Meta’s Latest AI Models: Scout, Maverick, and Behemoth – Performance, Insights, and Free Usage Options

Meta’s recent AI model release has sparked considerable interest in the tech community, particularly around their much-discussed capabilities. Three models—Scout, Maverick, and Behemoth—promise cutting-edge features but come with mixed reviews. This breakdown will help you understand their potential, evaluate their strengths and weaknesses, and learn how to access them for free using platforms like Cline and RooCode.

Let’s uncover what matters most. 🌟

🚨 Overview of Meta’s New AI Models

Meta introduced three models with different parameter sizes and goals:

1. Scout Model 🗺️

• Parameters: 109 billion

• Active Parameters: 17 billion

• Experts: 16 (designed for specialization in tasks like math or coding)

• Context Window: 10 million tokens, excellent for document understanding.

  Performance: Despite its size, Scout’s benchmarks align with the Gemma 3 model, which has far fewer parameters. Surprisingly underwhelming for such advanced machinery.

2. Maverick Model 🚀

• Parameters: 400 billion

• Active Parameters: 17 billion

• Experts: 128

  Performance: Holds the middle ground between Scout and Behemoth. Benchmarks suggest it’s comparable to GPT-4O Flash 2.0 models but struggles with reasoning tasks.

3. Behemoth Model 🦾

• Parameters: A record-breaking 2 trillion

• Active Parameters: 288 billion

• Experts: 16

  Performance: Behemoth is meant to outshine competitors like Claude Sonnet 3.7. However, its availability is an issue—it isn’t downloadable, which limits practical usage despite its potential edge.

📉 Disappointment: None of the models fully meet coding needs, particularly for tasks like SVG generation or synth keyboard creation. Even smaller models like Fi-414B outperform them here.

🔎 Performance Highlights: What Works and What Doesn’t

Testing these models sheds light on their ability (and inability) to handle various kinds of problems.

⭐️ Wins:

• Coding Tasks

• Basic coding questions involving interactive designs, like buttons triggering confetti explosions, were handled well by Scout and Maverick.

• Math Questions

• Both Scout and Maverick performed admirably in solving straightforward math problems.

• Relaxed Censorship 🚫

• These models provide outputs with fewer restrictions, offering candid results in sensitive areas. This makes them attractive for open-ended tasks like document parsing.

❌ Losses:

Key areas revealed the flaws in the models, especially relative to competitors:

• Pattern Recognition: Scout and Maverick failed at handling more complex recognition tasks.
• Sophisticated Reasoning: Maverick had moderate success where Scout floundered, but neither passed deeper inference requirements.
• Coding Outputs: SVG outputs and synth keyboard designs were labeled “atrocious,” bringing into question usability for developers.

🌐 Accessing the Models for Free

Want to use Scout and Maverick? The good news is these models are open-sourced and available for experimentation! 🎉

Platforms Offering Free APIs:

1. OpenRouter API

• Provides free access to Scout and Maverick models for testing.

1. Meta AI Platform

• Offers both models with active support for exploration and usage.

1. Hugging Face

• A popular repository where Scout and Maverick can be downloaded. Behemoth, however, remains elusive.

🛠️ Cline & RooCode Configuration Tip:
To access these APIs for free, set your preferred provider to OpenRouter or Gro within your app’s configurations. This ensures seamless integration. Note, however, that performance, particularly in coding, may require additional fine-tuning of the models.

🤔 Why the Models Fall Short

Developers and researchers were hopeful these models would showcase groundbreaking capabilities. However, Meta’s “mixture of experts” design philosophy didn’t deliver levels of performance proportional to the massive parameter size.

Key Misses:

• The base performance of Scout and Maverick lags behind existing models despite their parameter advantage.
• Behemoth is unavailable for testing, limiting the ability to validate Meta’s claims.
• Coding outputs are subpar even compared to smaller models, leading to developer frustration.

💡 Adapting for Future Use: While disappointing at face value, these models might improve massively through multimodal applications, document analysis tasks, or specialized fine-tuning.

🧠 Practical Applications

While the models don’t excel across the board, there are areas where they could shine:

• Document Understanding 📜

• A standout feature is their ability to parse up to 10 million tokens. Perfect for detailed comprehension and summarization tasks.

• Open Collaboration 🤝

• The open-source nature allows more collaboration and fine-tuning. Explorer platforms like Hugging Face could create better adaptations for specific industries.

• Censorship-Free Conversations 🗨️

• Ideal for applications demanding unfettered inputs or creative problem solving without predefined boundaries.

📚 Resource Toolbox

To make the most of Scout, Maverick, or Behemoth, check out these fantastic tools and platforms:

1. Meta AI Platform
   Meta AI Platform
   Official hub to explore capabilities of Scout and Maverick.

2. Hugging Face
   Hugging Face
   Get access to these models and adapt them for personalized use cases.

3. OpenRouter Free API
   OpenRouter
   Enables seamless access for interfacing with Scout and Maverick.

4. Cline and RooCode
   Cline Homepage | RooCode
   Platforms that support integration with Scout and Maverick via free endpoints.

5. Meta Blog Posts
   Meta Blog
   Read the latest updates on Behemoth’s future availability.

6. DocsGPT
   DocsGPT
   Fine-tune multimodal models for documentation tasks with large context windows.

💡 Final Insights

Meta’s attempt at pushing the boundaries of AI models with Scout, Maverick, and Behemoth offers excitement but leaves room for improvement. The models succeed in relaxed censorship, document understanding, and open collaboration, yet fall embarrassingly short in more rigorous coding and reasoning tests.

Whether you’re delving into free API experiments or dreaming of fine-tuning capabilities, these models are a starting point rather than a final solution. For now, tools like GPT-4O Flash may outperform Maverick in coding and reasoning, but Scout’s extensive token window brings specialized promise for document-heavy industries.

Quick Tip: If coding is your priority, consider existing models like Fi-414B or Flash 2.0 instead. Otherwise, Scout and Maverick can be test-driven for academic evaluations via OpenRouter or Hugging Face.

🌐 What’s Next?: All eyes are on the reasoning and omni versions of Llama 4. For researchers and developers alike, patience may yield better opportunities here.

What are your thoughts on Meta’s models? Are they a worthwhile investment in time or just hype-ridden disappointments? Share them below! 🚀