LLMiserLLMISER · LLM COST OPTIMIZATION
Make Every Token Count.
LLMiser is a free LLM cost optimizer for document inputs. Convert PDFs, Word files, spreadsheets, slides, images and ZIP projects to Markdown, then trim unnecessary context before sending it to an AI assistant or retrieval pipeline.
Optimize a document →How can document preparation reduce LLM costs?
An AI request can include much more context than the question needs. Converting a document to editable Markdown makes its text easier to inspect, select and reuse. Clean and Compact profiles remove some formatting overhead. Selecting relevant sections can reduce unnecessary input tokens.
Savings vary. Native PDF ingestion may include page images as well as text, while other workflows extract text only. Markdown can omit charts, equations or layout information. Keep the original when visual details matter, and check output quality before using a smaller representation.
Measure the result for your model
- Run the same task with the original input method and the reviewed Markdown.
- Record the exact model, tokenizer, billable input and output usage, caching and image charges.
- Use your provider's current prices, and include any OCR or processing costs.
- Compare answer accuracy, especially for tables, scans and visual material.
Text input cost = billable input tokens ÷ 1,000,000 × price per million tokens.
The app estimates text tokens using a character-based heuristic. It does not measure the original PDF's billable tokens or promise a fixed saving. It prepares documents rather than routing models, caching API calls or changing your provider's prices.
Supported documents and output
| Input | Formats | Limit | Output |
|---|---|---|---|
| Documents | PDF, DOCX, PPTX, XLSX, XLS, EPUB, HTML, CSV, TSV, JSON, XML, TXT, MD | 15 MB per file | Markdown with headings, lists and pipe tables |
| Scanned PDFs | PDF pages that are only pictures | 15 MB per file | Text pages read directly, scanned pages through text recognition (up to 100 per file) |
| Images | PNG, JPG, JPEG, WebP, TIFF, BMP, GIF | 10 MB per image | The text in the image, as Markdown |
| ZIP files | Code projects and document folders | 15 MB upload, 2,000 files inside | One combined Markdown file, a project index and a manifest |
Review converted headings, reading order, tables and OCR. RAG-ready adds section markers for downstream processing. It does not build an index or guarantee retrieval quality.
Frequently asked questions
What is LLMiser?
LLMiser is a free LLM cost optimizer for document inputs, made by DevBehindYou. It converts PDFs, Office files, images and ZIP projects into editable Markdown so you can remove unnecessary context before sending it to an AI assistant or RAG pipeline.
How does LLMiser help optimize LLM costs?
Convert a document, choose Clean or Compact output, and keep only the sections your task needs. Sending less unnecessary context can reduce billable input tokens. Savings depend on the model, tokenizer, input method and content. Always compare the result with the original and check answer quality.
Does converting PDF to Markdown always save tokens?
No. A provider that processes both PDF text and page images may use more tokens than a text-only Markdown input. A text extraction workflow may show little difference. Markdown can also lose charts or visual layout, so measure actual usage for your model and task.
How do I convert documents with LLMiser?
Choose or drop up to 20 files, select Standard, Clean, Compact or RAG-ready, and press Convert all. Review and edit the Markdown, then copy it, download a .md file or export a ZIP. Documents support up to 15 MB per file.
Can LLMiser read scanned PDFs and images?
Yes. LLMiser reads PDF text directly and uses text recognition for scanned pages, up to 100 scanned pages per file. PNG, JPG, WebP and other supported images work up to 10 MB each. Review handwriting, small print, tables and equations for recognition errors.
What happens to uploaded files?
Files are uploaded to the service for conversion, not processed entirely on your device. Uploads and results use private storage with a default 48-hour retention period. Administrative retention exceptions are described in the Privacy Policy. No account is required, and files are not used to train AI models.
What do the output profiles do?
Standard keeps the full result with a title and a source note. Clean removes the source note and extra blank lines. Compact also removes divider lines. RAG-ready adds a chunk marker before section headings. Review the output before using it, since smaller input is not automatically better input.