DeepSeek V4.1 Flash | DeepSeek Multimodal LLM API Pricing

Name: DeepSeek V4.1 Flash API
Brand: deepseek
Price: 0.3 USD
Availability: InStock

deepseek/deepseek-v4.1-flash

Yayın tarihi: 2026-09-10

1,048,576 context · $0.30/M input tokens · $1.20/M output tokens

DeepSeek V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on...

Fiyatlandırma

Kullandıkça öde

Ön ödeme yok, yalnızca kullandığınız kadar ödeyin

Giriş$0.30 / M Tokens

Çıkış$1.20 / M Tokens

Cache Read$0.0060 / M Tokens

Modeli dene

deepseek/deepseek-v4.1-flash

Çevrimiçi

Merhaba! Yardımcı bir yapay zeka asistanıyım. Size nasıl yardımcı olabilirim?

Bu modeli yerel bir coding agent içinde kullanmaya hazır mısınız?Agent setup

API Kullanımı

API'mizle entegre etmek için aşağıdaki kod örneklerini kullanın:

import OpenAI from 'openai';

if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
  apiKey: process.env.WAVESPEED_API_KEY,
  baseURL: 'https://llm.wavespeed.ai/v1',
  timeout: 120_000,
  maxRetries: 0,
});

try {
  const response = await client.chat.completions.create({
    model: 'deepseek/deepseek-v4.1-flash',
    messages: [{ role: 'user', content: 'Hello!' }],
  });
  const body = response as typeof response & { error?: { message?: string } };
  if (body.error) throw new Error(body.error.message || 'LLM generation failed');
  console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
  console.error('LLM request failed:', error);
  process.exitCode = 1;
}

import OpenAI from 'openai';

if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
  apiKey: process.env.WAVESPEED_API_KEY,
  baseURL: 'https://llm.wavespeed.ai/v1',
  timeout: 120_000,
  maxRetries: 0,
});

try {
  const response = await client.chat.completions.create({
    model: 'deepseek/deepseek-v4.1-flash',
    messages: [{ role: 'user', content: 'Hello!' }],
  });
  const body = response as typeof response & { error?: { message?: string } };
  if (body.error) throw new Error(body.error.message || 'LLM generation failed');
  console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
  console.error('LLM request failed:', error);
  process.exitCode = 1;
}

Model Tanıtımı

DeepSeek: DeepSeek V4.1 Flash

This model is available through the WaveSpeed AI OpenAI-compatible API for chat completions and compatible application workflows.

Why It Looks Great

text+image->text architecture for Text, Image to Text workloads
1048576 context window for long prompts, document analysis, and multi-turn workflows
Vision input support for image understanding and multimodal tasks
Function calling and tool-use support for agentic application workflows
Structured output support for JSON responses and schema-constrained generation
Reasoning controls available through supported parameters

Key Features

Context Window: 1048576 tokens
Max Input: 917504 tokens
Max Output: 131072 tokens
Vision: Supported
Function Calling: Supported
Structured Outputs: Supported
Image Generation: Not listed
Audio Input: Not listed
Supported Parameters: frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, min_p, parallel_tool_calls, presence_penalty, reasoning, reasoning_effort, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_a, top_k, top_logprobs, top_p

Specifications

Specification	Value
Provider	deepseek
Model Type	Chat Completions model
Architecture	text+image->text
Context Window	1048576 tokens
Max Input	917504 tokens
Max Output	131072 tokens
Input	Text, Image
Output	Text
Vision	Supported
Function Calling	Supported
Structured Outputs	Supported
Release Date	September 10, 2026

How to Use

Write your prompt - describe the task, provide context, and specify the desired output format.
Submit - the model processes your request and returns the response.

API Integration

Base URL: https://llm.wavespeed.ai/v1 API Endpoint: chat/completions Model ID: deepseek/deepseek-v4.1-flash

API Usage

Python SDK

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://llm.wavespeed.ai/v1"
)

response = client.chat.completions.create(
    model="deepseek/deepseek-v4.1-flash",
    messages=[{"role": "user", "content": "Hello!"}]
)

print(response.choices[0].message.content)

cURL

curl https://llm.wavespeed.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "deepseek/deepseek-v4.1-flash",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Notes

Model: deepseek/deepseek-v4.1-flash
Provider: deepseek

Bilgi

Sağlayıcıdeepseek

Türllm

Desteklenen İşlevsellik

Giriş

MetinGörsel

Çıkış

Metin

Bağlam1,048,576

Maks. Çıkış384,000

Vision✓ Destekleniyor

Function Calling✓ Destekleniyor

API Erişim Kılavuzu

Base URLhttps://llm.wavespeed.ai/v1

API Endpointchat/completions

Model IDdeepseek/deepseek-v4.1-flash

DeepSeek V4.1 Flash hakkında sık sorulan sorular

DeepSeek V4.1 Flash API ücreti ne kadar?+

WaveSpeedAI fiyatlandırması: milyon giriş tokenı başına $0.30 ve milyon çıkış tokenı başına $1.20. Prompt caching ve toplu işleme ayrı faturalanır ve uzun, tekrar eden yüklerde etkin maliyeti düşürür.

DeepSeek V4.1 Flash'in bağlam penceresi nedir?+

DeepSeek V4.1 Flash istek başına 1049K bağlam tokenını ve 384K çıkış tokenını destekler.

DeepSeek V4.1 Flash OpenAI uyumlu mu?+

WaveSpeedAI, DeepSeek V4.1 Flash modelini https://llm.wavespeed.ai/v1 adresindeki OpenAI uyumlu Chat Completions arayüzü üzerinden sunar. Çoğu OpenAI SDK istemcisinde base URL ve API anahtarını değiştirmek yeterlidir; isteğe bağlı alanlar modele bağlıdır.

DeepSeek V4.1 Flash'e nasıl başlarım?+

WaveSpeedAI’a giriş yapın, Access Keys bölümünde bir API anahtarı oluşturun ve yukarıdaki model id ile https://llm.wavespeed.ai/v1/chat/completions adresine istek gönderin. Güncel kullanılabilirlik, özellikler ve fiyatlar için model kataloğunu kontrol edin.