How to Reduce LLM Token Costs

8 practical ways to cut your AI API bill

Token costs add up fast at scale. Because you pay for every token sent and generated, small inefficiencies multiply across thousands of requests. Here are eight proven ways to spend less without hurting quality.

1. Right-size the model. Don't use GPT-4o for a task Gemini Flash or Claude Haiku can handle. Cheaper models are 10–40× less expensive — reserve premium models for genuinely hard reasoning. See the cost comparison.
2. Trim your prompts. Remove boilerplate, repeated instructions, and filler. A tighter system prompt sent on every request saves tokens on every request.
3. Cap output length. Output tokens cost 3–5× more than input. Set max_tokens and ask for concise answers ("reply in under 100 words").
4. Summarize long context. Instead of pasting an entire document, send a summary or only the relevant sections. Retrieval (RAG) beats stuffing everything into the prompt.
5. Prune chat history. In multi-turn apps, drop or summarize old messages rather than resending the whole conversation each turn.
6. Use prompt caching. OpenAI and Anthropic offer discounts for repeated prefixes (system prompts, instructions). Structure prompts so the stable part comes first.
7. Batch where possible. Some providers offer cheaper asynchronous batch pricing for non-urgent workloads.
8. Measure before you ship. Count tokens for your typical prompt and estimate the monthly bill before going live — surprises are expensive.

Measure first with a free tool

You can't optimize what you don't measure. Paste a representative prompt into our token counter to see the exact token count per model, then use the cost calculator to turn your request volume into a monthly estimate.

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