Google: Gemini 4 Argon API Pricing
Google: Gemini 4 Argon costs $2.00 per 1M input tokens and $10.00 per 1M output tokens (cache reads: $0.100). Context window: 1.0M. Prices verified 2026-10-21 · source
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From our own daily snapshot archive — independent of provider announcements.
Intelligence measures reasoning/knowledge/coding benchmarks. A high score with a low blended price = value pick; check the value chart to see where this model lands.
Token economics
| Token type | Price | vs base input |
|---|---|---|
| Standard input | $2.00 | — |
| Cache read | $0.100 | −95% |
| Cache write (5m) | $0.188 | -91% |
| Output | $10.00 | — |
| Web search tool | $14/1k searches | — |
Prompt caching stores repeated context server-side: reads cost far less, but writes carry a one-time premium.
Higher effort = deeper thinking = more reasoning tokens billed as output.
What would Google: Gemini 4 Argon cost you per month?
| Workload | Requests/mo | Avg tokens (in/out) | Est. monthly cost |
|---|---|---|---|
| Light chatbot | 10,000 | 800 / 400 | $56.00 |
| Coding agent | 5,000 | 8,000 / 2,000 | $180.00 |
| RAG pipeline | 30,000 | 4,000 / 500 | $390.00 |
| Doc summarizer | 2,000 | 20,000 / 1,000 | $100.00 |
Estimates use list prices without prompt caching. Caching can cut input cost by up to 90% on repeated context.
About Google: Gemini 4 Argon
Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across...
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Frequently asked questions
How much does Google: Gemini 4 Argon cost?
Google: Gemini 4 Argon costs $2.00 per 1M input tokens and $10.00 per 1M output tokens; cached input tokens cost $0.100 per 1M. Verified 2026-10-21 — provider source.
What is the context window?
1.0M tokens, with up to 944K output tokens per response. That's roughly 1311 pages of text in a single request.
Does it support prompt caching?
Yes — cached input is $0.100/1M, about 95% cheaper than standard input. Agents and RAG workloads with repeated context benefit most.
What's a cheaper alternative?
ScaleDown: Summarize saves about 99% on a typical workload with comparable quality.
Official resources
- Google: Gemini 4 Argon on OpenRouter — live endpoint, pricing source of record.
- Artificial Analysis leaderboard → — independent intelligence and performance rankings.
- Gemini docs → — first-party model specifications and limits.
- LMArena leaderboard → — independent human-preference rankings.
- Shred your current bill — paste a CSV and see if switching saves money.