inflates every request on a cheap model too.
  • Chasing headline rates: compare cost-per-successful-outcome, not cost-per-token.

Worked scenario (illustrative)

Illustrative assumptions — not a quote: a ticket-tagging job runs 500,000 times/month with ~400 input tokens and ~40 output tokens each. A small model clears the accuracy bar on the eval set.

Here the workload is high-volume and simple, so the deciding factor is per-token rate on a small model plus a tight output cap — a flagship would add cost with no benefit. Contrast that with a legal-summary job where a small model misses nuance: the flagship's higher rate can be the cheaper path once you count rework. Test both against your own data, then price the winner in the AI API Cost Calculator.

Pricing and capabilities change. Provider rates, model line-ups, and features are updated by the providers over time. Any figures on this page are clearly labelled illustrative planning assumptions, not quotes. Official sources are checked on a recurring schedule. Unusual or structural changes are held for human review. See the Pricing Sources & Data Status page for verification dates, and always confirm current rates on the official provider pages below.