Llama 4 Scout by Meta AI — Pricing, Benchmarks & Real Outputs
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Alternatives to Llama 4 Scout
Llama 4 Scout's competitors have been quietly putting in work.
Llama 4 Scout's competitors have been quietly putting in work.
Llama 4 Scout's competitors have been quietly putting in work.
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.
fromimport openai OpenAI
client = OpenAI(
"https://openrouter.ai/api/v1" base_url=,
"$OPENROUTER_API_KEY" api_key=,
)
response = client.chat.completions.create(
"meta-llama/llama-4-scout" model=,
"role""user""content""Hello!" messages=[{: , : }],
)
print(response.choices[0].message.content)Set OPENROUTER_API_KEY with your OpenRouter API key from openrouter.ai/keys.
Also on Amazon Bedrock · Google Vertex AI
The junior dev who reads the ticket literally. Won't interpret, won't improvise. Just executes requirements to spec. Reliable like a Honda Civic: nothing fancy, nothing broken.
Interprets prompts narrowly and executes them literally. World map? Here are some rectangles with labels. Xbox controller? Some circles and text. Works, technically. The model equivalent of "it compiles, ship it."
Taste is judged on an uncapped scale, originality first. The space past 100 is craft today's models rarely reach.
Unique words vs. total words. Higher = richer vocabulary.
Average words per sentence.
"Might", "perhaps", "arguably" per 100 words.
**Bold** markers per 1,000 characters.
Bullet and numbered list items per 1,000 characters.
Markdown headings per 1,000 characters.
Emoji per 1,000 characters.
"However", "moreover", "furthermore" per 100 words.
53 outputs from Llama 4 Scout
Llama 4 Scout is Meta's compact yet powerful multimodal model with 17B active parameters and 16 experts (109B total parameters). It fits on a single H100 GPU with Int4 quantization and offers an industry-leading 10M token context window, outperforming Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1 across various benchmarks.
fromimport openai OpenAI
client = OpenAI(
"https://openrouter.ai/api/v1" base_url=,
"$OPENROUTER_API_KEY" api_key=,
)
response = client.chat.completions.create(
"meta-llama/llama-4-scout" model=,
"role""user""content""Hello!" messages=[{: , : }],
)
print(response.choices[0].message.content)Set OPENROUTER_API_KEY with your OpenRouter API key from openrouter.ai/keys.
Also on Amazon Bedrock · Google Vertex AI
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."
Taste is judged on an uncapped scale, originality first. The space past 100 is craft today's models rarely reach.
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