★ Anthropic · Mid-tier

Claude Sonnet 5.5 API Pricing Calculator

$2 per 1M input tokens, $10 per 1M output. The default for production chat, RAG and coding assistants: strong quality at a mid-tier price.

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Updated 7 Nov 2026

Price card · Claude Sonnet 5.5

Anthropic

Input: $2 / 1M
Output: $10 / 1M
Cached input: $0.10 / 1M
Batch: 50% off
Context window: 1M tokens
Tier: Mid-tier

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: $2,100; Gemini 4 Argon
Spread: 1.0×; priciest vs cheapest

	Per request		Per 1K users / mo
Claude Sonnet 5.5	$0.00700	$2,100	$1,050
GPT-6.1 Sol	$0.00700	$2,100	$1,050
Gemini 4 ArgonIntro price	$0.00700	$2,100	$1,050

Per 1K users assumesrequests per user per day.

Where the money is

Caching half your input on Gemini 4 Argon (a shared system prompt or document) would cut about $570/month.

You're looking at ~$2,100/month.

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

Get a cost-cutting audit

What Claude Sonnet 5.5 costs in practice

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

Claude Sonnet 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	$0.00090	$0.9000	$900
Support chat reply	1,500 / 250	$0.00550	$5.50	$5,500
RAG answer over your docs	6,000 / 400	$0.0160	$16.00	$16,000
Long document summary	40,000 / 1,000	$0.0900	$90.00	$90,000

Pricing notes

The default for production chat, RAG and coding assistants: strong quality at a mid-tier price.

The full 1M context window is billed at the standard rate.

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 Sonnet 5.5 questions

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

Claude Sonnet 5.5 costs $2 per 1M input tokens and $10 per 1M output tokens, with cached input at $0.10 per 1M. Batch requests get 50% off.

What is the Claude Sonnet 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 Sonnet 5.5 cheaper than GPT-6.1 Sol?

For a typical request of 2,000 input and 300 output tokens they cost about the same ($0.00700 vs $0.00700 per call). Caching, batch support and output length will decide which is cheaper for you.

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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