Llama-4-Scout-17B-16E-Instruct
Llama-4-Scout-17B-16E-Instruct is an AI model published by Facebook AI Research Lab, released on 2025-04-05, for Multimodal model, with 109B parameters, and 1000K context length, requiring about 218GB storage, with a 74.30 score on MMLU Pro.
Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology
Model basics
Open source & experience
Official resources
API details
Benchmark Results
Llama 4 Scout Instruct currently shows benchmark results led by MMLU Pro (85 / 132, score 74.30), GPQA Diamond (152 / 187, score 57.20), LiveCodeBench (114 / 123, score 32.80). This page also consolidates core specs, context limits, and API pricing so you can evaluate the model from benchmark results and deployment constraints together.
General Knowledge
2 evaluationsCompare with other models
No curated comparisons for this model yet.
Want a custom combination? Open the compare tool
Publisher
Model Overview
Llama-4-Scout-17B-16E-Instruct is an AI model published by Facebook AI Research Lab, released on 2025-04-05, for Multimodal model, with 109B parameters, and 1000K context length, requiring about 218GB storage, with a 74.30 score on MMLU Pro.
DataLearner on WeChat
Follow DataLearner on WeChat for AI model updates and research notes.
