Llama 4 Scout vs Maverick - Image Understanding Comparison

Compare image understanding capabilities of LLaMA 4 Scout and Maverick using a visual workflow that analyzes home decor scene descriptions.

Playground API Pixelflow

If you're looking for an API, here is a sample code in NodeJS to help you out.

const axios = require('axios');
   
   const api_key = "YOUR API KEY";
   const url = "https://api.segmind.com/workflows/68137e1d6f6ddb5db5716fd4-v2";
   const data = {
     image: "publicly accessible image link",
      Your_Question: "the user input string"
   };
    
   axios.post(url, data, {
     headers: {
       'x-api-key': api_key,
       'Content-Type': 'application/json'
     }
   }).then((response) => {
     console.log(response.data);
   });

Response

application/json

{
  "poll_url": "<base_url>/requests/<some_request_id>",
  "request_id": "some_request_id",
  "status": "QUEUED"
}

You can poll the above link to get the status and output of your request.

Response

application/json

{
  "Llama_4_scout": "any user input string",
  "Llama_4_Maverick": "any user input string"
}

Attributes

imageimage*

Your_Questionstr*

To keep track of your credit usage, you can inspect the response headers of each API call. The x-remaining-credits property will indicate the number of remaining credits in your account. Ensure you monitor this value to avoid any disruptions in your API usage.

Comparing Image Understanding in LLaMA 4 Models

This workflow is designed to benchmark and compare the visual reasoning and image understanding capabilities of two different versions of LLaMA 4-based models: LLaMA 4 Scout and LLaMA 4 Maverick. It's particularly useful for evaluating how well these models can describe visual content-specifically in the context of home furnishing and interior decor.

How It Works

At the core of the workflow is a shared image input-a high-resolution photo of a modern living room featuring colorful wall art, a sofa, coffee table, decorative pillows, and other decor elements. This image is routed to two parallel nodes, each powered by a different LLaMA 4 variant (Scout and Maverick). Both nodes are prompted with the same instruction:
"Describe all the home furnishing and home decor items in this image."

Each model independently generates a textual output, which is then displayed for side-by-side comparison. This allows you to analyze differences in:

Object recognition accuracy (e.g. does the model see the artwork, plant, or rug?)
Level of detail (e.g. does it mention materials, positions, and textures?)
Descriptive richness (e.g. does it infer style or aesthetic choices?)
Hallucinations or omissions in the generated output

This is especially useful for teams building vision-language models or deploying multimodal applications where accurate scene interpretation is critical-such as in eCommerce, design tools, or real estate platforms.

How to Customize

You can easily adapt this workflow to your own use cases by:

Changing the input image to any other domain (e.g. fashion, food, outdoor scenes, product photography)
Editing the prompt to tailor the kind of information you want extracted (e.g. "Identify potential hazards in this image" or "Write a product description for this photo")
Swapping models by replacing the LLaMA 4 nodes with other multimodal models like GPT-4V, Gemini Pro, Claude 3, etc.
Adding evaluation logic to score or rank model responses based on criteria like completeness or alignment with ground truth labels

This modular setup makes it ideal for running rapid A/B tests across vision-language models.

Models Used in the Pixelflow

llama4-scout-instruct-basic

Unlock powerful multimodal AI with Llama 4 Scout basic, a 17 billion active parameters model offering leading text & image understanding.

llama4-maverick-instruct-basic

Llama 4 Maverick Instruct Basic is a 400B parameter powerhouse with 128 experts for unparalleled text and image understanding.