How to Cut Your OpenAI and Claude API Costs (Without Worse Output)

AI API costs rarely blow up in one dramatic moment. They creep — a slightly-too-long system prompt here, a full chat history resent on every turn there, a flagship model used for a job a cheaper one could do. Each request looks trivial. Multiply by your real traffic and the monthly bill tells a different story. The good news: almost every lever that lowers cost is something you control, and none of them require accepting worse output.

First, understand what you are paying for

Every API charges per token, and it charges separately for input (your prompt) and output (the reply). Output is almost always the pricier half — often three to five times the input rate — because generating text is the expensive part. So the two questions that drive your bill are: how many tokens am I sending, and how many am I asking the model to generate?

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The levers that actually move the bill

1. Trim the prompt. Cut boilerplate, repeated instructions, and stale context. A system prompt sent on every call is paid for on every call
2. Right-size the output. Do not request a 4,000-token reply when 400 will do. Cap max output tokens to what you actually need
3. Pick the smallest model that passes. GPT-4o mini and Claude Haiku cost a fraction of the flagships and handle most routine tasks well
4. Summarise history instead of resending it. In a long conversation, send a running summary rather than the full transcript each turn
5. Cache and reuse. If the same context is sent repeatedly, use prompt caching where the provider offers it

Notice that none of these are "use a worse model and hope". They are about not paying for tokens that add no value — the empty calories of an API bill.

Match the model to the job

A surprising share of production traffic is simple: classification, extraction, short rewrites, routing. These do not need a flagship. Send the easy work to a small, cheap model and reserve the expensive one for genuinely hard reasoning. A two-tier setup — cheap model first, escalate only when needed — often cuts cost dramatically while keeping quality where it matters.

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Verify the savings before you ship

Optimisation without measurement is guesswork. Before and after a change, count the tokens for a representative prompt and compare the estimated cost. Then multiply by your real request volume — a fraction of a cent saved per call becomes a serious number across a million calls. Seeing the two figures side by side is what turns "this feels leaner" into a decision you can defend.