DeepSeek V4 Flash performance data on Rival is based on blind head-to-head community voting. 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 53 challenges.
DeepSeek V4 Flash 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.
DeepSeek V4 Flash performance data on Rival is based on blind head-to-head community voting. 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 53 challenges.
DeepSeek V4 Flash 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.
DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. Designed for fast inference and high-throughput workloads, with hybrid attention for long-context processing and configurable reasoning modes. Well suited for coding assistants, chat systems, and agent workflows where responsiveness and cost efficiency are important.
Use DeepSeek V4 Flash 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""deepseek/deepseek-v4-flash" : ,
"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.
53 outputs from DeepSeek V4 Flash
DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. Designed for fast inference and high-throughput workloads, with hybrid attention for long-context processing and configurable reasoning modes. Well suited for coding assistants, chat systems, and agent workflows where responsiveness and cost efficiency are important.
Use DeepSeek V4 Flash 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""deepseek/deepseek-v4-flash" : ,
"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.
53 outputs from DeepSeek V4 Flash