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1M Token Context Document Planner

Plan whether PDFs, meeting notes, transcripts, and project docs can fit into a 1M-token context window before you paste them into an AI model.

Plan a Long-Context Document Pack
Context presets
Document groups

Enter rough counts by page, word, or known token count. Nothing is uploaded; this is a planning estimate.

Context Fit
Fits in one pass
Estimated input tokens
396K
44.9% of usable context
Usable input budget
882K
after output reserve and safety margin
Remaining
486K
Overflow
0
Batches
1
Input use before safety margin40.4%
Your estimated 396K input tokens fit inside the usable budget of 882K. You can likely paste the full pack in one long-context run.
Suggested Batch Plan
Batch 1
396K
300-page PDF, Project documentation, Meeting notes

Plan before pasting a huge document set

Use this 1M token context planner when the hard part is not the model, but whether your source material can safely fit.

Can a 300-page PDF fit?
Estimate a long PDF by page count and text density, reserve space for the answer, and see whether it fits in one context window.
Pack 20 meeting notes
Enter meeting note word counts, transcripts, and project docs to decide whether to paste everything or split by topic.
Prepare project documentation
Prioritize must-include specs, API docs, architecture notes, and optional references before asking an AI model to review them.

Token estimation guide

These are rough planning heuristics. Exact token counts vary by language, formatting, OCR quality, tables, and model tokenizer.

Input type
Planning estimate
When to use it
Light pages or slides
About 200-350 tokens per page
Good for slide decks, short memos, sparse notes, or pages with lots of whitespace.
Normal business documents
About 600-700 tokens per page
Use for reports, project docs, policy docs, and ordinary PDFs.
Dense PDFs or legal docs
About 900-1,200 tokens per page
Use when pages are full of small text, tables, footnotes, or OCR artifacts.
Word count
About 1.2-1.6 tokens per word
Use when Google Docs, Word, or your transcript tool already shows word counts.

How to use the batch plan

If the estimate does not fit, the best workaround is usually a controlled multi-pass workflow rather than random truncation.

Must include first

Keep source-of-truth docs together

Put contracts, final specs, or authoritative decisions before background notes.

Reserve output space

Long answers need room

A 1M context window still needs room for instructions, reasoning artifacts, citations, and the final answer.

Synthesize after batches

Ask for structured batch notes

Have the model summarize each batch with source labels, then merge those summaries in a final run.

1M Token Context Planner FAQ

Answers for long-context document planning, token estimates, PDFs, meeting notes, and batching.

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