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How to Start Building with Gemini 4 Argon API

Step-by-step guide to Gemini 4 Argon in the Gemini API and Android Studio: setup, thinking levels, long decode, and coding examples.

October 14, 2026 Yum News

Gemini 4 Argon became generally available on October 13, 2026. Google’s newest frontier model targets complex, multi-step work in software engineering, enterprise knowledge tasks, and cybersecurity defense. It supports a unified 1,048,576-token input-output window and long decode up to the remaining context.

Developers can call it today on the paid Gemini API tier in Google AI Studio and Android Studio. Google AI Ultra subscribers also get it in Google Antigravity. This guide shows the official setup path and practical patterns from the Gemini API docs.

Developer workspace with code and AI tools

What Gemini 4 Argon changes

Argon always thinks. You control depth with thinking levels: low, medium (default), or high. Medium balances quality and cost for most coding and agentic tasks. High is for hard math, multi-file debugging, or architecture work. Low keeps latency and token use down for classification or extraction.

Synchronous requests default to 65,536 output tokens. Long decode (continuation) lets you go up to the shared 1M limit minus input tokens. Inputs accept text, images, video, audio, and PDF. Output is text only. Capabilities include function calling, code execution, search grounding, structured outputs, and computer use (preview).

Introductory API pricing through November 30, 2026 is $2 per million input tokens and $10 per million output tokens (thinking tokens included). Cached input is $0.10. From December 1 the rates become $4 and $20. Argon is not available on the free tier or Google AI Pro.

Step 1: Get API access

Sign in to Google AI Studio. Enable billing for the Gemini API paid tier. Argon appears under the model list as gemini-4-argon.

Create an API key in the AI Studio settings or the Google Cloud console. Store it in an environment variable (GEMINI_API_KEY). Do not embed keys in client code.

Install or update the official SDK:

pip install -U "google-genai>=2.30.1"

For JavaScript:

npm install "@google/genai@>=2.29.1"

Step 2: Make your first call

The recommended starting configuration uses thinking level medium and a 64K output limit.

Python example from the official docs:

from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
    model="gemini-4-argon",
    contents="Write a three.js script that renders a realistic 3D black hole with gravitational lensing.",
    config=types.GenerateContentConfig(
        max_output_tokens=65536,
        thinking_config=types.ThinkingConfig(thinking_level="medium"),
    ),
)
print(response.text)

The same pattern works in JavaScript and cURL. For multi-turn conversations or function calling, keep full conversation history including thinking signatures so the model retains context.

Abstract visualization of neural networks and AI processing

Step 3: Use long decode for complex outputs

When you need more than 64K tokens (full multi-file diffs, long proofs, or large generated projects), enable long decode. The Google Gen AI SDK and Agent Studio handle the continuation token automatically on the GenerateContent API (v1beta1).

Set a higher max_output_tokens (for example 262144) and thinking_level="high". Background execution or longer HTTP timeouts (15+ minutes) help on hard prompts. Official docs cover reconnection and token counting details.

Step 4: Integrate in Android Studio

Android Studio supports Gemini models for code completion, Agent Mode, and direct API calls. Log in with a Google account that has paid Gemini API access. Android Studio detects your plan and offers Argon in the model picker for Agent Mode and code assistance.

For app code that calls the model at runtime, use Firebase AI Logic (same pattern as Gemini 3.8 Flash). Enable AI Logic in the Firebase console, add the Firebase AI SDK, and initialize:

val model = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel("gemini-4-argon")

Protect production traffic with App Check. See the related guide on adding Gemini 3.8 Flash with Firebase AI Logic for the full App Check and Gradle setup.

Google AI Ultra subscribers can also select Argon inside Google Antigravity for multi-agent planning and /goal runs.

Tips for effective use

Start with medium thinking. Move to high only after medium fails on a task you can verify.
Prefer server-side or Firebase AI Logic calls so keys stay off the device.
Monitor token usage. Output (including thinking) is billed at the higher rate. Implicit caching discounts repeated input by 95%.
For long-horizon Android work, compare results against Android Bench 2.0 long-horizon tasks. Frontier models still leave substantial room on multi-day engineering jobs.
Test structured outputs and function calling early. Argon supports them; sampling parameters such as temperature are ignored.
Increase client timeouts for high thinking or long decode. Hard prompts can take several minutes.

Conclusion

Gemini 4 Argon is available now for paid API users and eligible subscribers. The model ID is gemini-4-argon, thinking defaults to medium, and the shared context window is 1M tokens. Setup takes an API key, an SDK update, and one generateContent call.

Use the introductory pricing window through November 30, 2026, then re-evaluate after the rate change. Official Gemini API documentation and the model card remain the source of truth for limits, continuation, and safety details.