Gemini 4 Argon
google/gemini-4-argon
Google's flagship reasoning model for complex software engineering, deep math and science, and autonomous multi-step agents. Takes text, images, files, audio, and video in one request.
RoleThe brain when inputs are not just text
$2
In / M
$10
Out / M
1.0M
Context
- AA intelligence index
- 50.4
- Inputs
- audio, file, image, text, video
- Outputs
- text
- Tools
- Listed
- Structured output
- Listed
- Reasoning
- Listed
- Weights
- Not verified
- Listed
- Oct 13, 2026
01 · What it's good at
Use it for this.
Three jobs this model is built for, and the ones to give to something else.
01
Reasoning over mixed media: video, audio, screenshots, and PDFs together
02
Complex software engineering and multi-step agents
03
Math and science problems that need a long chain of reasoning
Skip it for
- Cheap bulk work. Gemini 3.8 Flash covers most of the same inputs for less
- Word-level timestamps. Use a transcription model
02 · Example tasks
What you'd actually send it.
Real inputs, not a benchmark. Paste one into the snippet below to try it.
You send
Here is a 40-second screen recording of the checkout bug and the component source. Describe what the user did, where it breaks, and the likely fix.
Why this model
Video input means no one has to write the repro steps by hand.
03 · Your stack
Where it sits: Plan & reason.
Make it the reasoning step when your pipeline receives recordings, screenshots, or long documents. Send media straight in instead of chaining OCR or transcription first, and keep a dedicated transcription model only where you need timestamps.
Layer 01
Retrieve
Layer 02 · this model
Plan & reason
Layer 03
Do the volume
Layer 04
Gate & route
Layer 05
Draw, speak, listen
04 · Wire it up
One request. Paste and run.
Uses OpenRouter, so one key works for every model on this site. Set OPENROUTER_API_KEY and swap the prompt for one of the tasks above.
// OpenRouter speaks the OpenAI Chat Completions format.
const res = await fetch("https://openrouter.ai/api/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.OPENROUTER_API_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"model": "google/gemini-4-argon",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "This screenshot shows our checkout error. What is the user seeing, and what is the most likely cause in a React app?"
},
{
"type": "image_url",
"image_url": {
"url": "https://example.com/checkout-error.png"
}
}
]
}
],
"reasoning": {
"effort": "high"
}
}),
});
const data = await res.json();
console.log(data.choices[0].message.content);
05 · Watch out for
Gotchas.
Effort levels on this listing are low, medium, and high only.
Audio and video inputs are large. Watch
usagein the response before you run it on a backlog.
Sources
Lessons are written from each model's listing and the linked docs. We don't publish our own benchmark numbers; the index above is Artificial Analysis, as shown on OpenRouter.
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