LLaMA 4 Scout is a 17-billion parameter model leveraging a Mixture-of-Experts architecture with 16 active experts, positioning it as the top multimodal model in its category. It consistently outperforms competitors like Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1 across diverse benchmark tasks. Despite its performance, LLaMA 4 Scout is remarkably efficient—capable of running on a single NVIDIA H100 GPU with Int4 quantization. It also boasts an industry-leading 10 million token context window and is natively multimodal, seamlessly processing text, images, and video inputs for advanced real-world applications.
Web Site AI Model Web Page | |
Provider The entity that provides this model. | |
Chat Input a message to start chatting | - |
Release Date When the model was first released. | 1 year ago Apr 05, 2025 |
Modalities Types of data this model can process | text images video |
API Providers The providers that offer this model. (This is not an exhaustive list.) | Meta AI, Hugging Face, Fireworks, Together, DeepInfra |
Knowledge Cut-off Date When the model's knowledge was last updated. | 2025-04 |
Open Source Whether the model's code is available for public use. | Yes (Source) |
Pricing Input Cost for processing tokens in your prompts | Not available |
Pricing Output Cost for tokens generated by the model | Not available |
MMLU Massive Multitask Language Understanding - Tests knowledge across 57 subjects including mathematics, history, law, and more | Not available |
MMLU-Pro A more robust MMLU benchmark with harder, reasoning-focused questions, a larger choice set, and reduced prompt sensitivity | 74.3% Reasoning & Knowledge Source |
MMMU Massive Multitask Multimodal Understanding - Tests understanding across text, images, audio, and video | 69.4% Image Reasoning Source |
HellaSwag A challenging sentence completion benchmark | Not available |
HumanEval Evaluates code generation and problem-solving capabilities | Not available |
MATH Tests mathematical problem-solving abilities across various difficulty levels | Not available |
GPQA Tests PhD-level knowledge in chemistry, biology, and physics through multiple choice questions that require deep domain expertise | 57.2% Diamond Source |
IFEval Tests model's ability to accurately follow explicit formatting instructions, generate appropriate outputs, and maintain consistent instruction adherence across different tasks | Not available |
SimpleQA Assessing the accuracy of simple questions | - |
AIME 2024 | - |
AIME 2025 | - |
Aider Polyglot Multilingual programming benchmark. | - |
LiveCodeBench v5 Benchmark for real-time programming | - |
Global MMLU (Lite) A simplified version of the benchmark for assessing the universality of models at the global level. | - |
MathVista Evaluates the mathematical reasoning abilities of AI models within visual contexts | - |
Mobile Application | - |
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