# Llama 4 Scout: API price, context window and how to connect

Llama 4 Scout by Meta: $0.1 per million input tokens, $0.3 per million output tokens, a 1.31M-token window. What it is good for, what to compare it with and how to connect it in SKILLOK.

## In short

Meta's mixture-of-experts model: 17 of 109 billion parameters active, with native image understanding. A 1.3-million-token context window and open weights.

## Specifications

| | |
|---|---|
| Provider | [Meta](https://skillok.org/docs/en/ai-models/providers/meta) |
| Released | 2025-04-05 |
| Context window | 1.31M tokens |
| Accepts | text, images |
| Answers with | text |
| Reasoning mode | no |
| Tool calling | yes |
| Open weights | yes |
| Knowledge up to | 2024-08 |
| Identifier | `meta-llama/llama-4-scout` |

## Price

| Per 1M tokens | In dollars |
|---|---|
| Input (question, context) | $0.1 |
| Output (answer) | $0.3 |

For reference: a conversation of 20 questions of ~500 tokens and answers of ~800 tokens costs about **$0.006**. Models in reasoning mode spend more tokens on an answer than the text shows: the reasoning is paid for too.

## What it is good for

* Very long context cheaply
* Running on your own hardware
* Images as input

## Similar models

Catalog models with the same kind of output, closest in the price of a typical request.

| Model | Input | Output | Window | Price |
|---|---|---|---|---|
| [Llama 3.3 70B Instruct](https://skillok.org/docs/en/ai-models/models/llama-3-3-70b-instruct) | $0.1 | $0.32 | 131K | about the same |
| [DeepSeek V4 Flash 0423](https://skillok.org/docs/en/ai-models/models/deepseek-v4-flash) | $0.091 | $0.182 | 1.05M | 1.4× cheaper |
| [GPT-6 Luna](https://skillok.org/docs/en/ai-models/models/gpt-6-luna) | $0.1 | $0.5 | 1.05M | 1.4× more expensive |
| [GPT-6 Luna Pro](https://skillok.org/docs/en/ai-models/models/gpt-6-luna-pro) | $0.1 | $0.5 | 1.05M | 1.4× more expensive |
| [Claude Haiku 5.5](https://skillok.org/docs/en/ai-models/models/claude-haiku-5-5) | $0.1 | $0.5 | 1M | 1.4× more expensive |

## Other Meta models

* [Llama 4 Maverick](https://skillok.org/docs/en/ai-models/models/llama-4-maverick)
* [Llama 3.3 70B Instruct](https://skillok.org/docs/en/ai-models/models/llama-3-3-70b-instruct)

## How to connect in SKILLOK

Llama 4 Scout works through OpenRouter: add an OpenRouter key in **Settings → AI** and pick the `meta-llama/llama-4-scout` model. It then becomes available in Space AI, in tasks and to the repository agent. In detail — [Connecting a model in SKILLOK](https://skillok.org/docs/en/ai-models/connecting-in-skillok).

## Where the data comes from

Prices, the context window and capabilities come from the public OpenRouter catalog as of 2026-10-19. A provider may change the price — the exact figure is always on the model's OpenRouter page.

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https://skillok.org/docs/en/ai-models/models/llama-4-scout