★ Anthropic · Frontier

Claude Opus 5.5 API Pricing Calculator

$4 per 1M input tokens, $20 per 1M output. Complex coding, multi-step agents and analysis where you need frontier quality at well under top-tier prices.

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Updated 7 Nov 2026

Price card · Claude Opus 5.5

Anthropic

Input: $4 / 1M
Output: $20 / 1M
Cached input: $0.20 / 1M
Batch: 50% off
Context window: 1M tokens
Tier: Frontier

Workload

Requests per day

Avg input tokens

tok

Avg output tokens

tok

Cached input

Repeated system prompt or docs

Batched requests

Jobs that can wait hours

Models

Anthropic

OpenAI

Google

Mistral

DeepSeek

$ per 1M tokens, input / output

Monthly cost · 30 days

Cheapest: $2,100; Claude Sonnet 5.5
Priciest: $4,200; Claude Opus 5.5
Spread: 2.0×; priciest vs cheapest

	Per request		Per 1K users / mo
Claude Sonnet 5.5Cheapest	$0.00700	$2,100	$1,050
GPT-6.1 SolCheapest	$0.00700	$2,100	$1,050
Claude Opus 5.5	$0.0140	$4,200	$2,100

Per 1K users assumesrequests per user per day.

Where the money is

If 60% of your traffic is simple enough for Claude Sonnet 5.5, routing it there instead of Claude Opus 5.5 saves about $1,260/month.

Caching half your input on Claude Opus 5.5 (a shared system prompt or document) would cut about $1,140/month.

You're looking at $2,100–$4,200/month.

Most AI products I audit can cut that 30–60% with routing, caching and shorter prompts.

Get a cost-cutting audit

What Claude Opus 5.5 costs in practice

List prices per million tokens are hard to reason about. Here is what common request types cost on Claude Opus 5.5, with no caching or batch discount applied.

Claude Opus 5.5 cost by request type
Request type	Tokens in / out	Per call	Per 1K calls	Per 1M calls
Classification or routing call	400 / 10	$0.00180	$1.80	$1,800
Support chat reply	1,500 / 250	$0.0110	$11.00	$11,000
RAG answer over your docs	6,000 / 400	$0.0320	$32.00	$32,000
Long document summary	40,000 / 1,000	$0.1800	$180	$180,000

Pricing notes

Complex coding, multi-step agents and analysis where you need frontier quality at well under top-tier prices.

Fast mode is billed separately at $8 input / $40 output per 1M tokens.
US-only inference is billed at 1.1x.

Source: platform.claude.com, pricing. Verified 7 Nov 2026.

Compare

Or compare all 14 models in the full calculator.

How the math works

Every provider bills input tokens (your prompt, system prompt and retrieved documents) and output tokens (the reply) separately, per million. Output is usually 4–5× the input price, so chatty apps are output-heavy and RAG apps are input-heavy.

cost per request = (fresh_in × input_price

+ cached_in × cached_price

+ out × output_price) / 1,000,000

× (1 − batched_share × batch_discount)

monthly = cost per request × requests per day × 30

Two adjustments are applied automatically. Introductory prices (currently on some Gemini models) are used until their end date. Long-prompt surcharges kick in when your average input crosses the provider's threshold: 272K tokens on current OpenAI models, 100K on Claude Haiku 5.5.

Not modelled: cache-write premiums, priority or fast tiers, regional surcharges, image and audio tokens, and reasoning tokens a model spends before answering (they bill as output, so add them to your output estimate if you use extended thinking).

Claude Opus 5.5 questions

How much does Claude Opus 5.5 cost per 1M tokens?

Claude Opus 5.5 costs $4 per 1M input tokens and $20 per 1M output tokens, with cached input at $0.20 per 1M. Batch requests get 50% off.

What is the Claude Opus 5.5 context window?

1M tokens, with up to 128,000 output tokens per request. A large window does not mean you should fill it: every token you send is billed, so retrieve only what the request needs.

Is Claude Opus 5.5 cheaper than Claude Sonnet 5.5?

For a typical request of 2,000 input and 300 output tokens, Claude Sonnet 5.5 is about 2.0× cheaper ($0.0140 vs $0.00700 per call for Claude Opus 5.5 and Claude Sonnet 5.5). Price is only half the decision; test both on your own prompts.

What is prompt caching and should I use it?

When the start of your prompt (system prompt, tool definitions, a long document) repeats across requests, providers can serve it from cache at a fraction of the input price, often 90–99% off. If your requests share a large fixed prefix, caching is usually the single biggest saving available. Put the stable content first and the variable content last.

Why does my real bill differ from this estimate?

Usually because of tokens you are not counting: reasoning or thinking tokens (billed as output), retries, tool-call round trips, and cache-write premiums. Compare the estimate against the usage page in your provider console for a week and adjust the averages.

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Fine print

Tools: on the house.
Engineer: by the hour.

Knowing your AI bill was free. Cutting it in half isn't, but I'm cheaper than the overspend.

I'm Himanshu, an AI and full-stack engineer. I build MVPs, AI agents and the growth plumbing around them for early-stage startups.

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