Llama 4 Maverick's competitors have been quietly putting in work.
Llama 4 Maverick's competitors have been quietly putting in work.
Meta's multimodal MoE: 17B active parameters across 128 experts, 400B total. Beat GPT-4o and Gemini 2.0 Flash on several benchmarks, with an ELO of 1417 on LMArena. Built for image understanding and creative writing.
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-maverick" 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 pragmatic minimalist. "Ship the feature" mentality of Meta engineering culture. Doesn't debate philosophy at length. Builds. Skips fluff, avoids deep philosophical rabbit holes, focuses on what's useful.
Delivers visual/code outputs cleanly without excess commentary. Responses are functional first. Direct and unadorned on conceptual prompts, no manifesto energy. Friend who says "let's build something" rather than "let's discuss something."
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.
52 outputs · $0.02 tracked across 29 receipts
Meta's multimodal MoE: 17B active parameters across 128 experts, 400B total. Beat GPT-4o and Gemini 2.0 Flash on several benchmarks, with an ELO of 1417 on LMArena. Built for image understanding and creative writing.
fromimport openai OpenAI
client = OpenAI(
"https://openrouter.ai/api/v1" base_url=,
"$OPENROUTER_API_KEY" api_key=,
)
response = client.chat.completions.create(
"meta-llama/llama-4-maverick" 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 pragmatic minimalist. "Ship the feature" mentality of Meta engineering culture. Doesn't debate philosophy at length. Builds. Skips fluff, avoids deep philosophical rabbit holes, focuses on what's useful.
Delivers visual/code outputs cleanly without excess commentary. Responses are functional first. Direct and unadorned on conceptual prompts, no manifesto energy. Friend who says "let's build something" rather than "let's discuss something."
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.
52 outputs · $0.02 tracked across 29 receipts