Are my PDF or DOCX files uploaded to external servers?

No. CleanWordCounter uses in-memory WebAssembly and client-side JavaScript parsers. Your files are decoded directly in your device's RAM and never leave your web browser.

Is CleanWordCounter compliant with NDAs, HIPAA, and confidentiality agreements?

Yes. Because there is zero data transmission over the network, using CleanWordCounter preserves full confidentiality for legal contracts, medical transcripts, manuscripts, and proprietary code.

Which file formats can I count words in?

You can drag and drop or upload PDF (.pdf), Microsoft Word (.docx), Plain Text (.txt), Markdown (.md), and Rich Text (.rtf) files.

100% Client-Side In-Memory Parser

Count Words in PDF & DOCX Without Uploading

Drag & drop any document to count words, characters, and reading time instantly in browser memory. Zero server transmission.

Words

Avg 0 chars/word

Characters

0 no spaces

Sentences

0 words/sent

Paragraphs

0 sents/para

Reading Time 225 wpm

0 sec

Silent reading

Speaking Time 130 wpm

0 sec

Speech / keynote

GOAL TARGET: No target set

Draft 1

Saved to device memory

Keywords:

Type text to view frequency

Reading Level: Ready

Keyword Frequency & Density

Identify recurring terms and calibrate your SEO phrase balance.

Filter stop words (the, is, at...)

PHRASE	OCCURRENCES	DENSITY
Type or paste text above to calculate keyword density.

AI Token Counter & Model Suite (2020 – 2026)

BPE sub-word token metrics, context window capacity, and official API pricing for 45+ AI models.

~0 chars/token 45 Models Supported

Era:

Brand:

Claude 3.7 Sonnet

2025 Anthropic

Hybrid standard & extended reasoning model with state-of-the-art coding and prose capabilities.

Tokenizer: claude

Active Token Count

~0 chars / token

Context Window Usage 0.00%

0 / 200,000

API Input / Output Cost

$0.000000

$3.00 / 1M input $15.00 / 1M output

Browse & Compare Models (45 matching)

Click any card to inspect full details

Historical Era Benchmark: 2020 GPT-3 to 2026 Claude 3.7

How model context, tokenizer efficiency, and API cost per million tokens evolved over 6 years.

ERA	MILESTONE MODEL	CONTEXT	TOKENIZER	YOUR TOKENS	INPUT COST

100% Client-Side Tokenization. Exact byte-pair encoding heuristics calibrated against official model vocabularies.

Security Architecture

Why Traditional PDF Word Counters Pose Confidentiality Risks

Most online word counters require you to upload your PDF or Word documents to their backend cloud servers for OCR or text extraction. If you work in legal defense, medical translation, financial auditing, or corporate journalism, uploading unpublished drafts or client records violates non-disclosure agreements (NDAs) and privacy regulations like GDPR and HIPAA.

CleanWordCounter executes client-side parsers (Mozilla pdfjs-dist and in-memory Mammoth DOCX pipelines) directly inside your web browser’s JavaScript sandbox. You can even disconnect your Wi-Fi and the tool will continue counting words flawlessly.

Count Words in PDF & DOCX Without Uploading

Keyword Frequency & Density

Exclude References & In-Text Citations

Style & Quality Audit

Filler Words Breakdown

Passive Voice Phrases

AI Token Counter & Model Suite (2020 – 2026)

Claude 3.7 Sonnet

Browse & Compare Models (45 matching)

Historical Era Benchmark: 2020 GPT-3 to 2026 Claude 3.7

Why Traditional PDF Word Counters Pose Confidentiality Risks

Count Words in PDF & DOCX Without Uploading

Keyword Frequency & Density

Platform Character Constraints & SERP Previews

Exclude References & In-Text Citations

Style & Quality Audit

Filler Words Breakdown

Passive Voice Phrases

AI Token Counter & Model Suite (2020 – 2026)

Claude 3.7 Sonnet

Browse & Compare Models (45 matching)

Historical Era Benchmark: 2020 GPT-3 to 2026 Claude 3.7

Set Word Goal

Why Traditional PDF Word Counters Pose Confidentiality Risks