> ## 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.

# GPT-6 Luna

> OpenAI's most efficient GPT-6 model, for focused, high-volume tasks. 1.05M context, 128K output, text, image and file input, reasoning; above 272K prompt tokens input doubles and output rises 1.5×.

## Overview

OpenAI's most efficient GPT-6 model, for focused, high-volume tasks. 1.05M context, 128K output, text, image and file input, reasoning; above 272K prompt tokens input doubles and output rises 1.5×.

## Specs

| Field | Value |
| - | - |
| Model ID | `gpt-6-luna` |
| Best for | High-volume chat, classification and lightweight agents at long context |
| Context window | 1.05M |
| Max output tokens | 128K |
| Input modalities | Text, Image, File |
| Output modalities | Text |
| Tool calling | Yes |
| Structured outputs | Yes |
| Prompt caching | Yes |
| Vision | Yes |
| Reasoning | Yes |
| Speed | Fast |
| Cost band | Cheap |
| Release stage | Stable |

## Pricing

| | Per 1M tokens |
| - | :-: |
| Input | \$0.1639 |
| Output | \$0.8195 |

Above a long prompt this model costs more per token, because the infrastructure
serving it charges more there. The threshold is on the **prompt**, so you know which
rate applies before you send.

| Prompt length | Input | Output |
| - | :-: | :-: |
| Standard | \$0.1639 | \$0.8195 |
| From 272,001 tokens | \$0.27 | \$1.0125 |

## 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="gpt-6-luna",
    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=strong"
```


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