Llama 4 Scout performance data on Rival is based on blind head-to-head community voting. Overall win rate: 21.6% across 171 duels. All vote data is part of Rival's open dataset of 21,000+ human preference judgments across 200+ AI models. Model responses are curated from 53 challenges.
Llama 4 Scout is good. We've said that. We stand by it. But we'd be doing you a disservice if we didn't show you these.
Llama 4 Scout performance data on Rival is based on blind head-to-head community voting. Overall win rate: 21.6% across 171 duels. All vote data is part of Rival's open dataset of 21,000+ human preference judgments across 200+ AI models. Model responses are curated from 53 challenges.
Llama 4 Scout is good. We've said that. We stand by it. But we'd be doing you a disservice if we didn't show you these.
Llama 4 Scout is Meta's compact yet powerful multimodal model with 17B active parameters and 16 experts (109B total parameters). It fits on a single H100 GPU with Int4 quantization and offers an industry-leading 10M token context window, outperforming Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1 across various benchmarks.
Use Llama 4 Scout in your applications via the OpenRouter API. Copy the code below to get started.
import requests
response = requests.post(
"https://openrouter.ai/api/v1/chat/completions" ,
headers={
"Authorization""Bearer $OPENROUTER_API_KEY" : ,
"Content-Type""application/json" :
},
json={
"model""meta-llama/llama-4-scout" : ,
"messages""role""user""content""Hello!" : [{: , : }]
}
)
print(response.json())Replace $OPENROUTER_API_KEY with your API key from openrouter.ai/keys
The junior dev who reads the ticket literally. Won't interpret, won't improvise. Just executes requirements to spec. Reliable like a Honda Civic: nothing fancy, nothing broken.
Interprets prompts narrowly and executes them literally. World map? Here are some rectangles with labels. Xbox controller? Some circles and text. Works, technically. The model equivalent of "it compiles, ship it."
Unique words vs. total words. Higher = richer vocabulary.
Average words per sentence.
"Might", "perhaps", "arguably" per 100 words.
**Bold** markers per 1,000 characters.
Bullet and numbered list items per 1,000 characters.
Markdown headings per 1,000 characters.
Emoji per 1,000 characters.
"However", "moreover", "furthermore" per 100 words.
53 outputs from Llama 4 Scout
Llama 4 Scout is Meta's compact yet powerful multimodal model with 17B active parameters and 16 experts (109B total parameters). It fits on a single H100 GPU with Int4 quantization and offers an industry-leading 10M token context window, outperforming Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1 across various benchmarks.
Use Llama 4 Scout in your applications via the OpenRouter API. Copy the code below to get started.
import requests
response = requests.post(
"https://openrouter.ai/api/v1/chat/completions" ,
headers={
"Authorization""Bearer $OPENROUTER_API_KEY" : ,
"Content-Type""application/json" :
},
json={
"model""meta-llama/llama-4-scout" : ,
"messages""role""user""content""Hello!" : [{: , : }]
}
)
print(response.json())Replace $OPENROUTER_API_KEY with your API key from openrouter.ai/keys
The junior dev who reads the ticket literally. Won't interpret, won't improvise. Just executes requirements to spec. Reliable like a Honda Civic: nothing fancy, nothing broken.
Interprets prompts narrowly and executes them literally. World map? Here are some rectangles with labels. Xbox controller? Some circles and text. Works, technically. The model equivalent of "it compiles, ship it."
Unique words vs. total words. Higher = richer vocabulary.
Average words per sentence.
"Might", "perhaps", "arguably" per 100 words.
**Bold** markers per 1,000 characters.
Bullet and numbered list items per 1,000 characters.
Markdown headings per 1,000 characters.
Emoji per 1,000 characters.
"However", "moreover", "furthermore" per 100 words.
53 outputs from Llama 4 Scout