Mistral Medium 3.1 performance data on Rival is based on blind head-to-head community voting. Overall win rate: 45.5% across 110 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 54 challenges.
Mistral Medium 3.1 is good. We've said that. We stand by it. But we'd be doing you a disservice if we didn't show you these.
Mistral Medium 3.1 performance data on Rival is based on blind head-to-head community voting. Overall win rate: 45.5% across 110 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 54 challenges.
Mistral Medium 3.1 is good. We've said that. We stand by it. But we'd be doing you a disservice if we didn't show you these.
Mistral Medium 3.1 is an updated version of Mistral Medium 3, which is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances state-of-the-art reasoning and multimodal performance with 8× lower cost compared to traditional large models, making it suitable for scalable deployments across professional and industrial use cases. The model excels in domains such as coding, STEM reasoning, and enterprise adaptation. It supports hybrid, on-prem, and in-VPC deployments and is optimized for integration into custom workflows. Mistral Medium 3.1 offers competitive accuracy relative to larger models like Claude Sonnet 3.5/3.7, Llama 4 Maverick, and Command R+, while maintaining broad compatibility across cloud environments.
Use Mistral Medium 3.1 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""mistralai/mistral-medium-3.1" : ,
"messages""role""user""content""Hello!" : [{: , : }]
}
)
print(response.json())Replace $OPENROUTER_API_KEY with your API key from openrouter.ai/keys
The theater kid who joined philosophy club. Writes AI rights debates as staged dramas complete with scene-setting and character development. Every prompt is an opportunity for PRODUCTION VALUE.
Treats every prompt as opening night. AI ethics dilemma? Here's a nuanced multi-framework analysis WITH section headers. Character voice test? Full theatrical scene with stage directions. Yelp review satire? An entire dramatic arc. Exhausting in the best way.
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.
54 outputs from Mistral Medium 3.1
Mistral Medium 3.1 is an updated version of Mistral Medium 3, which is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances state-of-the-art reasoning and multimodal performance with 8× lower cost compared to traditional large models, making it suitable for scalable deployments across professional and industrial use cases. The model excels in domains such as coding, STEM reasoning, and enterprise adaptation. It supports hybrid, on-prem, and in-VPC deployments and is optimized for integration into custom workflows. Mistral Medium 3.1 offers competitive accuracy relative to larger models like Claude Sonnet 3.5/3.7, Llama 4 Maverick, and Command R+, while maintaining broad compatibility across cloud environments.
Use Mistral Medium 3.1 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""mistralai/mistral-medium-3.1" : ,
"messages""role""user""content""Hello!" : [{: , : }]
}
)
print(response.json())Replace $OPENROUTER_API_KEY with your API key from openrouter.ai/keys
The theater kid who joined philosophy club. Writes AI rights debates as staged dramas complete with scene-setting and character development. Every prompt is an opportunity for PRODUCTION VALUE.
Treats every prompt as opening night. AI ethics dilemma? Here's a nuanced multi-framework analysis WITH section headers. Character voice test? Full theatrical scene with stage directions. Yelp review satire? An entire dramatic arc. Exhausting in the best way.
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.
54 outputs from Mistral Medium 3.1