Models / MetaLlama / / Llama 4 Scout API
Llama 4 Scout API
Chat
Vision
SOTA 109B model with 17B active params & large context, excelling at multi-document analysis, codebase reasoning, and personalized tasks.
Try our Llama 4 API

Together AI offers day 1 support for the new Llama 4 multilingual vision models that can analyze multiple images and respond to queries about them.
Register for a Together AI account to get an API key. New accounts come with free credits to start. Install the Together AI library for your preferred language.
Llama 4 Scout API Usage
Endpoint
meta-llama/Llama-4-Scout-17B-16E-Instruct
RUN INFERENCE
curl -X POST "https://api.together.xyz/v1/chat/completions" \
-H "Authorization: Bearer $TOGETHER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "meta-llama/Llama-4-Scout-17B-16E-Instruct",
"messages": [],
"stream": true
}'
RUN INFERENCE
from together import Together
client = Together()
response = client.chat.completions.create(
model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
messages=[],
stream=True
)
for token in response:
if hasattr(token, 'choices'):
print(token.choices[0].delta.content, end='', flush=True)
RUN INFERENCE
import Together from "together-ai";
const together = new Together();
const response = await together.chat.completions.create({
messages: [],
model: "meta-llama/Llama-4-Scout-17B-16E-Instruct",
stream: true
});
for await (const token of response) {
console.log(token.choices[0]?.delta?.content)
}
How to use Llama 4 Scout
Input
Output
Function Calling
Input
Output
Query models with multiple images
Currently this model supports 5 images as input.
Input
Output
Model details
- Model String: meta-llama/Llama-4-Scout-17B-16E-Instruct
- Specs:
- 17B active parameters (109B total)
- 16-expert MoE architecture
- 327,680 context length (will be increased to 10M)
- Support for 12 languages: Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese
- Multimodal capabilities (text + images)
- Support Function Calling
- Best for: Multi-document analysis, codebase reasoning, and personalized tasks
- Knowledge Cutoff: August 2024
Prompting Llama 4 Scout
Applications & Use Cases
- Multi-document summarization for legal/financial analysis: Analyze multiple legal contracts or financial statements simultaneously, identifying key terms, inconsistencies, and patterns across documents to generate comprehensive summaries and risk assessments.
- Personalized task automation using years of user data: Create tailored automation workflows by analyzing an individual's historical data patterns, communication style, and preferences, enabling highly personalized digital assistants that adapt to specific user needs.
- Efficient image parsing for multimodal applications: Process and understand image content in conjunction with text to power applications like visual search, content moderation, and accessibility features that require understanding the relationship between visual and textual elements.