> ## Documentation Index
> Fetch the complete documentation index at: https://docs.kymaapi.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Gemini 4 Argon

> Google's flagship reasoning model, built for complex software engineering, math and science problems, and autonomous multi-step agents. 1M context, 64K output, text, image, audio, video and file input, reasoning, tool calling, structured outputs.

## Overview

Google's flagship reasoning model, built for complex software engineering, math and science problems, and autonomous multi-step agents. 1M context, 64K output, text, image, audio, video and file input, reasoning, tool calling, structured outputs.

## Specs

| Field | Value |
| - | - |
| Model ID | `gemini-4-argon` |
| Best for | Hard reasoning, software engineering and long-horizon agentic work |
| Context window | 1.048576M |
| Max output tokens | 66K |
| Input modalities | Text, Image, Video, File, Audio |
| Output modalities | Text |
| Tool calling | Yes |
| Structured outputs | Yes |
| Prompt caching | Yes |
| Vision | Yes |
| Reasoning | Yes |
| Speed | Medium |
| Cost band | Premium |
| Release stage | Stable |

## Pricing

| | Per 1M tokens |
| - | :-: |
| Input | \$2.70 |
| Output | \$13.50 |

Promotional rate, promised through **2026-11-30**. The list rate it returns to is \$5.40 input and \$27.00 output per 1M tokens.

## Example

```python theme={null}
from openai import OpenAI

client = OpenAI(base_url="https://kymaapi.com/v1", api_key="kyma-...")

response = client.chat.completions.create(
    model="gemini-4-argon",
    messages=[{"role": "user", "content": "..."}],
    tools=[...],  # function calling supported
)
```

## Agent query example

Ask the API which models fit, instead of hardcoding an id:

```bash theme={null}
curl "https://kymaapi.com/v1/models?recommended_for=chat&tools=true&quality_tier=frontier-open"
```


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