About Llama 4 Scout
Llama 4 Scout is a 109 billion parameter Mixture-of-Experts (MoE) multimodal LLM with 17 billion active parameters and 16 experts, designed for efficient, large-scale reasoning over both text and images. It excels in tasks requiring extremely long context—supporting up to a 10 million token window—making it particularly strong for multi-document summarization, codebase analysis, and personalized automation based on extensive user data[1][2][4][6].
Some other noteworthy features of Llama 4 Scout include native support for both text and image inputs (multimodality), multilingual capabilities across twelve major languages, and compute efficiency that allows it to run on a single H100 GPU with quantization. Its architecture enables high performance in image understanding (e.g., visual question answering), long-context retrieval tasks (“needle-in-a-haystack”), coding benchmarks, and advanced reasoning—all while maintaining lower inference costs compared to similarly sized dense models.
| Metric | Value |
|---|---|
| Parameter Count | 109 billion total / 17 billion active |
| Mixture of Experts | Yes |
| Active Parameter Count | 17 billion |
| Context Length | Up to 10 million tokens (currently limited to ~300K–327K on Together.ai) |
| Multilingual | Yes |
| Quantized* | No |
*Quantization is specific to the inference provider and the model may be offered with different quantization levels by other providers.
Ready to build with Llama 4 Scout?
Try Llama 4 Scout in the Workbench to prompt it, compare outputs, and iterate on prompts without writing any code. When you're ready to ship, call the same model from our API and build your own apps on top of it.
curl -sSf -X POST https://hub.oxen.ai/api/ai/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OXEN_API_KEY" \
-d '{
"model": "meta-llama-llama-4-scout-17b-16e-instruct",
"messages": [
{
"role": "user",
"content": "Try sending a message."
}
]
}'