deepseek/deepseek-v3.2-exp
Yayın tarihi: 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...
Kullandıkça öde
Ön ödeme yok, yalnızca kullandığınız kadar ödeyin
API'mizle entegre etmek için aşağıdaki kod örneklerini kullanın:
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)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
| Specification | Value |
|---|---|
| Provider | Deepseek |
| Model Type | Large Language Model (LLM) |
| Architecture | N/A |
| Context Window | 163840 tokens |
| Max Output | 65536 tokens |
| Input | Text |
| Output | Text |
| Vision | Supported |
| Function Calling | Supported |
| Token Type | Cost per Million Tokens |
|---|---|
| Input | $0.3 |
| Output | $0.4 |
Base URL: https://llm.wavespeed.ai/v1 API Endpoint: chat/completions Model ID: deepseek/deepseek-v3.2-exp
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 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!"}]
}'
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...
Giriş
$0.27 /M
Çıkış
$0.41 /M
Bağlam
164K
Maks. Çıkış
66K
Araç Kullanımı
Destekleniyor
Birleşik API'miz aracılığıyla DeepSeek V3.2 Exp'e erişin — OpenAI uyumlu, soğuk başlatma yok, şeffaf fiyatlandırma.
WaveSpeedAI fiyatlandırması: milyon giriş tokenı başına $0.27 ve milyon çıkış tokenı başına $0.41. Prompt caching ve toplu işleme ayrı faturalanır ve uzun, tekrar eden yüklerde etkin maliyeti düşürür.
DeepSeek V3.2 Exp istek başına 164K bağlam tokenını ve 66K çıkış tokenını destekler.
Evet. WaveSpeedAI, DeepSeek V3.2 Exp modelini https://llm.wavespeed.ai/v1 adresindeki OpenAI uyumlu endpoint üzerinden sunar. Resmi OpenAI SDK'sını WaveSpeedAI API anahtarınızla bu base URL'ye yöneltin — başka kod değişikliği gerekmez.
WaveSpeedAI'a giriş yapın, Access Keys'te bir API anahtarı oluşturun, ardından yukarıda gösterilen model id ile https://llm.wavespeed.ai/v1/chat/completions adresine bir istek gönderin. Yeni hesaplar DeepSeek V3.2 Exp'i değerlendirmek için ücretsiz krediler alır.