DeepSeek V4 Flash API 多少錢?

WaveSpeedAI 定價:輸入每百萬 token $0.17,輸出每百萬 token $0.34。Prompt 快取與批次處理分別計費,可顯著降低長上下文、高重複任務的實際成本。

DeepSeek V4 Flash 的上下文視窗有多大?

DeepSeek V4 Flash 每次請求最多支援 1049K 上下文 token,輸出最多 384K token。

DeepSeek V4 Flash 是否相容 OpenAI?

WaveSpeedAI 透過 https://llm.wavespeed.ai/v1 的 OpenAI 相容 Chat Completions 介面提供 DeepSeek V4 Flash。大多數 OpenAI SDK 用戶端只需更換 base URL 和 API Key；選用欄位取決於具體模型。

如何開始使用 DeepSeek V4 Flash?

登入 WaveSpeedAI，在 Access Keys 中建立 API Key，然後使用上方顯示的 model id 向 https://llm.wavespeed.ai/v1/chat/completions 發送請求。模型可用性、能力和價格請以目前模型目錄為準。

DeepSeek V4 Flash | Cost-Efficient 284B MoE LLM API

Name: DeepSeek V4 Flash API
Brand: deepseek
Price: 0.17 USD
Availability: InStock

deepseek/deepseek-v4-flash

發布時間: 2026-04-24

1,048,576 context · $0.17/M input tokens · $0.34/M output tokens

DeepSeek V4 Flash is DeepSeek's efficiency-first open-source model released in April 2026, built on a 284B-parameter Mixture-of-Experts architecture with just 13B parameters active per token — the smallest activation footprint among current Tier-1 models. It shares the same 1M-token context window and hybrid attention design as V4 Pro, delivering near-equivalent reasoning capability (LiveCodeBench 91.6, Codeforces 3052, SWE-bench Verified 79.0) while running significantly faster and at dramatically lower cost. Pre-trained on 32T tokens, V4 Flash is purpose-built for high-throughput, latency-sensitive scenarios such as coding assistants, conversational agents, and batch processing pipelines. It supports thinking and non-thinking modes, function calling, JSON output, and FIM completion.

定價

按用量付費

無需預付費用，僅按實際使用量付費

輸入$0.17 / M Tokens

輸出$0.34 / M Tokens

Cache Read$0.03 / M Tokens

試用模型

deepseek/deepseek-v4-flash

線上

嗨！我是樂於助人的 AI 助理。有什麼可以幫你的嗎？

準備在本機編碼 Agent 中使用這個模型嗎？Agent 設定

API 使用

使用以下程式碼範例整合我們的 API：

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: 2,
});

try {
  const response = await client.chat.completions.create({
    model: 'deepseek/deepseek-v4-flash',
    messages: [{ role: 'user', content: 'Hello!' }],
  });
  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: 2,
});

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

模型介紹

Deepseek deepseek-v4-flash

DeepSeek-V4-Flash is DeepSeek's cost-efficient open-source model, released on April 24, 2026. It is a 284B parameter Mixture-of-Experts (MoE) language model with only 13B active parameters, pre-trained on 32T tokens, supporting a context length of one million tokens. V4-Flash delivers reasoning performance approaching V4-Pro while being significantly faster and cheaper — making it ideal for high-volume, latency-sensitive workloads.

Why It Looks Great

Mixture-of-Experts architecture with 284B total parameters and only 13B active — the smallest activation among Tier-1 models
1000000 context window powered by Compressed Sparse Attention (CSA) and DeepSeek Sparse Attention (DSA)
Near V4-Pro reasoning performance at a fraction of the cost

Key Features

Context Window: 1000000 tokens
Max Output: 384000 tokens
Vision: Not Supported
Function Calling: Supported
Thinking Mode: Supported (non-thinking / high / max)
JSON Output: Supported
FIM Completion: Supported (non-thinking mode only)

Benchmarks

Benchmark	V4-Flash	V4-Pro	Claude Opus 4.6	GPT-5.4
SWE-bench Verified	79.0	80.6	80.8	—
LiveCodeBench	91.6	93.5	88.8	91.7
Codeforces Rating	3052	3206	—	3168
MMLU-Pro	86.2	87.5	89.1	87.5
Terminal Bench 2.0	56.9	67.9	65.4	75.1

Specifications

Specification	Value
Provider	Deepseek
Model Type	Large Language Model (LLM)
Architecture	Mixture-of-Experts (MoE)
Total Parameters	284B (13B active)
Context Window	1000000 tokens
Max Output	384000 tokens
Input	Text
Output	Text
Vision	Not Supported
Function Calling	Supported
Thinking Mode	Supported (high / max)
Release Date	April 24, 2026

How to Use

Write your prompt — describe the task, provide context, and specify 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-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-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-flash",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Notes

Model: deepseek/deepseek-v4-flash
Provider: Deepseek
Open-source weights available on HuggingFace and ModelScope
Supports both OpenAI and Anthropic API formats
For simple Agent tasks, V4-Flash performs on par with V4-Pro; for complex agentic workflows, consider V4-Pro

資訊

提供商deepseek

類型llm

支援功能

輸入

文字

輸出

文字

上下文1,048,576

最大輸出384,000

視覺-

函式呼叫✓ 支援

API 存取指南

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

API 端點chat/completions

Model IDdeepseek/deepseek-v4-flash

定價