Models / Meta
Chat
Vision

Llama 4 Scout

SOTA 109B model with 17B active params & large context, excelling at multi-document analysis, codebase reasoning, and personalized tasks.

About model

Together AI offers day 1 support for the new Llama 4 multilingual vision models that can analyze multiple images and respond to queries about them.

Register for a Together AI account  to get an API key. New accounts come with free credits to start. Install the Together AI library for your preferred language.

Performance benchmarks

Model

FrontierMath Tier 4

GPQA Diamond

HLE

SciCode

GDPval-AA

Terminal-Bench 2.1

Agent Arena

FrontierCode

DeepSWE

51.8%

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  • How to use model

    Input

        
          from together import Together
    
          client = Together()  # API key via api_key param or TOGETHER_API_KEY env var
    
          # Query image with Llama 4 Scout model
          response = client.chat.completions.create(
            model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
            messages=[{
                "role": "user",
                "content": [
                    {"type": "text", "text": "What can you see in this image?"},
                    {"type": "image_url", "image_url": {"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"}}
                ]
            }]
          )
    
          print(response.choices[0].message.content)
        
    

    Output

        
          The image depicts a serene landscape of Yosemite National Park, featuring a river flowing through a valley surrounded by towering cliffs and lush greenery.
    
          *   **River:**
              *   The river is calm and peaceful, with clear water that reflects the surrounding scenery.
              *   It flows gently from the bottom-left corner to the center-right of the image.
              *   The riverbank is lined with rocks and grasses, adding to the natural beauty of the scene.
          *   **Cliffs:**
              *   The cliffs are massive and imposing, rising steeply from the valley floor.
              *   They are composed of light-colored rock, possibly granite, and feature vertical striations.
              *   The cliffs are covered in trees and shrubs, which adds to their rugged charm.
          *   **Trees and Vegetation:**
              *   The valley is densely forested, with tall trees growing along the riverbanks and on the cliffsides.
              *   The trees are a mix of evergreen and deciduous species, with some displaying vibrant green foliage.
              *   Grasses and shrubs grow in the foreground, adding texture and color to the scene.
          *   **Sky:**
              *   The sky is a brilliant blue, with only a few white clouds scattered across it.
              *   The sun appears to be shining from the right side of the image, casting a warm glow over the scene.
    
          In summary, the image presents a breathtaking view of Yosemite National Park, showcasing the natural beauty of the valley and its surroundings. The calm river, towering cliffs, and lush vegetation all contribute to a sense of serenity and wonder.
    
        
    

    Function Calling

    Input

        
          import os
          import json
          import openai
    
          client = openai.OpenAI(
              base_url = "https://api.together.xyz/v1",
              api_key = os.environ['TOGETHER_API_KEY'],
          )
    
          tools = [
            {
              "type": "function",
              "function": {
                "name": "get_current_weather",
                "description": "Get the current weather in a given location",
                "parameters": {
                  "type": "object",
                  "properties": {
                    "location": {
                      "type": "string",
                      "description": "The city and state, e.g. San Francisco, CA"
                    },
                    "unit": {
                      "type": "string",
                      "enum": [
                        "celsius",
                        "fahrenheit"
                      ]
                    }
                  }
                }
              }
            }
          ]
    
          messages = [
              {"role": "system", "content": "You are a helpful assistant that can access external functions. The responses from these function calls will be appended to this dialogue. Please provide responses based on the information from these function calls."},
              {"role": "user", "content": "What is the current temperature of New York, San Francisco and Chicago?"}
          ]
              
          response = client.chat.completions.create(
              model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
              messages=messages,
              tools=tools,
              tool_choice="auto",
          )
    
          print(json.dumps(response.choices[0].message.model_dump()['tool_calls'], indent=2))
        
    

    Output

        
          [
            {
              "id": "call_1p75qwks0etzfy1g6noxvsgs",
              "function": {
                "arguments": "{\"location\":\"New York, NY\",\"unit\":\"fahrenheit\"}",
                "name": "get_current_weather"
              },
              "type": "function"
            },
            {
              "id": "call_aqjfgn65d0c280fjd3pbzpc6",
              "function": {
                "arguments": "{\"location\":\"San Francisco, CA\",\"unit\":\"fahrenheit\"}",
                "name": "get_current_weather"
              },
              "type": "function"
            },
            {
              "id": "call_rsg8muko8hymb4brkycu3dm5",
              "function": {
                "arguments": "{\"location\":\"Chicago, IL\",\"unit\":\"fahrenheit\"}",
                "name": "get_current_weather"
              },
              "type": "function"
            }
          ]
        
    

    Query models with multiple images

    Currently this model supports 5 images as input.

    Input

        
          # Multi-modal message with multiple images
          response = client.chat.completions.create(
              model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
              messages=[{
                  "role": "user",
                  "content": [
                      {
                          "type": "text",
                          "text": "Compare these two images."
                      },
                      {
                          "type": "image_url",
                          "image_url": {
                              "url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
                          }
                      },
                      {
                          "type": "image_url",
                          "image_url": {
                              "url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/slack.png"
                          }
                      }
                  ]
              }]
          )
          print(response.choices[0].message.content)
    
        
    

    Output

        
          The first image is a collage of multiple identical landscape photos showing a natural scene with rocks, trees, and a stream under a blue sky. The second image is a screenshot of a mobile app interface, specifically the navigation menu of the Canva app, which includes icons for Home, DMs (Direct Messages), Activity, Later, Canvases, and More.
    
          ### Comparison:
          1. **Content**:
            - The first image focuses on a natural landscape.
            - The second image shows a digital interface from an app.
    
          2. **Purpose**:
            - The first image could be used for showcasing nature, design elements in graphic work, or as a background.
            - The second image represents the functionality and layout of the Canva app's navigation system.
    
          3. **Visual Style**:
            - The first image has vibrant colors and realistic textures typical of outdoor photography.
            - The second image uses flat design icons with a simple color palette suited for user interface design.
    
          4. **Context**:
            - The first image is likely intended for artistic or environmental contexts.
            - The second image is relevant to digital design and app usability discussions.
        
    
  • Model card

    • Model String: meta-llama/Llama-4-Scout-17B-16E-Instruct
    • Specs:
      • 17B active parameters (109B total)
      • 16-expert MoE architecture
      • 327,680 context length (will be increased to 10M)
      • Support for 12 languages: Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese
      • Multimodal capabilities (text + images)
      • Support Function Calling
    • Best for: Multi-document analysis, codebase reasoning, and personalized tasks
    • Knowledge Cutoff: August 2024
  • Applications & use cases

    • Multi-document summarization for legal/financial analysis: Analyze multiple legal contracts or financial statements simultaneously, identifying key terms, inconsistencies, and patterns across documents to generate comprehensive summaries and risk assessments.
    • Personalized task automation using years of user data: Create tailored automation workflows by analyzing an individual's historical data patterns, communication style, and preferences, enabling highly personalized digital assistants that adapt to specific user needs.
    • Efficient image parsing for multimodal applications: Process and understand image content in conjunction with text to power applications like visual search, content moderation, and accessibility features that require understanding the relationship between visual and textual elements.
  • Model provider
    Meta
  • Type
    Chat
    Vision
  • Main use cases
    Chat
    Function Calling
    Vision
  • Features
    Function Calling
  • Fine tuning
    Supported
  • Deployment
    Dedicated
  • Parameters
    109B
  • Context length
    1M
  • Input price

    $0.18 / 1M tokens

  • Output price

    $0.59 / 1M tokens

  • Input modalities
    Text
    Image
  • Output modalities
    Text
  • Released
    April 2, 2025
  • Last updated
    February 5, 2026
  • Quantization level
    FP16
  • External link
  • Category
    Chat