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LLM Summarization Cost Calculator

What it costs to summarize documents in bulk with Claude, GPT or Gemini, by document length, summary length and daily volume, on the batch API.

Updated · By Niraj Kumar · 6 sources

Model

Each request

Count text inCount text in
words

About 6,000 tokens. System prompt, conversation history, documents and tool definitions are all resent on every call.

words

About 467 tokens. The visible answer.

Count tokens from a prompt or JSON payloadShow

Plain text, OpenAI Chat or Responses JSON, or Anthropic Messages JSON. A final assistant message or a response body counts as output. Counted in your browser; nothing is uploaded.

Loading the tokenizer…

Counts use OpenAI's o200k tokenizer, so they are exact for GPT-6 and GPT-5 models, plus a few tokens of message framing. Other providers' tokenizers count the same text differently and do not publish a conversion factor; Anthropic says Claude models from 4.7 on produce about 30% more tokens than earlier Claude models. For an exact count on another model, paste one of its API responses: the usage it reports is shown above.

tokens

Thinking tokens are billed as output. Leave at 0 for models or settings that do not reason.

Volume

PricePrice

600,000 requests over 30 days.

Discounts

%
0%100%

The share of input that repeats exactly at the start of every request, such as a fixed system prompt. The prompt caching calculator prices cache writes and expiry in detail.

Batch APIBatch API
Monthly cost$2,501$0.00417 a request on Claude Haiku 4.5
Per 1,000 requests
$4.17
Input
$1,800
Output
$700.50
Price rank
9th cheapest of 24
Cheapest listed model
Claude Haiku 5.5
$250.05 a month, $2,250 less
Output28%
  • Input72%
  • Output28%
Every model on this workload, per month6,000 tokens in, 467 out, 20,000 a day, batch rates. Hover or focus a row for rates.
  1. 1Claude Haiku 5.5Anthropic$250.05
  2. 2GPT-6 LunaOpenAI$250.05
  3. 3GPT-5.6 LunaOpenAI$528.12
  4. 4Qwen3.8 FlashAlibaba$671.69
  5. 5Gemini 3.5 Flash-LiteGoogle$890.25
  6. 6DeepSeek V4.1 FlashDeepSeek$1,416
  7. 7Gemini 3.8 FlashGoogle$1,875
  8. 8Qwen3.7 PlusAlibaba$1,888
  9. 9Claude Haiku 4.5Anthropic$2,501
  10. 10Grok 4.3xAI$4,160
  11. 11Claude Sonnet 5.5Anthropic$5,001
  12. 12Claude Sonnet 5Anthropic$5,001
  13. 13GPT-6.1 SolOpenAI$5,001
  14. 14GPT-6 SolOpenAI$5,001
  15. 15GPT-5.6 TerraOpenAI$5,281
  16. 16Gemini 3.1 ProGoogle$5,281
  17. 17DeepSeek V4 ProDeepSeek$5,862
  18. 18Grok 4.7xAI$8,881
  19. 19Qwen3.8 MaxAlibaba$8,881
  20. 20Claude Opus 5.5Anthropic$10,002
  21. 21GPT-5.6 SolOpenAI$10,002
  22. 22Claude Opus 5Anthropic$12,502
  23. 23Claude Fable 5.1Anthropic$25,005
  24. 24GPT-6 AstraOpenAI$25,005

How the LLM Summarization Cost Calculator works

cost = (fresh input × input rate + cached input × cache rate + (output + reasoning) × output rate) ÷ 1,000,000

This page uses the same method as the LLM API Cost Calculator, which explains each term and assumption in full.

Worked example

20,000 documents a day of about 4,500 words each, cut to 350-word summaries. Nobody waits on a summary, so the work runs through the batch API at half price. Priced on Claude Haiku 4.5 through the batch API.

  1. 1Each request: 6,000 input tokens, 467 output
  2. 2Input costs $0.003 and output $0.00117, so $0.00417 a request
  3. 3× 600,000 requests over 30 days = $2,501

$2,501 a month on Claude Haiku 4.5. The cheapest listed model, Claude Haiku 5.5, would cost $250.05.

Frequently asked questions

Why use the batch API for summarization?

Summaries are rarely needed in seconds, and every provider listed takes 50% off batch requests, which return within 24 hours. For a steady daily volume, submitting the day's documents as one batch halves the bill with no change to the prompts.

Does summary length or document length matter more?

Document length, usually. Input is cheaper per token, but a 4,500-word document is about 6,000 tokens against 470 for a 350-word summary. On most models here that makes input more than half the cost, so trimming boilerplate from documents before sending them pays off.

Sources

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