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★ Google · Frontier

Gemini 4 Argon API Pricing Calculator

$2 per 1M input tokens, $10 per 1M output. Introductory price until 30 Nov 2026. Google's newest flagship. Large-context multimodal work, at an introductory price until the end of November 2026.

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  • Updated 7 Nov 2026

Price card · Gemini 4 Argon

Google

Input
$2 / 1M
Output
$10 / 1M
Cached input
$0.10 / 1M
Batch
50% off
Context window
1.05M tokens
Tier
Frontier

Workload

tok
tok
%

Repeated system prompt or docs

%

Jobs that can wait hours

Models

Anthropic
OpenAI
Google
Mistral
DeepSeek

$ per 1M tokens, input / output

Monthly cost · 30 days

Cheapest
$2,100
Gemini 4 Argon
Priciest
$2,100
GPT-6.1 Sol
Spread
1.0×
priciest vs cheapest
Per 1K users / mo
Gemini 4 ArgonIntro price
$2,100$1,050
Claude Sonnet 5.5
$2,100$1,050
GPT-6.1 Sol
$2,100$1,050
Per 1K users assumesrequests per user per day.

Where the money is

Caching half your input on GPT-6.1 Sol (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
01

What Gemini 4 Argon costs in practice

List prices per million tokens are hard to reason about. Here is what common request types cost on Gemini 4 Argon, with no caching or batch discount applied.

Gemini 4 Argon cost by request type
Request typeTokens in / outPer callPer 1K callsPer 1M calls
Classification or routing call400 / 10$0.00090$0.9000$900
Support chat reply1,500 / 250$0.00550$5.50$5,500
RAG answer over your docs6,000 / 400$0.0160$16.00$16,000
Long document summary40,000 / 1,000$0.0900$90.00$90,000
02

Pricing notes

Google's newest flagship. Large-context multimodal work, at an introductory price until the end of November 2026.

  • Introductory price of $2 input / $10 output per 1M runs through 30 Nov 2026, then $4 / $20.
  • Caching is implicit only (95% discount on cache hits). Explicit caching is not supported, so hit rates are not guaranteed.

Source: ai.google.dev, gemini-4-argon, batch-api. Verified 7 Nov 2026.

03

Compare

Or compare all 14 models in the full calculator.

04

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).

05

Gemini 4 Argon questions

How much does Gemini 4 Argon cost per 1M tokens?
Gemini 4 Argon 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. This is an introductory price that runs until 30 Nov 2026; after that it is $4 input and $20 output.
What is the Gemini 4 Argon context window?
1.05M tokens, with up to 65,536 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 Gemini 4 Argon cheaper than Claude Sonnet 5.5?
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.
06

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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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