Models Model details

Meta Llama

Meta: Llama 4 Scout

meta-llama/llama-4-scout

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input (text and image) and multilingual output (text and code) across 12 supported languages. Designed for assistant-style interaction and visual reasoning, Scout uses 16 experts per forward pass and features a context length of 10 million tokens, with a training corpus of ~40 trillion tokens. Built for high efficiency and local or commercial deployment, Llama 4 Scout incorporates early fusion for seamless modality integration. It is instruction-tuned for use in multilingual chat, captioning, and image understanding tasks. Released under the Llama 4 Community License, it was last trained on data up to August 2024 and launched publicly on April 5, 2025.

Input
$0.1

/ 1M tokens

Output
$0.3

/ 1M tokens

Context window
1.31M

tokens

Limits

Context

1,310,720

tokens

Max output

16,384

1.3% of the context window

Parameters

108,641,793,536

Model facts

Author

Meta Llama

Tokenizer

Llama4

Modality

Text + Image -> Text

License

Other

Knowledge cutoff

2024-08-31

Created

Apr 5, 2025

Hugging Face

meta-llama/Llama-4-Scout-17B-16E-Instruct

Publication status

Hugging Face: verifiedLicense: publishedParameter count: published

Modalities

Input · TextInput · ImageOutput · Text

Generation controls

Frequency penaltyLogit biasMax tokensMin pPresence penaltyRepetition penaltyResponse formatSeedStopStructured outputsTemperatureTool choiceToolsTop kTop p

Capability scores

0.5
Agentic index

Better than 1% of models compared

8.2
Coding index

Better than 2% of models compared

6.5
Intelligence index

Better than 6% of models compared

Creative tasks

Category performance

Data visualization
Top 97%

896 Elo

39.3% win

Games
Top 100%

774 Elo

27.4% win

Code
Top 100%

791 Elo

26.6% win

UI components
Top 100%

767 Elo

25.5% win

Websites
Top 100%

752 Elo

22.7% win