Clean the context first. Keep the information that matters and remove document formatting that does not help answer the question.
No sign-up for the first conversions · Files processed temporarily · Current plan limits apply
More context is not automatically better context. Long documents often contain repeated headers, page numbers, layout fragments and redundant spacing. Those elements can consume context without improving the answer.
TokenPig converts supported files into structured Markdown and estimates the token difference. The goal is not to make every document as short as possible. The goal is to improve the ratio of useful information to formatting noise.
Compare estimated tokens before and after cleanup on the actual document.
Use structured Markdown rather than flattening everything into an unreadable wall of text.
Free room for instructions, examples, additional sources and the model’s response.
Use the same cleanup process for reports, policies, meeting packs and research material.
Decide what you need from the document so you can keep relevant sections and remove the rest.
Upload the PDF, DOCX, PPTX, XLSX or another supported format.
Review the before-and-after token estimate and inspect what was removed.
Give ChatGPT the Markdown plus a precise question, desired output and constraints.
Download the sample, run it through TokenPig and inspect the Markdown yourself before trusting it with important documents.
The extracted representation can include repeated text, markup, layout artifacts and other content that is not central to the task.
Markdown is lightweight, but the reduction depends on the original extraction and cleanup. The useful comparison is the estimate shown for your specific file.
No. Use a more aggressive mode only when the removed detail is not needed. Accuracy and completeness matter more than a headline reduction percentage.
Cleaner, relevant context can help, but performance also depends on the model, instructions, source quality and task.
State the goal, tell the model to rely on the supplied source, define the desired format and ask it to flag missing information rather than inventing it.
Start with a real file, inspect the output, and keep the original source for verification.
Try TokenPig free