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Clean and minify text/code for LLM context windows. Strip comments, whitespace, trailing commas, and markdown fences. Real-time token savings estimate. All processing is local.
Paste your prompt, code, or context. Cleaned output appears in the right pane.
LLMs have a maximum context length — the total number of tokens they can process in a single request. Exceeding it causes truncation or higher costs. Cleaning redundant content helps you fit more useful information within the limit.
Comments, blank lines, indentation, and markdown fences add tokens without adding meaning. Removing them can reduce token usage by 20–40% in typical code and documentation, saving money and improving retrieval quality.
All cleaning happens entirely in your browser using client-side JavaScript. Your text never leaves your device and is never sent to any AI provider or server.
Code comments explain intent to humans but add tokens without helping the model reason. Strip them when feeding large files into an LLM for analysis or refactoring.
Indentation, blank lines, and trailing spaces consume tokens. Collapsing them to single spaces can shrink large inputs significantly while preserving semantics.
Markdown code block delimiters (```) add tokens on every block. If you're feeding already-extracted code, strip the fences to save tokens per block.
Trailing commas in JSON, object literals, and arrays are valid but unnecessary for LLMs. Removing them is a quick win when every token counts.
