Aurora Alpha performance data on Rival is based on blind head-to-head community voting. Overall win rate: 29.6% across 27 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 48 challenges.
These are the models that show up when Aurora Alpha doesn't. Or when it does, but you want a second opinion. Which is healthy.
Aurora Alpha performance data on Rival is based on blind head-to-head community voting. Overall win rate: 29.6% across 27 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 48 challenges.
These are the models that show up when Aurora Alpha doesn't. Or when it does, but you want a second opinion. Which is healthy.
Aurora Alpha is a cloaked reasoning model provided by OpenRouter to gather community feedback. Designed for speed, it is built for coding assistants, real-time conversational applications, and agentic workflows. Default reasoning effort is set to medium for fast responses; for agentic coding use cases, high effort is recommended.
Use Aurora Alpha 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""openrouter/aurora-alpha" : ,
"messages""role""user""content""Hello!" : [{: , : }]
}
)
print(response.json())Replace $OPENROUTER_API_KEY with your API key from openrouter.ai/keys
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.
48 outputs from Aurora Alpha
Aurora Alpha is a cloaked reasoning model provided by OpenRouter to gather community feedback. Designed for speed, it is built for coding assistants, real-time conversational applications, and agentic workflows. Default reasoning effort is set to medium for fast responses; for agentic coding use cases, high effort is recommended.
Use Aurora Alpha 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""openrouter/aurora-alpha" : ,
"messages""role""user""content""Hello!" : [{: , : }]
}
)
print(response.json())Replace $OPENROUTER_API_KEY with your API key from openrouter.ai/keys
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.
48 outputs from Aurora Alpha