Deepseek4 1600B parameter (FP8) model with 524,288 token context window.
deepseek v4 pro preview stands as a flagship thinking model within the Deepseek4 family, engineered for complex reasoning tasks requiring extensive context retention. With 1600B parameters quantized to FP8, this architecture delivers exceptional performance on multi-step problem solving and advanced code generation. Developers gain access to a massive 524,288 token context window, enabling seamless analysis of large codebases or lengthy documentation without fragmentation. This capacity ensures your API integrations handle substantial data payloads efficiently, reducing the need for complex chunking strategies during initial implementation.
For research pipelines and enterprise deployments, deepseek v4 pro preview offers robust bilingual proficiency, specifically optimized for Arabic and English workflows. This makes it an ideal choice for regional applications requiring nuanced language understanding alongside technical precision. While operating at a 2x credit multiplier relative to the base rate, the model justifies the cost through production-ready reliability and superior benchmark scores against comparable alternatives. Accessible from the starter tier, teams can immediately validate performance metrics and pricing structures in supported currencies including KWD, SAR, and AED. Evaluate the full benchmark comparisons and integrate via our standard API endpoints to accelerate your development cycle. This transparency ensures budget predictability for all projects.
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-pro:preview",
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-pro:preview",
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-pro:preview",
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-pro:preview",
"messages": [
{"role": "user", "content": "Hello!"}
]
}'