Gemini 4 Argon API pricing

By the aitokensai editors · Verified 2026-10-31 Sources: Gemini API pricing · Gemini API docs

Google's first Gemini 4 tier to reach the gateway. Available at listed at platform rate (no published official USD price).

USD, standard tier. Rate read from the live rate config on 2026-10-31. Google publishes no USD list price for this model, so nothing here is cross-checked against one — this is a gateway rate, not a discount.

Specifications

What Gemini 4 Argon costs at your volume

Calculated at $1 input / $5 output per 1M tokens, 5.0× apart on this model, assuming a 5:1 input-to-output ratio. That ratio is the single largest source of error in this table: resend-heavy workloads drift well past it, and at 5.0× the correction is not linear. The cost calculator takes the actual figures.

When Gemini 4 Argon is the right call

At $1 in and $5 out per million tokens, Gemini 4 Argon lands where most production deployments actually live — cheap enough to leave running, expensive enough that you still look at the bill. The 5.0× output multiple is conventional, so standard cost intuition holds: output dominates, context matters, neither is pathological.

It is not the answer at either end. Bulk classification finds identical results several times cheaper, and the hardest reasoning problems reward moving up rather than staying here. Everything in between is this model's ground.

Where Gemini 4 Argon sits in the index

Gemini 4 Argon is 151 of 226 on input rate and 162 of 226 on output rate. A gap of 11 places, because on this model the output rate is 5.0× the input rate, steeper than the index median of 4.0× An output-heavy workload therefore leaves it almost where it started, which is why the cheapest model per input token is routinely not the cheapest per task. Percentile is the share of the 226 models in this index that cost more than Gemini 4 Argon.

On an agent mix the lowest blended rate in the same index is Gemma2 9B It at $0.01 per 1M tokens, about 400.0× less than this model at the same ratio. Across the whole index, sorting by input price alone moves a model by as much as 74 places; on Gemini 4 Argon specifically the shift is 11 — which is what the re-ranked index exists to show.

Gemini 4 Argon is 151 of 226 across every model in the index; the comparison that usually settles a choice is Google's own lineup further down.

Gemini 4 Argon among Google's 4 models

Gemini 4 Argon is the most expensive of Google's 4 models in this index at $1 per 1M input tokens. The range inside that group runs from Gemini 2.5 Flash at $0.15 to Gemini 4 Argon at $1 (a spread of 7×, and this is the top of it) Gemini 2.5 Flash does the same job at 1/7 of the input rate.

On input rate alone, dropping to Gemini 2.5 Pro cuts it by 38%. Both measured at 2026-10-31, the same check as every other rate on this page.

Full Google range, including models outside this shortlist: Google rate index.

Similarly priced alternatives

Blended comparison (input + output per 1M). Price is one axis These are the three closest to Gemini 4 Argon in blended rate, not interchangeable substitutes for it.

Calling Gemini 4 Argon

The identifier you send is gemini-4-argon. Copy it exactly, because a mismatch returns a model error rather than a connection error, which sends most people to debug the wrong thing. Everything else is a base URL argument (Gemini 4 Argon's own SDKs work unchanged).

from openai import OpenAI

client = OpenAI(base_url="https://aicomp.ai/v1", api_key="sk-...")

resp = client.chat.completions.create(
    model="gemini-4-argon",
    messages=[
        {"role": "system", "content": "Answer in one short paragraph."},
        {"role": "user", "content": "Explain what changed in this release."},
    ],
    max_tokens=300,
)

# prompt_tokens / completion_tokens 是真实账单的唯一依据 —— 记下来，别猜
print(resp.usage.prompt_tokens, resp.usage.completion_tokens)

Log the usage object on every call, not just while you are optimising. On Gemini 4 Argon output costs 5.0× what input costs, so the same monthly token count bills very differently depending on how much of it comes back as output — measure your own split before extrapolating from the headline input rate, because rates move with vendor promotions.

Setting it up

Pointing an existing integration at Gemini 4 Argon is a base URL change, not a rewrite. Pick your tool:

• Claude Code — set the base URL via environment variables
• Cursor — override the API endpoint in settings
• OpenAI / Anthropic SDK — one base_url argument
• Python, sync, streaming and async examples
• Cutting the bill — eight levers ranked by effect

What different usage shapes cost on Gemini 4 Argon

A monthly token budget does not convert cleanly into a monthly invoice, and Gemini 4 Argon is an ordinary case rather than an extreme one: output is priced at 5.0× input, above the median of 4.0× across the 226 models in our index. Everything follows from that one number At 5:1 it is manageable, at 1:1 the same token count costs half as much. Below are five shapes real Gemini 4 Argon deployments fall into, each costing it out at the split it actually produces.

The cheapest comparison is GPT-5 nano at $0.025 / $0.2 per 1M — 26.7× cheaper than Gemini 4 Argon on a blended rate. Treat it as a rate floor, not a recommendation: it says nothing about whether GPT-5 nano can do the work you need Gemini 4 Argon for.

Read the table as a sanity check rather than a forecast. If your own output share sits anywhere near 3%, everything above is priced almost entirely at Gemini 4 Argon's $1 input rate; at 40% the $5 output rate has taken the larger half. Nobody's split is exactly the one you assumed when you picked the model, which is why the calculator asks for it before quoting anything.

FAQ

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