About
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.
Pricing
| Input | $1.40 | per 1M prompt tokens |
|---|---|---|
| Output | $7.00 | per 1M generated tokens, reasoning included |
| Cached input | $0.07 | per 1M tokens read back from the prompt cache |
| Cache write | $0.26 | per 1M tokens written to the prompt cache |
| Web search | $0.020 | per request that uses the provider’s built-in search |
50% off right now. The provider is running a promotion, and the developer API bills what the provider actually charged for your call plus our flat markup, so it reaches your bill with nothing to claim. The “from” figure is the cheapest endpoint serving the model; which one answers is the provider’s routing decision, so treat it as a floor. Promotions can end at any time.
What a request costs
| Typical request | Each | Per 1,000 |
|---|---|---|
| Short chat reply1,500 in · 400 out | $0.0049 | $4.90 |
| Question over a long document40,000 in · 800 out | $0.062 | $61.60 |
| Agent tool step20,000 in · 1,200 out | $0.036 | $36.40 |
At the developer-API rate, before any provider promotion or prompt-cache saving. In the app, the model picker shows what a message costs on your plan before you send it.
Benchmarks
Head-to-head rankings
- #63DModel arenaElo 139565% wins
- #6Game devModel arenaElo 137463% wins
- #7WebsitesModel arenaElo 132156% wins
- #8CodeModel arenaElo 133558% wins
- #11Data vizModel arenaElo 132857% wins
- #12UI componentsModel arenaElo 131955% wins
Scores from Artificial Analysis; bars are scaled to the best model on our list. Rankings from Design Arena head-to-head votes. Published by OpenRouter alongside its catalogue.
Call it from code
OpenAI-compatible: point any OpenAI SDK at https://api.privateer.pro/v1 and set
model. Keys are created in the app under Settings.
curl https://api.privateer.pro/v1/chat/completions \
-H "Authorization: Bearer $PRIVATEER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "google/gemini-4-argon:batch",
"messages": [{ "role": "user", "content": "Hello" }]
}'import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.privateer.pro/v1",
api_key=os.environ["PRIVATEER_API_KEY"],
)
reply = client.chat.completions.create(
model="google/gemini-4-argon:batch",
messages=[{"role": "user", "content": "Hello"}],
)
print(reply.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.privateer.pro/v1",
apiKey: process.env.PRIVATEER_API_KEY,
});
const reply = await client.chat.completions.create({
model: "google/gemini-4-argon:batch",
messages: [{ role: "user", content: "Hello" }],
});
console.log(reply.choices[0].message.content);Parameters this model accepts
include_reasoningmax_tokensreasoningreasoning_effortresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_p