Mistral Devstral Medium performance data on Rival is based on blind head-to-head community voting. Overall win rate: 35.8% across 120 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 Devstral Medium 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 Devstral Medium performance data on Rival is based on blind head-to-head community voting. Overall win rate: 35.8% across 120 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 Devstral Medium 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.
Devstral Medium is a high-performance code generation and agentic reasoning model developed jointly by Mistral AI and All Hands AI. Positioned as a step up from Devstral Small, it achieves 61.6% on SWE-Bench Verified, placing it ahead of Gemini 2.5 Pro and GPT-4.1 in code-related tasks, at a fraction of the cost.
Use Mistral Devstral Medium 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/devstral-medium" : ,
"messages""role""user""content""Hello!" : [{: , : }]
}
)
print(response.json())Replace $OPENROUTER_API_KEY with your API key from openrouter.ai/keys
The AI that took a corporate communications course and never recovered. Every character sounds like they are reading from the same HR training manual.
Picks Shawshank Redemption. Sentience test reads like two coworkers having a polite disagreement at a team offsite. The pirate, knight, and hacker all speak in the same measured, reasonable tone. Built for code, not for vibes.
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 Devstral Medium
Try Mistral Devstral Medium
Devstral Medium is a high-performance code generation and agentic reasoning model developed jointly by Mistral AI and All Hands AI. Positioned as a step up from Devstral Small, it achieves 61.6% on SWE-Bench Verified, placing it ahead of Gemini 2.5 Pro and GPT-4.1 in code-related tasks, at a fraction of the cost.
Use Mistral Devstral Medium 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/devstral-medium" : ,
"messages""role""user""content""Hello!" : [{: , : }]
}
)
print(response.json())Replace $OPENROUTER_API_KEY with your API key from openrouter.ai/keys
The AI that took a corporate communications course and never recovered. Every character sounds like they are reading from the same HR training manual.
Picks Shawshank Redemption. Sentience test reads like two coworkers having a polite disagreement at a team offsite. The pirate, knight, and hacker all speak in the same measured, reasonable tone. Built for code, not for vibes.
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 Devstral Medium
Try Mistral Devstral Medium