See the token count before you paste

Turn a folder of papers into one context window.

Drop the PDFs, your notes, and the data. FileConcat pulls the text out of every paper right in your browser, packs it into one document, and shows you the token count so you know it fits ChatGPT, Claude, or Gemini before you paste.

  • Nothing is uploaded
  • Token count shown before you paste
  • PDFs, notes, and data
See a worked example

Drag your reading folder here

Papers, notes, and data. Read in a second.

Know it fits before you paste.

The hard part of reading with a model is not the reading, it is the budget. FileConcat counts the tokens as it bundles, so you see whether the whole pile lands in one window. When it runs long, it splits into a few numbered parts you can feed in order.

Counted locally, with the same tokenizer the models use

attention-survey · token budget
6 papers, notes, data≈ 128,000 tokens
64% of a 200k windowfits in one paste

From a reading pile to a prompt.

  1. 1

    Drop the reading folder

    Papers, notes, and datasets together. Nested folders are fine.

  2. 2

    Every file becomes text

    PDFs are extracted, notes and data pass straight through, boilerplate is dropped.

  3. 3

    One document, sized to fit

    You get a single file, or a few numbered parts when the pile runs past the window.

One pile, three kinds of file.

A literature review is rarely just PDFs. Your notes and your numbers go in the same bundle, so the model reasons over the reading and the evidence at once.

Papers

PDF

born-digital preprints and journal PDFs

Notes

MD · TXT

your reading notes and outlines

Data

CSV · JSON · XLSX

results tables and small datasets

Scanned and heavy two-column PDFs read poorly

Text is pulled from the PDF layer, not from the pixels. A scan with no text layer comes through flagged as empty rather than dropped, and a dense two-column layout can arrive out of order. There is no OCR step yet.

A review folder, packed and counted.

Three papers, a note file, and a results table go in. One document comes out, labeled as documents, with the token count already known.

attention-survey/
attention-survey/
├── papers/
│   ├── vaswani-2017.pdf
│   ├── devlin-2019.pdf
│   └── brown-2020.pdf
├── notes.md
└── benchmarks.csv
becomes
attention-survey.txt
≈ 96,300 tokens
<documents project="attention-survey">
<summary>
This is a packed snapshot of a set
of documents, assembled by
fileconcat.com.
File count: 5.
</summary>
...

Fit the whole literature in one prompt.