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DeepSeek V3.2 Exp Thinking
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DeepSeek V3.2 Exp Thinking

DeepSeek-V3.2-Exp-Think
DeepSeek
The model DeepSeek-V3.2-Exp-Think is officially named deepseek-reasoner. It is an experimental version. As an intermediate step towards the next-generation architecture, V3.2-Exp introduces DeepSeek Sparse Attention (a sparse attention mechanism) based on V3.1-Terminus, exploring and validating exploratory optimizations for training and inference efficiency on long texts.

Pricing

  • Input Tokens: $0.274 /M tokens
  • Output Tokens: $0.411 /M tokens
  • Cache Read: $0.0274 /M tokens

Input Modalities

  • Text

Output Modalities

  • Text

Capabilities

  • Thinking
  • Tools
  • Tool calling
  • Structured outputs

Providers

DeepSeek deepseek-deepseek-v3.2-exp-think
Pricing$0.274$0.411
Cache$0.0274
Context131K
Max output64K
Latency2.0S
Throughput22.8TPS
Uptime
0.00% uptime 2 days ago
0.00% uptime yesterday
0.00% uptime today
Baidu baidu-deepseek-v3.2-exp-think
Pricing$0.274$0.411
Cache$0.0274
Context128K
Max output16K
Latency2.0S
Throughput22.8TPS
Uptime
0.00% uptime 2 days ago
0.00% uptime yesterday
0.00% uptime today

Performance for DeepSeek-V3.2-Exp-Think

Uptime is the percentage of requests that succeeded over the past 72 hours. AIHubMix continuously monitors every provider and automatically retries with the next-best provider when one returns an error or responds too slowly; Latency is total round-trip time (lower is better); Throughput is how fast the model writes (tokens per second, higher is better).

Uptime
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Latency
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Throughput
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Try this model

Python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AIHUBMIX_API_KEY"],
    base_url="https://shkq.org/v1",
)

response = client.chat.completions.create(
    model="DeepSeek-V3.2-Exp-Think",
    messages=[
      {
        "role": "user",
        "content": "Hello, how are you?"
      }
    ],
    max_tokens=1024,
    stream=False,
)

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

Frequently asked questions

What is DeepSeek V3.2 Exp Thinking?

The model DeepSeek-V3.2-Exp-Think is officially named deepseek-reasoner. It is an experimental version. As an intermediate step towards the next-generation architecture, V3.2-Exp introduces DeepSeek Sparse Attention (a sparse attention mechanism) based on V3.1-Terminus, exploring and validating exploratory optimizations for training and inference efficiency on long texts.

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