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Gemini and GemmaGoogleClosed weights

Gemini 4 Argon.

Gemini 4 Argon is the reasoning model of Google's Gemini 4 generation. It is built for long, multi-step tasks such as software engineering, mathematical and scientific problems and autonomous agents, with reasoning at three levels, input as text, image, audio, video and PDF, and a context of one million tokens.

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base_url="https://api.noirdoc.de/v1" model="gemini-4-argon"

  • Data centres in the EU
  • § 203 confidentiality
  • Zero retention
  • List prices

Details.

Where the model runs, which rules apply and what it can do.

  1. Data centres in the EU Google Vertex Contracting party: Google Cloud EMEA Limited · based in Dublin, Ireland
§ 203 German Criminal Code (StGB)
Confidentiality agreed with Google Vertex
Weights
Closed weights
Features
Tools, Vision, Reasoning and JSON
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Map: Gemini 4 Argon runs in data centres in the EU (Google Vertex).

Providers.

We offer Gemini 4 Argon through these providers.

Provider § 203 model=
€ · 1M tokens Input Cache Read Cache Write Output
Google Vertex EU EU data residency via Vertex AI
§ 203 agreed
gemini-4-argon
Input 2.00 Cache Read 0.10 Output 9.90

More models.

Google develops the proprietary Gemini series and publishes open models from the same line of research with Gemma.

All Gemini and Gemma models →
Model
€ · 1M tokens Input Cache Read Cache Write Output
Gemini 3.8 Flash Tools Vision Reasoning JSON Google's Flash model of the current Gemini generation — built for speed and high throughput. Google Vertex Logo Google Vertex · EU
Input 1.49 Cache Read 0.149 Output 7.41
Gemini 3.5 Flash Lite Tools Vision Reasoning JSON Compact Flash variant for simple, very frequent tasks — tuned for low latency. Google Vertex Logo Google Vertex · EU
Input 0.30 Cache Read 0.03 Output 2.47
Gemini 3.1 Flash Lite Tools Vision Reasoning JSON Earlier Flash-Lite generation — stable for existing integrations and simple high-volume tasks. Google Vertex Logo Google Vertex · EU
Input 0.25 Cache Read 0.025 Output 1.49

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python · openai sdk api.noirdoc.de/v1 
import os
from openai import OpenAI

client = OpenAI(
    # one line, that's all
    base_url="https://api.noirdoc.de/v1",
    api_key=os.environ["NOIRDOC_API_KEY"],
)

response = client.chat.completions.create(
    model="gemini-4-argon",  # provider & model
    messages=[{"role": "user", "content": "Hello"}],
)