What Is the Estimated Cost to Convert a Book with book-to-skill?

The book-to-skill library itself charges no fees; the estimated cost to convert a book with book-to-skill depends entirely on the number of language-model tokens your source material generates, which you can predict deterministically before running any model-backed conversion.

The book-to-skill repository provides a deterministic, language-agnostic token estimator that lets you predict API expenses before processing begins. Unlike managed services that bill per conversion, this open-source tool reveals exactly how many tokens your book will consume, allowing you to apply your chosen LLM provider's pricing rates. Understanding how the estimated cost to convert a book with book-to-skill is calculated ensures you never encounter surprise charges during large-scale document processing.

How book-to-skill Calculates Token Estimates

The Core Algorithm in utils.py

The estimate_tokens(text: str) -> int function in [book_to_skill/utils.py](https://github.com/virgiliojr94/book-to-skill/blob/master/book_to_skill/utils.py) performs deterministic token counting without external dependencies. For Latin-based (whitespace-delimited) text, the function divides word counts by the WORDS_PER_TOKEN constant defined in [book_to_skill/config.py](https://github.com/virgiliojr94/book-to-skill/blob/master/book_to_skill/config.py).

The default value of WORDS_PER_TOKEN is approximately 1.33, meaning every 1.33 words consumes roughly one token. This constant lives alongside other configuration parameters and can be tuned for different model families if your target LLM uses a different tokenizer ratio.

Handling CJK Characters Accurately

Asian language books require special handling to avoid massive underestimation. The regex _CJK_RE in utils.py captures Unicode blocks including Chinese, Japanese, and Korean characters (covering supplementary planes) to apply the CJK_CHARS_PER_TOKEN constant instead.

When CJK characters are detected, each character counts directly against CJK_CHARS_PER_TOKEN (default 1.0). Without this logic, token estimates for CJK-heavy books could be off by up to 1,000×, leading to severe budget miscalculations.

From Tokens to Dollars: Calculating Conversion Cost

Once you have the token estimate, multiply it by your LLM provider's pricing. Typical OpenAI rates (as of 2024) are:

Model	Input price / 1,000 tokens	Output price / 1,000 tokens
gpt-3.5-turbo	$0.0005	$0.0015
gpt-4-turbo	$0.003	$0.006
gpt-4-32k	$0.06	$0.12

Use this formula to calculate the estimated cost to convert a book with book-to-skill:

Estimated cost = tokens × price_per_1k / 1,000

For example, a 90,000-word novel generates approximately 68,000 tokens (90,000 ÷ 1.33). Running this through gpt-4-turbo at $0.003 per 1,000 input tokens costs roughly $0.20 (68,000 × 0.003 ÷ 1,000).

Running Cost Estimates via CLI and Python

Programmatic Estimation

Import the utility function directly to estimate costs within your Python workflow:

# Quick token estimate for a plain-text string

from book_to_skill.utils import estimate_tokens

sample = "Chapter 1. Introduction\n" + ("Lorem ipsum " * 5000)
print("Estimated tokens:", estimate_tokens(sample))

# → Estimated tokens: 380 (≈ 0.38 k tokens)

For real book files, combine the parser with the estimator:

from pathlib import Path
from book_to_skill.parsers.text import read_text_file
from book_to_skill.utils import estimate_tokens

book_path = Path("my_book.txt")
raw_text = read_text_file(book_path)          # Extracts text according to file type

tokens = estimate_tokens(raw_text)

# Apply gpt-4-turbo input pricing

price_per_1k = 0.003  # USD

cost_usd = tokens * price_per_1k / 1_000
print(f"Tokens: {tokens:,} → Estimated cost: ${cost_usd:.2f}")

Command-Line Dry Run

The CLI provides instant estimates without performing the actual conversion. The --dry-run flag in [book_to_skill/cli.py](https://github.com/virgiliojr94/book-to-skill/blob/master/book_to_skill/cli.py) calculates tokens and displays the projected cost:

$ python -m book_to_skill.cli --dry-run /path/to/book.pdf
[INFO] Detected 2,345 tokens → Estimated cost on gpt-4-turbo: $7.04

This approach leverages the same estimate_tokens logic used by [scripts/extract.py](https://github.com/virgiliojr94/book-to-skill/blob/master/scripts/extract.py), ensuring consistency between CLI previews and actual conversion costs.

Summary

• book-to-skill is free; you pay only for the LLM tokens consumed during conversion.
• The estimate_tokens() function in utils.py calculates costs deterministically using WORDS_PER_TOKEN (≈1.33) for Latin text and CJK_CHARS_PER_TOKEN (1.0) for Asian characters.
• The _CJK_RE regex prevents underestimation for CJK-heavy books by counting characters individually rather than using word boundaries.
• Use --dry-run in the CLI or import estimate_tokens programmatically to predict expenses before processing PDFs, EPUBs, or DOCX files.

Frequently Asked Questions

Does book-to-skill charge a fee per book conversion?

No, the repository is open-source and charges no usage fees. You are responsible only for the API costs charged by your chosen LLM provider (OpenAI, Anthropic, etc.) based on the token count calculated by the library's estimator.

How accurate is the token estimator compared to OpenAI's tokenizer?

The estimator provides a deterministic approximation using language-agnostic heuristics (1.33 words per token for Latin text, 1 character per token for CJK). While it may not match OpenAI's exact tiktoken counts byte-for-byte, it avoids external dependencies and guarantees consistent results across runs, typically staying within 5-10% of actual token counts for most English prose.

Can I adjust the token calculation for different LLM models?

Yes. Edit the WORDS_PER_TOKEN and CJK_CHARS_PER_TOKEN constants in book_to_skill/config.py to match your specific model's tokenizer characteristics. For example, some models use 1.5 words per token rather than 1.33, and updating this constant ensures your estimated cost to convert a book with book-to-skill reflects your actual provider's rates.

Why does the tool handle CJK characters differently?

CJK languages (Chinese, Japanese, Korean) do not use whitespace to delimit words. If the estimator used space-splitting alone, a 10,000-character Chinese novel might be counted as only 10 "words," leading to a 1,000× underestimation of costs. The _CJK_RE regex identifies these Unicode blocks automatically, counting each character as a full token to maintain billing accuracy.

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    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
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}