Overview

Deepseek v4 pro 0813 delivers enterprise-grade performance through its massive 1650B parameter architecture optimized with FP8 quantization. Designed for complex reasoning tasks, this thinking model supports a massive 1,048,576 token context window, enabling comprehensive document analysis and long-form code generation without truncation. Developers can integrate this capability immediately via our standard API endpoints, ensuring seamless deployment into existing pipelines without infrastructure overhead. The starter tier accessibility allows teams to prototype high-level reasoning applications quickly while maintaining production-grade reliability. Benchmark data confirms superior handling of nuanced logic compared to previous generations, satisfying rigorous research requirements for accuracy and depth.

For enterprises requiring native linguistic precision, this model offers robust bilingual proficiency in English and Arabic, eliminating the need for translation layers in critical workflows. Pricing is transparent through our credit system, with a 2x multiplier reflecting the advanced computational resources required for high-fidelity outputs. This structure ensures cost predictability for high-volume operations while granting access to state-of-the-art intelligence. Decision makers can verify production readiness through available performance metrics without needing direct sales consultation. Choose Deepseek v4 pro 0813 to scale intelligent applications with confidence and linguistic capability.

Specifications

Display Name deepseek v4 pro 0813

Family Deepseek4

Category Thinking

Parameters 1650B

Context Window 1,048,576 tokens

Quantization FP8

Min Tier Starter

Status Available

Pricing

2×

credits per token

1K 2,000 Credits

10K 20,000 Credits

100K 200,000 Credits

View Pricing Plans

Code Examples

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:0813",
    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:0813",
    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:0813",
            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:0813",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Analyzing entire software codebases for refactoring and optimization

Summarizing extensive legal documents and contract clauses accurately

Processing full book manuscripts for structural editing and feedback

Solving complex mathematical problems with enhanced reasoning capabilities

Synthesizing insights from massive datasets and research papers

In-Depth Guide

Full Guide

Complete Guide to deepseek v4 pro 0813 — LLM Resayil

deepseek v4 pro 0813