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High Llama 4 Scout, a 17B active parameter multimodal model with 109B total parameters and a 10M token context, delivers state-of-the-art performance in its class, outperforming Gemma 3 and Gemini 2.0 Flash-Lite on coding, reasoning, long context, and image benchmarks, with an Intelligence Index of 13.5. Reasoning

Llama 4 Scout

Llama 4 Scout is a powerful general-purpose AI model with 17 billion active parameters across 109 billion total, delivering state-of-the-art multimodal performance on text, images, coding, and reasoning tasks. Featuring an industry-leading 10 million token context window and efficient single-GPU deployment, it excels in multi-document summarization, long-context analysis, and precise visual grounding.

10M Context
High Llama 4 Scout, a 17B active parameter multimodal model with 109B total parameters and a 10M token context, delivers state-of-the-art performance in its class, outperforming Gemma 3 and Gemini 2.0 Flash-Lite on coding, reasoning, long context, and image benchmarks, with an Intelligence Index of 13.5. Intelligence
Aug '24 Knowledge

Available for Chat, Vision, and File Uploads.

Performance Benchmarks

MMLU
79.6%
MATH
50.3%
HumanEval
81.1%

How do you want to interact?

Start a Conversation

Ask anything.
Have a natural conversation, brainstorm ideas, draft emails, or ask for advice.

Start Chatting

Use a Persona

Specialized Experts.
Instruct the AI to act as a Coding Tutor, Marketing Expert, or Travel Guide.

Pick a Persona

Why use Llama 4 Scout?

Native Multimodality

Processes text and image inputs natively via early fusion for tasks like image analysis and visual question answering

10M Token Context

Supports up to 10 million token context length for long-context tasks like multi-document summarization and vast codebase reasoning

Advanced Code Generation

Excels in coding benchmarks with strong performance on reasoning over extensive codebases

Capability Examples

Long Context Summarization
Summarize this 5 million token codebase for key functions and bugs.
After analyzing the entire codebase in one prompt, the main functions are userAuth() with SQL injection vulnerability at line 2.4M, paymentProcessor() optimized but with race condition at 4.1M; 12 minor bugs fixed recommendations provided.
Multimodal Image Analysis
Describe this chart image and answer: What's the trend in Q1 sales vs forecast?
The bar chart shows Q1 sales at 120K units (green bars), exceeding forecast of 105K (blue dashed line) by 14%; upward trend from Jan (35K actual vs 30K forecast) to Mar (50K vs 45K), indicating strong overperformance.

How to use

1
Go to Chat

Navigate to the "AI Chat" page.

2
Select Model

Ensure Llama 4 Scout is selected.

3
Type Prompt

Ask a question or paste code.

4
Interact

Refine the answer by replying to the AI.

Compare LLMs Side-by-Side

Is Llama 4 Scout better than Claude 3.5 or Gemini? Test same prompts simultaneously in the Chat Playground.

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