Intelligence
Index v5.0
Gemini 4 Argon overview
Gemini 4 Argon by Google
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. It accepts text and image input, returns text, and offers a 1,048,576-token context window with up to 65,536 tokens per response.
Gemini 4 Argon specs
- Model ID
google/gemini-4-argon- Developer
- Context window
- 1,048,576 tokens
- Max output
- 65,536 tokens
- Input
- Text, Image
- Output
- Text
- API
- OpenAI-compatible, base URL
https://api.aimlapi.com/v1
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. It is available on AI/ML API through the chat completions endpoint as google/gemini-4-argon.
Specifications
- Model ID: google/gemini-4-argon
- Context window: 1,048,576 tokens
- Capabilities: reasoning, parallel tool calling, vision, structured output, streaming
What Google reports
Google reports Argon ahead of competing models from OpenAI and Anthropic on several benchmarks, including long-video understanding. These are vendor-reported results and have not been independently reproduced.
Sources: blog.google, venturebeat.com
Use cases
Google describes Argon as built for agentic software engineering, including analysing fleet-wide profiling telemetry and migrating C/C++ codebases with agents running over long horizons.
It is also positioned for deep mathematical and scientific problem solving, and for autonomous multi-step agents that keep working across many sequential tool calls.
The 1,048,576-token context window suits long-document and multi-file analysis, and image input allows multimodal document and screenshot understanding.
Capabilities
- Function calling
- Reasoning
- Streaming
- Structured outputs
- Vision
Gemini 4 Argon benchmarks by 
Artificial Analysis
See how Gemini 4 Argon compares in intelligence, generation speed, cost per task and latency. Data retrieved Nov 3, 2026.
Benchmark results
Compare 5 models available on AI/ML API across 4 independent benchmarks.
Humanity's Last Exam
Expert-written questions across academic subjects.
Terminal-Bench 4.0
Agentic tasks completed in a command-line environment.
AA-LCR
Reasoning across long documents of about 100k tokens.
SciCode
Python coding tasks drawn from scientific research.
Gemini 4 Argon API pricing
| Usage | Price | Unit |
|---|---|---|
| Input | $2 | 1M tokens |
| Output | $10 | 1M tokens |
| Cached input | $0.1 | 1M tokens |
Prices from the AI/ML API price list, checked on Nov 4, 2026.
Gemini 4 Argon vs other models: token prices
AI/ML API input and output rates for 5 models, per million tokens.
| Model | Input | Output |
|---|---|---|
 Gemini 4 Argon | $2 | $10 |
| Gemini 3.5 Flash Lite | $0.3 | $2.5 |
| Gemini 3.8 Flash | $0.75 | $3.75 |
| Gemini 3.7 Flash | $0.75 | $3.75 |
| Gemini 3.6 Flash | $1.5 | $7.5 |
How to use the Gemini 4 Argon API
View docs
Build with your agent
Give your agent the model details, code and documentation.
Paste it into your coding agent to build the integration.
1Get your API key
Create API keyexport AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"2Make your first request
curl 'https://api.aimlapi.com/v1/chat/completions' \
-H "Authorization: Bearer $AIMLAPI_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "google/gemini-4-argon",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}'import os
import requests
headers = {
"Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
"Content-Type": "application/json",
}
payload = {
"model": "google/gemini-4-argon",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}
response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"model": "google/gemini-4-argon",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}),
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());Models similar to Gemini 4 Argon
Gemini 4 Argon FAQ
Is Gemini 4 Argon available on AI/ML API?
Yes. Gemini 4 Argon is live on AI/ML API and callable with the model id google/gemini-4-argon on the chat completions endpoint, using the same API key as every other model on the platform.
How many tokens can Gemini 4 Argon return in one response?
Up to 65,536 tokens per response, within a 1,048,576-token context window.
How much does Gemini 4 Argon cost?
Gemini 4 Argon costs $2 input and $10 output per 1M tokens on AI/ML API, billed as you go with no subscription.
What is the context window of Gemini 4 Argon?
Gemini 4 Argon has a 1,048,576-token context window and can return up to 65,536 tokens in one response.
What is Gemini 4 Argon best suited for?
Google positions Argon for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. It cites agents analysing fleet-wide profiling telemetry and migrating C/C++ codebases over long horizons. The 1M-token context window also suits long-document and multi-file analysis.
How do I get access to Gemini 4 Argon?
Create an AI/ML API account, generate a key in Key Management and call google/gemini-4-argon on https://api.aimlapi.com/v1/chat/completions. No separate Google access is needed.
Are the benchmark claims for Gemini 4 Argon independent?
No. Google reports Argon ahead of competing models from OpenAI and Anthropic on several benchmarks, including long-video understanding. Those results are vendor-reported and have not been independently reproduced.
What is the Gemini 4 Argon model ID?
Use google/gemini-4-argon as the model parameter. Requests go to the OpenAI-compatible endpoint with your AI/ML API key.