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USE1-Claude4.6Sonnet-input-tokens

Every token sent into a Bedrock model as part of the prompt (system instructions, chat history, retrieved context, few-shot examples) is metered and billed separately per model, distinct from output tokens.

AI & MLAmazon Bedrock

Bedrock input tokens (per model)

Verified against official AWS documentation last checked

Why it shows up

Input-token spend climbs fastest when prompts carry more context than the task needs: long system prompts, unpruned chat history, or retrieval that stuffs in whole documents instead of relevant chunks.

Because the usage type is per-model, a silent model switch or an A/B test can double the number of these line items without the aggregate number appearing to move.

How to cut it

  • Trim system prompts and cap retrieved context to what's relevant.
  • Truncate or summarize conversation history instead of resending the full transcript each turn.
  • Route repeated static context through prompt caching so it bills as a cache read, not full input.
  • Use a smaller, cheaper model for steps that don't need frontier-level reasoning.

How to read this code

USE1 / USE2

us-east-1 (N. Virginia) / us-east-2 (Ohio)

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Token spend moves every day.

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