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deepseek
deepseek/deepseek-v3.2-exp

deepseek/deepseek-v3.2-exp

發布時間: 2025-09-29

163,840 context · $0.27/M input tokens · $0.41/M output tokens

DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...

定價

按用量付費

無需預付費用,僅按實際使用量付費

輸入$0.27 / M Tokens
輸出$0.41 / M Tokens

試用模型

deepseek/deepseek-v3.2-exp
線上
deepseek
嗨!我是樂於助人的 AI 助理。有什麼可以幫你的嗎?

API 使用

使用以下程式碼範例整合我們的 API:

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-v3.2-exp",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

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

模型介紹

Deepseek deepseek-v3.2-exp

DeepSeek-V3

DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism designed to improve training and inference efficiency in long-context scenarios while maintaining output quality. Users can control the reasoning behaviour with the reasoning enabled boolean. Learn more in our docs

The model was trained under conditions aligned with V3.1-Terminus to enable direct comparison. Benchmarking shows performance roughly on par with V3.1 across reasoning, coding, and agentic tool-use tasks, with minor tradeoffs and gains depending on the domain. This release focuses on validating architectural optimizations for extended context lengths rather than advancing raw task accuracy, making it primarily a research-oriented model for exploring e


Why It Looks Great

  • Large Language Model architecture for efficient processing
  • 163840 context window for long document handling
  • Competitive pricing at $0.3/$0.4 per million tokens

Key Features

  • Context Window: 163840 tokens
  • Max Output: 65536 tokens
  • Vision: Supported
  • Function Calling: Supported

Specifications

SpecificationValue
ProviderDeepseek
Model TypeLarge Language Model (LLM)
ArchitectureN/A
Context Window163840 tokens
Max Output65536 tokens
InputText
OutputText
VisionSupported
Function CallingSupported

Pricing

Token TypeCost per Million Tokens
Input$0.3
Output$0.4

How to Use

  1. Write your prompt — describe the task, provide context, and specify desired output format.
  2. 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-v3.2-exp


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-v3.2-exp",
    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-v3.2-exp",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Notes

  • Model: deepseek/deepseek-v3.2-exp
  • Provider: Deepseek

資訊

提供商deepseek
類型llm

支援功能

輸入
文字
輸出
文字
上下文163,840
最大輸出65,536
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-v3.2-exp

DeepSeek V3.2 Exp API

deepseek/deepseek-v3.2-exp

DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...

輸入

$0.27 /M

輸出

$0.41 /M

上下文

164K

最大輸出

66K

工具調用

支援

在 WaveSpeedAI 試用 DeepSeek V3.2 Exp

透過我們的統一 API 接入 DeepSeek V3.2 Exp — 相容 OpenAI、無冷啟動、透明計費。

關於 DeepSeek V3.2 Exp 的常見問題

DeepSeek V3.2 Exp API 多少錢?+

WaveSpeedAI 定價:輸入每百萬 token $0.27,輸出每百萬 token $0.41。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 V3.2 Exp 的上下文視窗有多大?+

DeepSeek V3.2 Exp 每次請求最多支援 164K 上下文 token,輸出最多 66K token。

DeepSeek V3.2 Exp 是否相容 OpenAI?+

是的。WaveSpeedAI 透過 https://llm.wavespeed.ai/v1 的 OpenAI 相容端點提供 DeepSeek V3.2 Exp。將官方 OpenAI SDK 的 base URL 指向該位址,使用 WaveSpeedAI 的 API Key 即可,無需其他程式碼變更。

如何開始使用 DeepSeek V3.2 Exp?+

登入 WaveSpeedAI,在 Access Keys 建立 API Key,使用上方顯示的 model id 向 https://llm.wavespeed.ai/v1/chat/completions 發送請求。新帳號將獲得免費額度,用於試用 DeepSeek V3.2 Exp。

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