Anthropic Pricing & Token Cost

Type: official-doc Tier: 1 (Official Doc) Author(s): Anthropic Date: Accessed 2026-06-01 URL: https://platform.claude.com/docs/en/docs/about-claude/pricing Accessed: 2026-06-01

Why This Source Matters

Cost is the constraint that shapes every production LLM decision. This source gives the exact per-token prices and the formula to turn usage fields into dollars — the basis for the cost tracker built in Project 1. It also states the rule of thumb for estimating tokens from text.

Key Claims

Token estimation

• A token is a piece of text the model processes. Rough estimate: 1 token ≈ 4 characters ≈ 0.75 words in English. Exact count varies by language and content.
• Note: Opus 4.7+ use a new tokenizer that may use up to ~35% more tokens for the same text than earlier models.

Pricing is per million tokens (MTok), split input vs. output

Output is consistently ~5× the input price. Selected current rates (USD / MTok):

| Model | Input | Output | |-------|-------|--------| | Claude Opus 4.5 / 4.6 / 4.7 | $5 | $25 | | Claude Opus 4.1 | $15 | $75 | | Claude Sonnet 4.5 / 4.6 | $3 | $15 | | Claude Haiku 4.5 | $1 | $5 |

Cost formula

cost = (input_tokens  / 1_000_000) * input_price_per_mtok
     + (output_tokens / 1_000_000) * output_price_per_mtok

Worked example (Opus 4.8, one turn): 50,000 input + 15,000 output

• input: 50,000 × $5 / 1,000,000 = $0.25
• output: 15,000 × $25 / 1,000,000 = $0.375
• total ≈ $0.625

Why chatbots get expensive

• The whole conversation history is resent every turn (see anthropic-messages-api.md), so input_tokens grows each turn. A long conversation pays for the full history repeatedly.
• Prompt caching mitigates this: cache reads cost 0.1× the base input price (a 5-minute cache write costs 1.25×, a 1-hour write 2×). Reusing a large system prompt or history across calls pays off after one or two cache reads.

Volume reference

• ~10,000 support conversations at ~3,700 tokens each on Haiku 4.5 ≈ $37 total.

Relevant To

• concepts: [tokens, token-cost, cost-projection, prompt-caching, model-selection]
• projects: [01-ai-chatbot]

Notes

• Prices change; always re-verify against claude.com/pricing before quoting absolute numbers. The structure (per-MTok, input vs output ~1:5, history-resend cost growth) is the durable lesson.
• Cost-optimization hierarchy from the docs: pick the cheapest adequate model (Haiku→Sonnet→Opus), use prompt caching for repeated context, batch non-urgent work, monitor usage.