How to Stretch Your ChatGPT o3 & o3-Pro Credits: A Guide to Token-Efficient Prompts
- Patrick Law
- Aug 7
- 1 min read
Feeding long prompts or letting ChatGPT spill out endless pages can drain your o3 or o3-Pro credits in no time. That’s because OpenAI bills based on tokens; not API calls. Every word you send and every word you receive counts toward your token quota.
What Are Tokens?
Tokens are chunks of text, about four characters each on average. So a 100-word exchange (prompt + response) can use roughly 267 tokenS.
Why Tokens Matter
OpenAI charges per 1,000 tokens at each model’s rate. For example, o3 runs cost $10 per 1M input tokens and $40 per 1M output tokens. That means a few long back-and-forths can add up fast.
5 Tips to Save Tokens (and Credits)
Keep Prompts Lean Only include the essential context. Trim off old chat history and extra details.
Cap the Response Use the max_tokens parameter to set an upper limit on how long the AI’s reply can be.
Pre-Filter Your Input Skim large documents yourself or use a cheaper model (o1/o2) to pull out key sections before sending to o3.
Tier Your Models Do rough summaries and drafts on lower-cost models, then reserve o3/o3-Pro for final extraction or complex parsing tasks.
Highlight Specific Excerpts Instead of dumping entire PDFs or logs, copy only the tables, paragraphs, or bullet points you need.
By thinking in tokens and targeting your heavy-duty models only when it counts, you’ll stretch your o3 and o3-Pro credits further—saving money and getting faster, more focused results.
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