Llama 4 Scout 17B-16E delivers state-of-the-art results for its size class that outperforms previous Llama generations and other open and proprietary models on several benchmarks. It features MoE architecture with 17 billion active parameters out of the 109 billion total parameters and 16 experts.
Llama 4 Scout 17B-16E is suited for retrieval tasks within long contexts and tasks that demand reasoning over large amounts of information, such as summarizing multiple large documents, analyzing extensive user interaction logs for personalization, and reasoning across large codebases.
Managed API (MaaS) specifications
Try in Agent Studio View model card in Model Garden
| Model ID | llama-4-scout-17b-16e-instruct-maas |
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| Launch stage | GA | |
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| Knowledge cutoff date | August 2024 | |
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| Pricing | See Pricing. | |
Deploy as a self-deployed model
To self-deploy the model, navigate to the Llama 4 Scout 17B-16E model card in the Model Garden console and click Deploy model. For more information about deploying and using partner models, see Deploy a partner model and make prediction requests.
Considerations
- You can include a maximum of three images per request.
- The MaaS endpoint doesn't use Llama Guard, unlike previous versions. To use Llama Guard, deploy Llama Guard from Model Garden and then send the prompts and responses to that endpoint. However, compared to Llama 4, Llama Guard has a more limited context (128,000) and can only process requests with a single image at the beginning of the prompt.
- Batch predictions aren't supported.