Overview

deepseek v4 flash represents a high-performance iteration within the DeepSeek family, optimized for rapid inference and low-latency interactions. As a dedicated chat model, it excels in conversational workflows where response time is critical without sacrificing contextual understanding. Developers integrating this model via LLM Resayil gain access to robust reasoning capabilities tailored for real-time applications, such as customer support agents, interactive coding assistants, and dynamic content generation. The architecture balances computational efficiency with output quality, making it an ideal choice for production environments requiring scalable throughput.

Accessible from the starter tier, deepseek v4 flash operates with a 2x credit multiplier relative to the base rate, offering a cost-effective solution for high-volume tasks. This pricing structure ensures that teams can deploy sophisticated language capabilities without incurring prohibitive operational costs. The model maintains strict adherence to instruction following and safety protocols, reducing the need for extensive post-processing. By leveraging this endpoint, engineers can build resilient systems that handle complex queries efficiently. Whether prototyping new features or scaling existing services, deepseek v4 flash provides the reliability and speed necessary to meet demanding SLAs while maintaining budgetary control.

Specifications

Display Name DeepSeek V4 Flash

Family DeepSeek

Category Chat

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

Use Cases

Handling real-time customer support chat interactions

Rapid code snippet generation and debugging assistance

Quick summarization of lengthy text documents

Interactive tutoring for complex STEM subject matters

Fast data extraction from unstructured text inputs

In-Depth Guide

Full Guide

Complete Guide to deepseek v4 flash — LLM Resayil

DeepSeek V4 Flash