What models does this prompt chain compare?

It compares Llama 4 Scout (a 17B-parameter mixture-of-experts model with 16 experts) against Llama 3 8B as a reference point, both hosted on Replicate.

What topics are used to evaluate the models?

The evaluation covers seven subjects: AI/mixture-of-experts, multimodal AI, quantum computing, climate solutions, space exploration, healthy lifestyle tips, and creative writing.

What types of assertions does this example use?

It uses `contains-any` keyword checks (e.g., for quantum computing terms) mixed with `llm-rubric` quality checks (e.g., evaluating multimodal AI understanding capabilities).

What generation settings are configured for both models?

Both providers are configured with temperature 0.7, top_p 0.9, and approximately 1000 max tokens.

Prompt Chain

Replicate Llama4 Scout for Research

Name: Replicate Llama4 Scout
Availability: OnlineOnly
Author: promptfoo/promptfoo

Promptfoo example running Meta's Llama 4 Scout via Replicate, testing reasoning and creative tasks against Llama 3.

Copy chain

Works with replicate

promptfoo/promptfoo

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Spark score

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Updated 10 days ago

Source checked Sep 10, 2026

Version 0.123.0

Models

llama 3

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Why it matters

Leverage the Llama4 Scout model via Replicate to perform advanced web searches and synthesize information for research purposes.

Outcomes

What it gets done

Execute complex web searches using Llama4 Scout.

Index and retrieve relevant information from search results.

Summarize findings to provide concise research insights.

Install

Add it to your toolbox

Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/pfoo-replicate-llama4-scout | bash

After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.

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Overview

Replicate Llama4 Scout

This promptfoo example runs Meta's Llama 4 Scout model through Replicate, testing reasoning and creative-writing tasks and comparing results against Llama 3. Use it as a starting template for benchmarking Llama 4 Scout, or comparing it to Llama 3, on your own tasks via Replicate.

What it does

This promptfoo example runs Meta's Llama 4 Scout - a 17-billion-parameter, 16-expert mixture-of-experts model - via Replicate, testing its reasoning and creative-writing abilities and comparing it against Llama 3 across a set of analytical and creative prompts. Llama 4 Scout is part of Meta's natively multimodal Llama 4 model collection, with the project describing it as delivering industry-leading performance in both text and image understanding tasks.

When to use - and when NOT to

Use it as a starting template for benchmarking Llama 4 Scout, or comparing it to Llama 3, on your own tasks through Replicate's hosted API. It is specific to Scout; the README notes Llama 4 Maverick (128 experts) can be swapped in when available, but this example as written does not test multimodal or image inputs even though Llama 4 is natively multimodal.

Inputs and outputs

Requires a REPLICATE_API_TOKEN environment variable from replicate.com/account/api-tokens. Running promptfoo eval executes the configured test cases - AI and mixture-of-experts self-awareness, multimodal understanding, quantum computing, climate solutions, and creative writing - against the model, and promptfoo view opens the results. Model behavior is tunable via temperature (0.0 for deterministic output up to 1.0 for very random output), max_tokens, and top_p (the nucleus-sampling threshold for token selection) in the Replicate provider config. Per the project's own notes, the model's mixture-of-experts approach is meant to keep computation efficient while still handling both analytical and creative tasks well, with the 16-expert architecture contributing to response quality.

Integrations

Replicate's hosted inference API for Llama 4 Scout, and promptfoo's eval and provider-comparison framework; the example is structured to make swapping in other Replicate-hosted models for comparison straightforward. The README suggests extending it to test Llama 4 Maverick (128 experts) once available, add image-understanding tests when multimodal features are enabled, or compare against other state-of-the-art models to explore how the mixture-of-experts architecture affects different task types.

Who it's for

Developers evaluating Llama 4 Scout's reasoning and creative output quality, or comparing it against Llama 3 or other Replicate-hosted models, before committing to it as a production model in their own application.

Source README

provider-replicate/llama4-scout (Replicate Llama 4 Scout)

You can run this example with:

npx promptfoo@latest init --example provider-replicate/llama4-scout
cd provider-replicate/llama4-scout

This example demonstrates how to use Replicate to run the new Llama 4 Scout model, a cutting-edge 17 billion parameter model with 16 experts using mixture-of-experts architecture.

About Llama 4 Scout

Llama 4 Scout is part of the Llama 4 collection of natively multimodal AI models. Key features:

17 billion parameters with 16 experts
Mixture-of-experts architecture for enhanced performance
Natively multimodal - enables text and multimodal experiences
Industry-leading performance in text and image understanding

Environment Variables

This example requires the following environment variable:

REPLICATE_API_TOKEN - Your Replicate API key (get one at https://replicate.com/account/api-tokens)

You can set this in a .env file or directly in your environment:

export REPLICATE_API_TOKEN=your_api_token_here

What This Example Does

This example:

Tests the Llama 4 Scout model on various analytical and creative tasks
Demonstrates the model's advanced reasoning capabilities
Compares Llama 4 Scout with Llama 3 to show improvements
Shows how to configure Replicate model parameters for optimal results

Running the Example

Set your Replicate API token (see above)
Run the evaluation:

promptfoo eval

View the results:

promptfoo view

Model Configuration

The example demonstrates key Replicate configuration options for Llama 4:

temperature: Controls randomness (0.0 = deterministic, 1.0 = very random)
max_tokens: Maximum number of tokens to generate
top_p: Nucleus sampling threshold for token selection

Test Cases

The example includes tests for:

AI and mixture-of-experts architecture - Testing the model's self-awareness
Multimodal AI - Exploring the model's understanding of multimodal capabilities
Quantum computing - Complex technical topics
Climate solutions - Practical problem-solving
Creative writing - Narrative and storytelling abilities

Customizing the Example

You can modify this example to:

Test Llama 4 Maverick (128 experts) when available
Add image understanding tests (when multimodal features are enabled)
Compare against other state-of-the-art models
Explore the mixture-of-experts architecture's impact on different tasks

Notes

Llama 4 Scout uses a mixture-of-experts approach for efficient computation
The model excels at both analytical and creative tasks
Response quality benefits from the 16-expert architecture
Part of the Llama 4 ecosystem with multimodal capabilities

FAQ

Common questions

Discussion