Llama 4 Scout
Llama 4 Scout 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 79.60 score on MMLU.
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
| Type | Condition | Input | Output |
|---|---|---|---|
| Text | - | $0.080/ 1M tokens | $0.300/ 1M tokens |
“—” means the modality is not billed in that direction, or the vendor has not published a price for it.
Benchmark Results
Llama 4 Scout currently shows benchmark results led by MBPP (39 / 96, score 67.80), MMLU (56 / 124, score 79.60), MMMU (48 / 74, score 69.40). 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
5 evaluationsCoding and Software Engineer
3 evaluationsMultimodal Understanding
2 evaluationsAgent Level Benchmark
3 evaluationsPublisher
Model Overview
Llama 4 Scout 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 79.60 score on MMLU.
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