A million tokens, measured in
Output tokens are what the model generates — each one costs 2-8× more than input because the model predicts them sequentially. A million output tokens is thousands of responses accumulated over many API calls.
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
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
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
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 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
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
Output tokens are the expensive side of the equation. While input tokens are processed in parallel, the model generates output tokens one at a time — predicting the next word, then the next, thousands of times. That sequential generation is why output costs 2-8x more than input. For applications that generate text at scale — chatbots, code assistants, content pipelines — output token budgets are usually the binding constraint.
Everything on this page is exactly one million tokens. So the price of having a model read all of it is simply each model's per-million rate. Updated 2026-10-02.
| Model | Read all of it (input) |
|---|---|
| GPT-6 Astra | $10 |
| Claude Fable 5.1 | $10 |
| Claude Opus 5 | $5 |
| Claude Opus 5.5 | $4 |
| GPT-6 Sol | $2 |
| Gemini 3.1 Pro | $2 |
| Claude Sonnet 5 | $2 |
| Claude Haiku 4.5 | $1 |
| Gemini 3.8 Flash | $0.75 |
| Gemini 3.5 Flash-Lite | $0.30 |
| GPT-6 Luna | $0.10 |
From $0.10 to $10 for the same million tokens. Try your own budget in the calculator →
Or start over with the full picture — scale, prices, and the budget calculator.