How many tokens are in a book?
The short answer
A standard novel is 80,000 to 130,000 tokens. Most novels fit comfortably in modern long-context models. GPT-5's 400k window or Claude Sonnet 4.5's 200k window holds nearly any single book.
Rough heuristic: ~1.3 tokens per English word for OpenAI's tokenizer, slightly higher for Claude's (which uses ~35% more tokens for the same text after the Opus 4.8 update). So a 70,000-word novel ≈ 91,000 tokens on GPT, ~120,000 on Claude.
Worked examples from real books
These numbers were generated using OpenAI's tiktoken (cl100k_base) for GPT-4-class models. Claude counts run 30-35% higher.
| Book | Approx words | GPT tokens | Claude tokens |
|---|---|---|---|
| *The Great Gatsby* | 47,000 | ~62,000 | ~84,000 |
| *Of Mice and Men* | 30,000 | ~40,000 | ~54,000 |
| *To Kill a Mockingbird* | 100,000 | ~135,000 | ~182,000 |
| *Pride and Prejudice* | 122,000 | ~165,000 | ~223,000 |
| *1984* | 89,000 | ~120,000 | ~162,000 |
| *Brave New World* | 64,000 | ~85,000 | ~115,000 |
| *Lord of the Rings* (one volume, *Fellowship*) | 187,000 | ~245,000 | ~330,000 |
| *Harry Potter and the Sorcerer's Stone* | 77,000 | ~102,000 | ~138,000 |
| *The Bible* (King James) | 783,000 | ~1,050,000 | ~1,420,000 |
| *War and Peace* | 561,000 | ~750,000 | ~1,015,000 |
| Average academic textbook | 200,000 | ~270,000 | ~365,000 |
Will it fit?
This is the question most people are actually asking. Here's where common books fit in 2026's context windows:
| Model | Context window | Fits |
|---|---|---|
| Claude Haiku 3.5 | 200,000 | Most novels; up to Pride & Prejudice on GPT counts, but Claude counts push longer novels over |
| Claude Sonnet 4.5 / Opus 4.8 | 200,000 | Same as Haiku |
| GPT-4o / GPT-4o-mini | 128,000 | Most novels under ~95k words |
| GPT-5 / GPT-5 family | 400,000 | Single-volume LOTR, *War and Peace* in ~2 chunks |
| GPT-5 Pro (1M variant) | 1,000,000 | Entire Bible in one call; *War and Peace* with room to spare |
| Gemini 2.5 Pro | 2,000,000 | Multi-volume works in a single context |
| Gemini 3.1 Pro | 2,000,000 | Same |
If you're working with a single average-length novel, anything from Claude Haiku upward will fit it. If you're working with reference books, multi-volume series, or full academic textbooks, Gemini Pro's 2M-token window is the only single-shot option in 2026.
Cost to send a whole book
Sending an entire book to a model costs money in proportion to its tokens. Real examples:
A 100k-token novel as input, with a 2,000-token output:
| Model | Input cost | Output cost | Total per call |
|---|---|---|---|
| Claude Haiku 3.5 ($0.80/$4) | $0.080 | $0.008 | $0.088 |
| GPT-4o-mini ($0.15/$0.60) | $0.015 | $0.001 | $0.016 |
| Claude Sonnet 4.5 ($3/$15) | $0.300 | $0.030 | $0.330 |
| GPT-4o ($2.50/$10) | $0.250 | $0.020 | $0.270 |
| GPT-5 ($1.25/$10) | $0.125 | $0.020 | $0.145 |
| Claude Opus 4.8 ($5/$25) | $0.500 | $0.050 | $0.550 |
| GPT-5 Pro ($30/$120) | $3.000 | $0.240 | $3.240 |
For analyzing books at scale, GPT-4o-mini at $0.016 per book is hard to beat. For careful single-book reasoning, GPT-5 at $0.145 per call is the sweet spot. For the highest-quality literary analysis, Opus 4.8 is the premium tier.
When you should chunk instead
Even when a book fits in a model's context window, sending the whole thing in one call isn't always optimal:
Cost. Most analysis questions don't need every word. A focused 20k-token chunk often gets better answers cheaper than the full 100k context.
Attention drift. Models perform worse at recall in the middle of very long contexts ("lost in the middle" effect). For specific lookups, retrieving relevant passages with embeddings and sending those is often more accurate than dumping the whole book.
Output quality. Long-context tasks tend to produce summaries-of-summaries; chunked tasks with iterative reasoning produce more granular work.
Rule of thumb: use full-book context for synthesis ("compare the themes of chapters 4 and 17"), use chunked retrieval for lookup ("what does the character X say about Y?").
How to count your own book
Drop the text into our GPT-5 counter or Claude counter for the exact token count. For very large books, split into ~50k-word chunks; both tokenizers are linear so the chunked counts sum correctly to the whole-book count.
If you're working programmatically, run tiktoken for OpenAI counts and the official count_tokens endpoint for Anthropic counts. The provider-specific counters on this site call those endpoints under the hood, so the result you see is what you'll pay for.
Try this on every model
- Claude Opus 4.8 $5.00/$25.00
- Claude Opus 4.8 (Fast Mode) $10.00/$50.00
- Claude Sonnet 4.6 $3.00/$15.00
- Claude Haiku 4.5 $1.00/$5.00
- GPT-5.5 $5.00/$30.00
- GPT-5.5 Pro $30.00/$180.00
- GPT-5.4 $2.50/$15.00
- GPT-5.4 Mini $0.75/$4.50
- GPT-5.4 Nano $0.20/$1.25
- GPT-5.4 Pro $30.00/$180.00
- GPT-5.3 $1.75/$14.00
- GPT-5.2 $1.75/$14.00
- GPT-5.2 Pro $21.00/$168.00
- GPT-5.1 $1.25/$10.00
- GPT-5 $1.25/$10.00
- GPT-5 Mini $0.25/$2.00
- GPT-5 Nano $0.05/$0.40
- GPT-5 Pro $15.00/$120.00
- GPT-4.1 $2.00/$8.00
- GPT-4.1 Mini $0.40/$1.60
- GPT-4.1 Nano $0.10/$0.40
- o3 $2.00/$8.00
- o3-mini $1.10/$4.40
- o3-pro $20.00/$80.00
- o4-mini $1.10/$4.40
- GPT-4o $2.50/$10.00
- GPT-4o mini $0.15/$0.60
- GPT-4 Turbo $10.00/$30.00
- Gemini 3.1 Pro $2.00/$12.00
- Gemini 3 Flash $0.50/$3.00
- Gemini 3.1 Flash-Lite $0.25/$1.50
- Gemini 2.5 Pro $1.25/$10.00
- Gemini 2.5 Flash $0.30/$2.50
- Gemini 2.5 Flash-Lite $0.10/$0.40
- Llama 3.3 70B $0.88/$0.88
- Llama 3.1 405B $3.50/$3.50
- Llama 3.1 70B $0.59/$0.79
- Llama 3.1 8B $0.18/$0.18
- Mistral Large $2.00/$6.00
- DeepSeek V3 $0.27/$1.10
- DeepSeek V3.1 $0.60/$1.70
- DeepSeek R1 $3.00/$7.00
- Qwen 2.5 72B $0.90/$0.90
- Qwen 2.5 Coder 32B $0.80/$0.80
- Qwen3 Coder 480B $2.00/$2.00
- GLM-5.1 $1.40/$4.40