Deepseek_v41 763B parameter (FP8) model with 1,048,576 token context window.
DeepSeek V4.1 Flash delivers enterprise-grade performance with 763 billion parameters optimized in FP8 quantization. Designed for complex reasoning tasks, this thinking model supports a massive 1,048,576 token context window, enabling seamless analysis of extensive codebases or long-form documents without fragmentation. Developers can integrate immediately via our standard API endpoints, ensuring your first call executes within minutes. The architecture balances high-level cognition with efficiency, making it ideal for building robust applications that require deep contextual understanding and precise output generation across multiple turns. Access requires an enterprise tier account to accommodate the computational resources needed for such scale.
For researchers and decision-makers, DeepSeek V4.1 Flash excels in bilingual proficiency, delivering state-of-the-art results in both Arabic and English benchmarks. The MIT license facilitates unrestricted deployment in production environments, while the 18x credit multiplier reflects its premium computational density. This model is production-ready for high-stakes workflows, offering the reliability required for enterprise adoption. Whether validating research pipelines or deploying customer-facing solutions, you gain access to a powerful engine capable of handling nuanced linguistic tasks without compromising on speed or accuracy. Pricing structures align with enterprise expectations for high-value AI infrastructure.
from openai import OpenAI
client = OpenAI(
base_url="https://llmapi.resayil.io/v1/",
api_key="YOUR_API_KEY"
)
response = client.chat.completions.create(
model="deepseek-v4.1-flash",
messages=[
{"role": "user", "content": "Hello!"}
]
)
print(response.choices[0].message.content)import anthropic
client = anthropic.Anthropic(
base_url="https://llmapi.resayil.io/v1",
api_key="YOUR_API_KEY"
)
message = client.messages.create(
model="deepseek-v4.1-flash",
max_tokens=1024,
messages=[
{"role": "user", "content": "Hello!"}
]
)
print(message.content[0].text)const response = await fetch(
"https://llmapi.resayil.io/v1/chat/completions",
{
method: "POST",
headers: {
"Content-Type": "application/json",
"Authorization": "Bearer YOUR_API_KEY"
},
body: JSON.stringify({
model: "deepseek-v4.1-flash",
messages: [
{ role: "user", content: "Hello!" }
]
})
}
);
const data = await response.json();
console.log(data.choices[0].message.content);curl https://llmapi.resayil.io/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model": "deepseek-v4.1-flash",
"messages": [
{"role": "user", "content": "Hello!"}
]
}'