DeepSeek

DeepSeek V3.2 Thinking

deepseek/deepseek-v3.2-think
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

DeepSeek-V3.2 是一款大型语言模型,旨在兼顾高计算效率、强大的推理能力以及智能体工具使用性能。它引入了 DeepSeek 稀疏注意力机制 (DSA),这是一种细粒度的稀疏注意力机制,能够在长上下文场景下降低训练和推理成本,同时保持模型质量。可扩展的强化学习后训练框架进一步提升了推理能力,其性能已达到 GPT-5 的水平,并在 2025 年国际数学奥林匹克竞赛 (IMO) 和国际信息学奥林匹克竞赛 (IOI) 中荣获金奖。V3.2 还采用了大规模智能体任务合成流程,将推理更好地融入工具使用场景,从而提升模型在交互式环境中的适应性和泛化能力。

Input / output modalities
文本 to 文本
Reference input / output price
Input¥2Output¥3per 1M tokens
Context window
128K
Added to catalog
Dec 2, 2025

Providers and pricing

ProviderInput /MOutput /MCached /MContextMax outputDetails
DeepSeek¥2¥3¥0.2128K64K
Ali¥2¥3¥0.2128K64K

DeepSeek

Latency
1.22s
Throughput
73 tokens/s
Context
128K

Pricing

Input
¥2/M tokens
Output
¥3/M tokens
Cached
¥0.2/M tokens

Specifications

Context
128K
Max output
64K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

Ali

Latency
1.11s
Throughput
40 tokens/s
Context
128K

Pricing

Input
¥2/M tokens
Output
¥3/M tokens
Cached
¥0.2/M tokens

Specifications

Context
128K
Max output
64K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

DeepSeek V3.2 Thinking code examples and API guide

Modelmesh normalizes requests and responses across service providers behind one consistent API.

Modelmesh provides an OpenAI-compatible Completion API for more than 300 models and service providers. Call it directly, through the OpenAI SDK, or with supported third-party SDKs.

Modelmesh-specific request headers in these examples are optional. When supplied, your application can appear on the Modelmesh rankings.

Supported endpointsSelect an endpoint to switch the example below.
/v1/chat/completions
from openai import OpenAI API_KEY = "$SSY_API_KEY" client = OpenAI( base_url="https://router.shengsuanyun.com/api/v1", api_key=API_KEY, ) try: completion = client.chat.completions.create( model="deepseek/deepseek-v3.2-think", messages=[{"role": "user", "content": "Which number is larger, 9.11 or 9.8?"}], temperature=0.6, top_p=0.7, stream=True, ) response_text = "" for chunk in completion: if chunk.choices and chunk.choices[0].delta.content is not None: content = chunk.choices[0].delta.content print(content, end="", flush=True) response_text += content except Exception as error: print(f"Request failed: {error}")
                
              

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