LLAMA 4: BEST OPEN LLM! Beats Sonnet 3.7, R1, GPT-4.5! 10 Million Context Window! (Fully Tested)

Key Concepts

Llama 4, Llama Force Scoot, Llama 4 Maverick, Llama 4 Behemoth, Mixture of Experts (MoE), Context Window, Image Grounding, Reasoning, Coding, Early Fusion, IRO Architecture, Rotary Embeddings, Benchmarks, Multimodal Models, Long Context, RAG (Retrieval-Augmented Generation).

Llama 4 Model Family Overview

Meta AI has released three new Llama 4 models:

• Llama Force Scoot: A 17 billion active parameter model with 16 experts and a 10 million token context window. It outperforms Gemma 3, Gemini 2.0 Flash, and Mistral 3.1 across various benchmarks. Its large context window could potentially replace RAG for tasks like multi-document summarization and reasoning over large codebases. It uses a new IRO architecture with interleaved attention layers and rotary embeddings, excelling at long context tasks and demonstrating strong retrieval and code performance.
• Llama 4 Maverick: Similar to Scoot with 17 billion active parameters, but with 128 experts. It excels at image grounding, surpassing GPT-4 Omni and Gemini 2.0 Flash. It matches DeepSeek V3 in reasoning and coding with half its size and achieves an ELO score of 1400 on Ella Marina.
• Llama 4 Behemoth: Still in training, but already outperforming GPT-4.5, Claude 3.7 Sonnet, and Gemini 2.0 Pro on STEM benchmarks. It serves as the foundation for the other two models.

Llama Force Scoot: Long Context Capabilities

• 10 Million Token Context Window: Enables processing of extensive documents and codebases.
• IRO Architecture: Interleaved attention layers and rotary embeddings optimized for long context tasks.
• Potential RAG Replacement: The large context window may eliminate the need for retrieval-augmented generation in certain applications.
• Applications: Multi-document summarization, reasoning over large codebases, and knowledge retrieval.

Llama 4 Maverick: Multimodal Performance

• Image Grounding: Outperforms GPT-4 Omni and Gemini 2.0 Flash in understanding and relating images to text.
• Reasoning and Coding: Matches DeepSeek V3 in these areas with a smaller model size.
• Mixture of Experts (MoE): Each token activates only a small subset of parameters, improving efficiency.
• Early Fusion: Seamlessly integrates text and vision.
• Deployment: Fits on a single H100 GPU or H100 host, facilitating large-scale deployment.

Llama 4 Behemoth: Performance Benchmarks

• Outperforms: Claude 3.7 Sonnet and Gemini 2.0 Pro on STEM benchmarks.
• Areas of Strength: Coding, multilingual tasks, and general knowledge.

Practical Testing and Examples

The video demonstrates the capabilities of Llama Force Scoot and Llama 4 Maverick through several tests:

1. Front-End Generation: Scoot successfully generated a functional drag-and-drop UI for a sticky note app.
2. Conway's Game of Life: Scoot accurately implemented Conway's Game of Life in Python, demonstrating algorithmic implementation and state transition logic.
3. SVG Butterfly: Both Scoot and Maverick failed to generate a recognizable SVG butterfly.
4. Train Problem: Scoot correctly solved a word problem involving relative motion, demonstrating algebra and time-distance calculations.
5. Prime and Fibonacci Filters: Scoot successfully wrote a Python function to filter a list of integers, identifying prime and Fibonacci numbers efficiently.
6. Image Description: Scoot accurately described an image of a dog behind a tree and correctly identified the dog breed as a Jack Russell Terrier.
7. Long Context Summary: Scoot effectively summarized a large article, demonstrating its ability to process and understand long context.
8. Detective Case: Scoot correctly identified the guilty suspect in a logical reasoning problem with multiple suspects and conflicting statements.

Access and Usage

• llama.com: Models can be downloaded for local hosting.
• Hugging Face: Access models through the Hugging Face platform.
• Meta AI Chatbot: Interact with the models through Meta AI's chatbot.
• Open Router: Access a free API for Llama 4 Maverick and Scoot.

Conclusion

The Llama 4 family of models represents a significant advancement in open-source AI, offering strong performance across various tasks, including long context processing, multimodal understanding, and coding. Llama Force Scoot and Llama 4 Maverick provide viable alternatives to existing models like Gemini 2.0 Flash, while Llama 4 Behemoth promises even greater capabilities upon its full release. The models are easily accessible through various platforms, making them valuable tools for developers and researchers.