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OpenAI's GPT-5.4 supports a massive 256K token context window, allowing you to feed entire codebases, books, or lengthy research papers into a single prompt. This makes it ideal for tasks that require long-range comprehension.

Formatting tokens add up fast. A code block with triple backticks and a language specifier adds ~5 tokens. Markdown headers, bullet points, and bold formatting each add 1-3 tokens. For high-volume APIs, stripping unnecessary formatting in prompts can reduce token counts by 5-15%.

Your system prompt is sent with every API request and billed as input tokens every time. A 2,000-token system prompt on 100,000 daily calls equals 200M tokens/day just in system prompts. Keep system prompts concise, or use prompt caching to amortize the cost.

Some tokenizers treat words with leading spaces differently. For example, ' hello' might be a different token than 'hello'. Be consistent with spacing, especially when providing examples, to ensure token efficiency.

Different LLMs use different tokenizers. The same piece of text can result in a varying number of tokens depending on the model, which impacts cost and context window usage. Tools like TokenCalculator help you see these differences.

System prompts are often re-sent with every API call. Keep them concise to save tokens. Use abbreviations or keywords if the model is fine-tuned or understands them.

System prompts are often re-sent with every API call. Keep them concise to save tokens. Use abbreviations or keywords if the model is fine-tuned or understands them.

If you frequently use long model names in your prompts or code (e.g., 'gpt-3.5-turbo-instruct-0914'), consider using shorter aliases if your framework allows, or use variables to store them. This can slightly reduce token count in complex prompts.

Use a tokenizer tool (like the one on TokenCalculator.com!) to see how your text is split into tokens. This helps you understand why certain phrasing might consume more tokens and how to optimize it.

In chat applications, analyze how much of the conversation history is repeated tokens. Strategies like summarization can reduce this significantly.

Different LLMs tokenize text differently. The same sentence can result in varying token counts across models, directly affecting API costs and context window usage.