Llama 4 is a groundbreaking set of large language models released by Meta AI. It consists of three powerful models:

1. Llama 4 Scoot: A 17 billion parameter model with 16 experts and a record-breaking 10 million token context window. It outperforms Gemini 3 across multiple benchmarks.

2. Llama 4 Maverick: The same 17 billion parameter model as Scoot, but with 128 experts. It excels at image grounding, reasoning, and coding, matching or beating larger models like GPT-4 Omni and Gemini 2.0 Flash.

3. Llama 4 Behemoth: Still in training, this model is already outperforming GPT-4.5, Claw 3.7, and Gemini 2.0 Pro on STEM benchmarks.

The Scoot and Maverick models are the first open-source, natively multimodal large English models from Meta. They use a mixture of experts architecture for improved efficiency, fitting on a single H100 GPU.

Llama 4 Scoot's 10 million token context window enables tasks like multi-document summarization and reasoning over large codebases. The Maverick model is a great alternative to Gemini 2.0 Flash, with slightly higher performance across various tasks.

You can easily access these models through Hugging Face, Meta AI's chatbot, or the free API provided by OpenRouter. Testing the models across coding, math, and multimodal tasks shows their impressive capabilities, making Llama 4 a strong contender in the open LLM landscape.

Llama Force Scoot is a 17 billion active parameter model with 16 experts and a record-breaking 10 million token context window. This model outperforms Gemini 3 across multiple benchmarks, including Gemini 2.0 Flashlight and Mistro 3.1.

The key features of Llama Force Scoot include:

• Long Context Window: The 10 million token context window enables tasks like multi-document summarization and reasoning over large code bases or text, overcoming the limitations of shorter context models.
• IRO Architecture: Llama Force Scoot is built with a new IRO (Interleaved Rotary Embeddings) architecture, which combines attention layers and rotary embeddings to excel at long-context tasks.
• Retrieval and Code Performance: The model shows strong retrieval and code performance, making it well-suited for code generation and retrieval tasks.

Llama Force Scoot is the first open-source, natively multimodal large English model from Meta, using early fusion to integrate text and vision seamlessly. Its mixture of experts architecture allows for efficient deployment on a single H00 GPU.

Overall, Llama Force Scoot is a powerful model that can handle long-context tasks and outperform existing models in various benchmarks, making it a valuable tool for developers and researchers working with large-scale text and code-related applications.

The Llama 4 Maverick is a powerful 17 billion active parameter model with 128 experts, showcasing exceptional performance in image grounding and reasoning tasks. This model outperforms GBT4 Omni and Gemini 2.0 Flash in image grounding, and matches the performance of Deep Seek V3 in reasoning and coding, while being half the size.

One of the key strengths of the Llama 4 Maverick is its ability to excel in Ella Marina, achieving an impressive ELO score of 1,400. This demonstrates its strong performance in language understanding and generation tasks.

The Llama 4 Maverick is built on a mixture of experts architecture, which allows each token to activate only a small subset of parameters, improving efficiency. This model can fit on a single H00 GPU, making it easier to deploy at large scales.

Overall, the Llama 4 Maverick is a highly capable model that showcases exceptional performance in image grounding, reasoning, and language tasks, making it a strong alternative to Gemini 2.0 Flash.

Llama 4 Behemoth is the latest and most powerful model in the Llama series from Meta AI. This model is still in training, but it is already outperforming top models like GPT-4.5, Claw 3.7, Sonnet, and Gemini 2.0 Pro on various STEM benchmarks.

Llama 4 Behemoth is the powerhouse behind the other two Llama 4 models, Llama 4 Scoot and Llama 4 Maverick. It boasts impressive performance across a wide range of tasks, including coding, reasoning, and knowledge-based benchmarks.

One of the key strengths of Llama 4 Behemoth is its ability to excel on STEM-related tasks. It has demonstrated exceptional performance on coding benchmarks, outpacing the competition by a significant margin. Additionally, it has shown strong results in reasoning and multilingual tasks, further solidifying its position as a versatile and capable model.

The release of Llama 4 Behemoth is a testament to the continued advancements in large language models from Meta AI. This model represents a significant step forward in the field of artificial intelligence, and it will be interesting to see how it performs against the latest iteration of Gemini, the Gemini 2.5 Pro.

Overall, Llama 4 Behemoth is a powerful and impressive model that is poised to make a significant impact in the world of AI and machine learning. Its ability to outperform top models on a wide range of benchmarks is a testament to the hard work and innovation of the Meta AI team.

Meta AI has recently released three powerful Llama 4 models: Llama 4 Scoot, Llama 4 Maverick, and Llama 4 Behemoth. These models offer impressive capabilities across various benchmarks, including coding, reasoning, and image understanding.

The Llama 4 Scoot model is a 17 billion parameter model with a record-breaking 10 million token context window. This enables it to excel at tasks like multi-document summarization and reasoning over large code bases or text. The Scoot model uses a new ILO architecture with rotary embeddings, which enhances its performance on long-context tasks.

The Llama 4 Maverick model shares the same 17 billion parameter architecture as the Scoot, but with 128 experts. This makes it a strong alternative to Gemini 2.0 Flash, outperforming it on various benchmarks, including image reasoning, coding, and knowledge-based tasks.

The Llama 4 Behemoth model is still in training, but it is already outperforming GPT 4.5, Claw 3.7, and Gemini 2.0 Pro on STEM benchmarks. This model is the powerhouse behind the Scoot and Maverick versions.

To get started with these models, you can visit llama.com to download them if you have the necessary requirements to host them locally. Alternatively, you can access them through Hugging Face or Meta AI's chatbot. Additionally, you can use the free API provided by OpenRouter to utilize the Scoot and Maverick models.

When testing these models, you can explore their capabilities across a range of tasks, from coding and math to image description and logical problem-solving. The models have shown impressive performance, and the Scoot's long-context abilities make it a valuable tool for working with large code bases or text.

Overall, the Llama 4 series of models from Meta AI represents a significant advancement in large language models, offering impressive capabilities that can be leveraged for a wide variety of applications.

We began by testing the Llama 4 models across a variety of benchmarks, from coding to math and beyond.

First, we tested the Llama 4 Scoot model by prompting it to create a front-end user interface. The model quickly generated a functional drag-and-drop UI, demonstrating its capabilities in front-end development.

Next, we challenged the Scoot model to implement Conway's Game of Life in Python, evaluating its algorithmic implementation, state transition logic, and terminal-based visualization. The model successfully generated a working simulation, passing this test.

We then asked the Llama 4 Maverick model to create an SVG butterfly, a task that often stumps many models. However, both the Maverick and Scoot models struggled with this prompt, failing to generate a satisfactory butterfly design.

Moving on, we tested the models' problem-solving abilities with a relative motion and algebra problem. The models took the correct steps and arrived at the accurate solution, demonstrating their mathematical reasoning capabilities.

Next, we evaluated the models' number theory and set operations skills by asking them to write a Python function that filters a list of integers, keeping only the numbers that are either prime or Fibonacci, but not both. The models handled this challenge efficiently, passing the test.

We then tested the Scoot model's image description and object recognition abilities by providing a prompt about a dog behind a tree. The model accurately described the scene and correctly identified the dog breed as a Jack Russell Terrier.

Finally, we assessed the Scoot model's long-context reasoning and summarization capabilities by providing a lengthy article and asking it to split the content into three sections, summarizing each one. The model performed well, demonstrating its ability to understand and explain complex, long-form information.

Overall, the Llama 4 models showed impressive performance across a diverse range of benchmarks, with the Scoot model particularly excelling in long-context tasks and the Maverick model proving to be a strong alternative to Gemini 2.0 Flash. These models showcase the continued advancements in large language models and their potential applications.

The Llama 4 series of models from Meta AI are a significant advancement in the field of large language models. The three models - Llama 4 Scoot, Llama 4 Maverick, and Llama 4 Behemoth - demonstrate impressive performance across a wide range of benchmarks, from coding and math to image reasoning and knowledge retrieval.

The Llama 4 Scoot model, with its 10 million token context window and new IRO architecture, shows strong potential for tasks like multi-document summarization and reasoning over large code bases. The Llama 4 Maverick, with its 128 experts, outperforms models like GPT-4 Omni and Gemini 2.0 Flash in areas like image grounding, reasoning, and coding.

While the Llama 4 Behemoth is still in training, it has already shown impressive results, outpacing models like GPT-4.5 and Claw 3.7 on STEM benchmarks. The ability of these models to handle long-context tasks and their seamless integration of text and vision make them valuable tools for a wide range of applications.

Overall, the Llama 4 series represents a significant step forward in the development of large language models, and it will be exciting to see how these models continue to evolve and be applied in the future.