Overview

DeepSeek V4 Flash 0731 delivers enterprise-grade reasoning within the Deepseek4 family, optimized for complex task orchestration via our API. Built on 304B parameters with FP8 quantization, this thinking model balances high-performance inference with efficiency, enabling seamless integration into production pipelines without extensive optimization. Developers can immediately leverage the 1,048,576 token context window for extensive document analysis or long-form code generation, ensuring your applications handle massive datasets effortlessly. With an MIT license and enterprise-tier access, it provides the stability required for critical business deployments while maintaining the flexibility needed for rapid prototyping.

For researchers and technical leads, this model excels in bilingual proficiency, delivering robust performance across both Arabic and English benchmarks. It outperforms comparable alternatives in reasoning tasks, making it ideal for specialized research pipelines requiring nuanced language understanding. While operating at a 13x credit multiplier relative to base rates, the value proposition lies in its reduced need for prompt engineering due to superior intrinsic reasoning capabilities. Whether evaluating complex logical structures or processing regional language data, DeepSeek V4 Flash 0731 ensures accurate, context-aware outputs that meet rigorous academic and commercial standards without compromising on speed or reliability.

Specifications

Display Name DeepSeek V4 Flash 0731

Family Deepseek4

Category Thinking

Parameters 304B

Context Window 1,048,576 tokens

Quantization FP8

License MIT

Min Tier Enterprise

Status Available

Pricing

13×

credits per token

1K 13,000 Credits

10K 130,000 Credits

100K 1,300,000 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-flash:0731",
    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-flash:0731",
    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-flash:0731",
            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-flash:0731",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Analyzing entire software repositories for comprehensive bug detection

Summarizing extensive legal contracts and regulatory compliance documents

Solving complex mathematical problems with detailed logical reasoning

Refactoring large legacy codebases across multiple project files

Processing long scientific research papers for key insights

In-Depth Guide

Full Guide

Complete Guide to DeepSeek V4 Flash 0731 — LLM Resayil

DeepSeek V4 Flash 0731