A field guide to AI pricing

Whatisamilliontokens?

Every AI API bills you per million tokens. Nobody tells you how much that actually is. Here's the answer — in novels, photos, podcasts, and dollars.

≈ 750,000 words≈ 8 novels of input≈ 2,000 long responses of output$0.50 input – $30 output per 1M tokens

The scale — input tokens

How much can an AI read with a million tokens?

Input tokens are what you send to the model — documents, code, images, conversations. Here's what a million of them looks like.

≈ 750,000 words English words, drawn to scale

1 block = 750 words

≈ 750,000 words

English words

On average one token is about ¾ of an English word, so a million tokens works out to roughly three quarters of a million words.

basis: 1 token ≈ 0.75 words

≈ 1,500 pages single-spaced pages, drawn to scale

1 block = 1 page

≈ 1,500 pages

single-spaced pages

At about 500 words per single-spaced page, 750,000 words fills roughly 1,500 pages — a stack of paper about 15 cm (6 in) tall.

basis: 500 words per page

≈ 4,000,000 characters characters of text, drawn to scale

1 block = 4,000 characters

≈ 4,000,000 characters

characters of text

One token is roughly 4 characters of English text, so a million tokens is about 4 million characters — enough to fill 8,000 standard text messages.

basis: 1 token ≈ 4 characters

≈ 10,000 paragraphs paragraphs of text, drawn to scale

1 block = 10 paragraphs

≈ 10,000 paragraphs

paragraphs of text

An average paragraph is about 75 words (~100 tokens). A million tokens is roughly 10,000 paragraphs — a library of essays.

basis: ~75 words per paragraph

≈ 50,000 sentences sentences, drawn to scale

1 block = 50 sentences

≈ 50,000 sentences

sentences

The average English sentence is about 15 words (~20 tokens). A million tokens is roughly 50,000 sentences.

basis: ~15 words per sentence

≈ 8 novels average novels, drawn to scale

1 spine = 1 novel

≈ 8 novels

average novels

A typical novel runs about 90,000 words (~120,000 tokens), so a million tokens holds roughly eight of them.

basis: 90,000 words per novel

≈ 69% of Harry Potter of the complete 7-book series, drawn to scale

the 7 books — filled part = 1M tokens

≈ 69% of Harry Potter

of the complete 7-book series

The full Harry Potter series is 1,084,170 words — about 1.45 million tokens. A million tokens gets you past Order of the Phoenix and into the early chapters of Half-Blood Prince.

basis: Series total 1,084,170 words

≈ 1.3 × War and Peace copies of War and Peace, drawn to scale

2 copies — filled part = 1M tokens

≈ 1.3 × War and Peace

copies of War and Peace

Tolstoy’s epic is about 587,000 words in English translation (~780,000 tokens). A million tokens covers it with room for a second read of the first third.

basis: 587,000 words per copy

≈ 1.6 × Lord of the Rings copies of the trilogy, drawn to scale

2 copies — filled part = 1M tokens

≈ 1.6 × Lord of the Rings

copies of the trilogy

Tolkien's trilogy is about 481,000 words (~641,000 tokens). A million tokens covers the full trilogy with room for more than half a reread.

basis: 481,000 words (trilogy without appendices)

≈ 96% of the Bible of the King James Bible, drawn to scale

1 Bible — filled part = 1M tokens

≈ 96% of the Bible

of the King James Bible

The King James Bible is about 783,000 words (~1.04 million tokens). A million tokens gets you through almost the entire text — falling just short at Revelation.

basis: 783,137 words (KJV)

≈ 42% of Game of Thrones of the 5 published ASOIAF books, drawn to scale

the 5 books — filled part = 1M tokens

≈ 42% of Game of Thrones

of the 5 published ASOIAF books

George R.R. Martin's A Song of Ice and Fire is about 1,770,000 words (~2.36 million tokens). A million tokens covers the first two books and about 30% of A Storm of Swords.

basis: 1,770,000 words (5 published books)

≈ 100,000 lines of code lines of source code, drawn to scale

1 block = 100 lines of code

≈ 100,000 lines of code

lines of source code

Code averages roughly 10 tokens per line, so a million tokens is about 100,000 lines — nearly twice the original Doom engine (~57,000 lines).

basis: ~10 tokens per line

≈ 1,000 images detailed images analyzed, drawn to scale

1 tile = 1 image

≈ 1,000 images

detailed images analyzed

Vision models spend roughly 800–1,600 tokens to read one detailed image, so a million tokens lets a model look at about a thousand photos or screenshots.

basis: ~1,000 tokens per image

≈ 650 PDF pages visually-processed PDF pages, drawn to scale

1 block = 1 PDF page

≈ 650 PDF pages

visually-processed PDF pages

When a model reads a PDF page as both text and image (preserving layout, tables and figures), each page costs ~1,500 tokens. Text-only extraction is cheaper: ~2,000 pages per million tokens.

basis: ~1,500 tokens per page with layout

≈ 75,000 chat messages chat messages, drawn to scale

1 block = 25 messages

≈ 75,000 chat messages

chat messages

A typical text message is about 10 words (~13 tokens). A million tokens is over six years of texting at 30 messages a day.

basis: ~13 tokens per message

≈ 83 hours of speech hours of transcribed speech, drawn to scale

1 bar = 1 hour of talking

≈ 83 hours of speech

hours of transcribed speech

People speak about 150 words per minute (~200 tokens/min), so a million tokens transcribes about 83 hours — three and a half days of non-stop talking.

basis: 150 words per minute

≈ 110 podcast episodes 45-minute episodes, drawn to scale

1 block = 1 episode

≈ 110 podcast episodes

45-minute episodes

At ~9,000 tokens per 45-minute episode transcript, a million tokens holds about 110 episodes — a full year of a weekly show, twice over.

basis: 45 min per episode, 150 wpm

≈ 14,000 max-length posts 280-character posts, drawn to scale

1 block = 10 posts

≈ 14,000 max-length posts

280-character posts

A maxed-out 280-character post is about 70 tokens, so a million tokens is roughly 14,000 of them — about 10 years of posting four times a day.

basis: 280 chars ≈ 70 tokens

≈ 10,000 work emails typical work emails, drawn to scale

1 block = 10 emails

≈ 10,000 work emails

typical work emails

The average work email body runs about 75 words (~100 tokens). A million tokens is ten thousand of them — several years of a busy inbox.

basis: ~100 tokens per email

≈ 37 movie screenplays feature-film screenplays, drawn to scale

1 spine = 1 screenplay

≈ 37 movie screenplays

feature-film screenplays

A feature screenplay is around 20,000 words (~27,000 tokens), so a million tokens holds about 37 movies’ worth of scripts.

basis: 20,000 words per screenplay

≈ 125 Terms & Conditions Terms & Conditions agreements, drawn to scale

1 block = 1 T&C agreement

≈ 125 Terms & Conditions

Terms & Conditions agreements

The average social media Terms & Conditions is about 6,000 words (~8,000 tokens). A million tokens is 125 of them — still less than what you've blindly agreed to this year.

basis: ~6,000 words per T&C

≈ 15,000 IKEA manuals IKEA instruction manuals, drawn to scale

1 block = 10 manuals

≈ 15,000 IKEA manuals

IKEA instruction manuals

An IKEA manual averages about 50 words of actual text (~67 tokens) — they're mostly pictures of confused stick figures. A million tokens is 15,000 of them. You'd still build the shelf wrong.

basis: ~50 words per manual

≈ 50,000 Tinder bios dating app bios, drawn to scale

1 block = 10 bios

≈ 50,000 Tinder bios

dating app bios

The average dating profile bio is about 15 words (~20 tokens) — mostly "love to travel" and dog photos. A million tokens is 50,000 of them. That's a lot of swiping.

basis: ~15 words per bio

≈ 1,000 Wikipedia articles average Wikipedia articles, drawn to scale

1 block = 1 article

≈ 1,000 Wikipedia articles

average Wikipedia articles

The average Wikipedia article is about 725 words (~970 tokens), so a million tokens is roughly a thousand articles of general knowledge.

basis: ~725 words per article

≈ 2,500 songs songs’ worth of lyrics, drawn to scale

1 bar = 2 songs

≈ 2,500 songs

songs’ worth of lyrics

Song lyrics average ~300 words (~400 tokens), so a million tokens is the lyrics to about 2,500 songs — over six days of continuous music.

basis: ~300 words per song

The other side — output tokens

What can an AI write with a million tokens?

Output tokens are what the model generates back. They cost 2-8x more than input tokens, but each response is typically short — a few hundred to a few thousand tokens. A million output tokens accumulates over many requests.

≈ 2,000 detailed responses long AI responses, drawn to scale

1 block = 1 response

≈ 2,000 detailed responses

long AI responses

A detailed AI response (explanation, analysis, essay) averages about 500 tokens. A million output tokens is roughly 2,000 of these — months of heavy daily usage.

basis: ~500 tokens per detailed response

≈ 5,000 code completions function implementations, drawn to scale

1 block = 1 completion

≈ 5,000 code completions

function implementations

A typical code completion — a function implementation, a refactor, a bug fix — runs about 200 tokens. A million output tokens is 5,000 of these.

basis: ~200 tokens per code completion

≈ 500 blog posts AI-written articles, drawn to scale

1 block = 1 article

≈ 500 blog posts

AI-written articles

A full blog post or report generated by AI averages about 2,000 tokens (~1,500 words). A million output tokens produces 500 complete articles.

basis: ~2,000 tokens per article

≈ 10,000 email drafts AI-drafted email replies, drawn to scale

1 block = 10 emails

≈ 10,000 email drafts

AI-drafted email replies

A professional email reply generated by AI averages about 100 tokens (~75 words). A million output tokens drafts 10,000 emails — years of inbox assistance.

basis: ~100 tokens per email draft

≈ 1,000 document summaries page-long summaries, drawn to scale

1 block = 1 summary

≈ 1,000 document summaries

page-long summaries

A thorough document summary runs about 1,000 tokens (~750 words, a full page). A million output tokens produces 1,000 of these — an entire research library summarized.

basis: ~1,000 tokens per summary

≈ 50,000 chatbot replies short chatbot responses, drawn to scale

1 block = 10 replies

≈ 50,000 chatbot replies

short chatbot responses

A quick chatbot answer — a classification, a yes/no, a short extraction — averages about 20 tokens. A million output tokens is 50,000 of these, enough for a busy customer service bot.

basis: ~20 tokens per short reply

Why output costs more

Input tokens are cheap because the model just reads them. Output tokens are expensive because the model has to generate each one sequentially — predicting the next word thousands of times. That's why most models charge 3-6x more for output.

GPT-5.6 Sol$5/M in -> $30/M out(6x more)GPT-5.5 Pro$30/M in -> $180/M out(6x more)Claude Fable 5$10/M in -> $50/M out(5x more)GPT-5.6 Terra$2.5/M in -> $15/M out(6x more)

The price

Same money, very different shelf

Pick a budget and see how many novels' worth of text each model writes (or reads) for it. Prices updated 2026-07-28.

Gemini 3 Flash
28 novels$3/1M
Claude Haiku 4.5
16.7 novels$5/1M
GPT-5.6 Luna
13.9 novels$6/1M
Gemini 3.5 Flash
9.3 novels$9/1M
Claude Sonnet 5
8.3 novels$10/1M
Gemini 3.1 Pro
6.9 novels$12/1M
GPT-5.6 Terra
5.6 novels$15/1M
Claude Opus 4.8
3.3 novels$25/1M
GPT-5.6 Sol
2.8 novels$30/1M
GPT-5.5
2.8 novels$30/1M
Claude Fable 5
1.7 novels$50/1M
GPT-5.5 Pro
0.5 novels$180/1M

One spine = one novel written for $10. Same money, 60× difference between the cheapest and priciest model. Filtering the scale section above switches the unit here too.

View as a table
ModelProviderInput / 1MOutput / 1MContext
GPT-5.6 SolOpenAI$5$301.05M
GPT-5.5 ProOpenAI$30$180400K
Claude Fable 5Anthropic$10$501M
GPT-5.6 TerraOpenAI$2.50$151.05M
GPT-5.5OpenAI$5$30400K
Claude Opus 4.8Anthropic$5$251M
Claude Sonnet 5Anthropic$2$101M
Gemini 3.1 ProGoogle$2$121M
Gemini 3.5 FlashGoogle$1.50$91M
GPT-5.6 LunaOpenAI$1$61.05M
Claude Haiku 4.5Anthropic$1$5200K
Gemini 3 FlashGoogle$0.50$31M

GPT-5.5: Long-context (>272K input) priced at 2× input / 1.5× output. Claude Sonnet 5: Introductory pricing — list price is $3 / $15 from Sep 1, 2026. Gemini 3.1 Pro: Prices double for prompts above 200K tokens ($4 / $18).

The calculator

What does your budget buy?

Pick a model, set a budget, and see what it translates to.

Token direction

1,000,000tokens

750,000English words
8.3novels
1,499pages of text
100,000lines of code
1,000images analyzed
83.3hours of speech

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The fine print

Frequently asked questions

What exactly is a token?

A token is the unit AI models read and write text in — usually a word, part of a word, or a punctuation mark. "Cat" is one token, but "tokenizer" splits into pieces like "token" + "izer". In typical English, one token is about ¾ of a word, so 1,000 tokens ≈ 750 words.

Why do output tokens cost more than input tokens?

Generating text is much more computationally expensive than reading it. Every provider reflects that: output tokens typically cost 5–6× more than input tokens. Your real bill depends on your mix — a summarization app sends lots of input and gets little output, while a writing app is the opposite.

Is a token the same size across models?

No. Each model family uses its own tokenizer, so the same text can differ by 30% or more in token count between providers — and even between model generations from the same provider. That means a per-million-token price is only comparable once you account for how each tokenizer counts your text.

How can I count tokens before I send a request?

Every major provider offers a token-counting endpoint or tokenizer tool (Anthropic has a count-tokens API, OpenAI and Google publish tokenizers). Rough planning is easier: divide your word count by 0.75, or your character count by 4.

So what does one million tokens actually cost?

Anywhere from $0.50 (Gemini 3 Flash input) to $180 (GPT-5.5 Pro output) as of July 2026 — a 360× spread. That is exactly why it pays to know what a million tokens is before choosing a model.

Our methodology

How we calculate and verify

Token conversion

All comparisons use the widely-accepted standard of 1 token ≈ 0.75 English words (approximately 4 characters). Every figure on this site shows its underlying assumption so you can verify the math yourself.

Pricing data

Prices are verified against each provider's official API documentation and updated regularly. The date of the last pricing check is displayed alongside every price on this site.

Reference sources

Word counts for specific works (novels, screenplays, religious texts) are sourced from Wikipedia, Project Gutenberg, and other authoritative public references. Each comparison links to its source.

Independence

This site is independently maintained and not affiliated with any AI provider — not OpenAI, not Anthropic, not Google. We have no financial relationship with any model provider.