Paste your prompt to see estimated token count and specific suggestions to trim it without losing meaning.
For a single API call, a slightly wordy prompt costs a fraction of a cent, negligible. But system prompts in production applications are sent with every single API call, if your chatbot makes 100,000 calls a month with a system prompt that's 50 tokens longer than it needs to be, that's 5 million extra tokens billed for content that added no value to the model's output. Trimming a system prompt is a one-time edit that pays off on every subsequent call, unlike optimizing a single one-off prompt.
Phrases like "please," "I would like you to," and "kindly" are social conventions for communicating with humans, they don't measurably improve output quality from language models, and they add token cost on every call for zero benefit. This differs from conversational chat interfaces where a person is typing casually, system prompts and API instructions are read by a different context, direct, specific instructions consistently perform as well or better than politely-phrased ones.
Instructions like "be as thorough and comprehensive as possible" don't give the model a concrete target, it has no way to know what "as thorough as possible" specifically means for your use case, and different runs may interpret it inconsistently. Replacing vague intensifiers with concrete specifications ("cover these 3 points" or "limit to 200 words") both reduces token count and improves output consistency, a genuine double win rather than a pure cost trade-off.
Not all length is waste. Specific examples (few-shot demonstrations), explicit formatting instructions, and genuine context the model needs to complete the task correctly are not filler, cutting these to save tokens usually costs more in wasted API calls from poor-quality output than it saves in token cost. The goal of prompt optimization is removing genuinely redundant social/vague language, not stripping out substance.