Llama 3.1 70B (Instruct) performance data on Rival is based on blind head-to-head community voting. Overall win rate: 19.4% across 62 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 51 challenges.
We built Llama 3.1 70B (Instruct) a whole page. Gave it the spotlight. And now, in the spirit of fairness, here are models that would like a word.
Llama 3.1 70B (Instruct) performance data on Rival is based on blind head-to-head community voting. Overall win rate: 19.4% across 62 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 51 challenges.
We built Llama 3.1 70B (Instruct) a whole page. Gave it the spotlight. And now, in the spirit of fairness, here are models that would like a word.
Llama 3.1 70B offers a dramatically expanded context window and improved performance on mathematical reasoning and general knowledge tasks.
Use Llama 3.1 70B (Instruct) 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-3.1-70b-instruct" : ,
"messages""role""user""content""Hello!" : [{: , : }]
}
)
print(response.json())Replace $OPENROUTER_API_KEY with your API key from openrouter.ai/keys
The competent middle sibling who actually finished the assignment. Earnest, dependable, and occasionally charming. The Llama family's valedictorian.
Says "Great question!" and actually means it. Logic puzzles land correctly. Creative prompts get earnest attempts. HTML might not win design awards, but at least it renders. The responsible friend who shows up with snacks.
Taste is judged on an uncapped scale where 100 is the reference, originality first. The space past 100 is the 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.
51 outputs from Llama 3.1 70B (Instruct)
Llama 3.1 70B offers a dramatically expanded context window and improved performance on mathematical reasoning and general knowledge tasks.
Use Llama 3.1 70B (Instruct) 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-3.1-70b-instruct" : ,
"messages""role""user""content""Hello!" : [{: , : }]
}
)
print(response.json())Replace $OPENROUTER_API_KEY with your API key from openrouter.ai/keys
The competent middle sibling who actually finished the assignment. Earnest, dependable, and occasionally charming. The Llama family's valedictorian.
Says "Great question!" and actually means it. Logic puzzles land correctly. Creative prompts get earnest attempts. HTML might not win design awards, but at least it renders. The responsible friend who shows up with snacks.
Taste is judged on an uncapped scale where 100 is the reference, originality first. The space past 100 is the 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.
51 outputs from Llama 3.1 70B (Instruct)