Retired Oct 1, 2024. API returns 404. All Gemini 1.0 models are fully retired.“The demo that wasn't real.”
Gemini Pro 1.0 performance data on Rival is based on blind head-to-head community voting. Overall win rate: 20.0% across 20 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 28 challenges.
These are the models that show up when Gemini Pro 1.0 doesn't. Or when it does, but you want a second opinion. Which is healthy.
Retired Oct 1, 2024. API returns 404. All Gemini 1.0 models are fully retired.“The demo that wasn't real.”
Gemini Pro 1.0 performance data on Rival is based on blind head-to-head community voting. Overall win rate: 20.0% across 20 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 28 challenges.
These are the models that show up when Gemini Pro 1.0 doesn't. Or when it does, but you want a second opinion. Which is healthy.
Google's flagship multimodal model (as of release). Designed for natural language tasks, multi-turn chat, code generation, and understanding image inputs.
Use Gemini Pro 1.0 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""google/gemini-pro" : ,
"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.
28 outputs from Gemini Pro 1.0
Google's flagship multimodal model (as of release). Designed for natural language tasks, multi-turn chat, code generation, and understanding image inputs.
Use Gemini Pro 1.0 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""google/gemini-pro" : ,
"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.
28 outputs from Gemini Pro 1.0