DeepSeek V4.1 Flash 1017 | DeepSeek Multimodal LLM API Pricing

Name: DeepSeek V4.1 Flash 1017 API
Brand: deepseek
Price: 0.3 USD
Availability: InStock

deepseek/deepseek-v4.1-flash-1017

發布時間: 2026-10-17

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

DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek V4.1 Flash, served by DeepSeek under the deepseek-flash API model name. V4.1 Flash is a sparse mixture-of-experts model...

定價

按用量付費

無需預付費用，僅按實際使用量付費

輸入$0.30 / M Tokens

輸出$1.20 / M Tokens

Cache Read$0.0060 / M Tokens

試用模型

deepseek/deepseek-v4.1-flash-1017

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: 0,
});

try {
  const response = await client.chat.completions.create({
    model: 'deepseek/deepseek-v4.1-flash-1017',
    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-1017',
    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;
}

模型介紹

DeepSeek: DeepSeek V4.1 Flash 1017

DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek V4.1 Flash, served by DeepSeek under the deepseek-flash API model name. V4.1 Flash is a sparse mixture-of-experts model...

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: 655360 tokens
Max Output: 393216 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_completion_tokens, 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_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	655360 tokens
Max Output	393216 tokens
Input	Text, Image
Output	Text
Vision	Supported
Function Calling	Supported
Structured Outputs	Supported
Release Date	October 17, 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-1017

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-1017",
    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-1017",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Notes

Model: deepseek/deepseek-v4.1-flash-1017
Provider: deepseek

資訊

提供商deepseek

類型llm

支援功能

輸入

文字影像

輸出

文字

上下文1,048,576

最大輸出393,216

視覺✓ 支援

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-v4.1-flash-1017

關於 DeepSeek V4.1 Flash 1017 的常見問題

DeepSeek V4.1 Flash 1017 API 多少錢?+

WaveSpeedAI 定價:輸入每百萬 token $0.30,輸出每百萬 token $1.20。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 V4.1 Flash 1017 的上下文視窗有多大?+

DeepSeek V4.1 Flash 1017 每次請求最多支援 1049K 上下文 token,輸出最多 393K token。

DeepSeek V4.1 Flash 1017 是否相容 OpenAI?+

WaveSpeedAI 透過 https://llm.wavespeed.ai/v1 的 OpenAI 相容 Chat Completions 介面提供 DeepSeek V4.1 Flash 1017。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 V4.1 Flash 1017?+

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。

定價