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const { OpenAI } = require('openai');
const api = new OpenAI({
baseURL: 'https://api.ai.cc/v1',
apiKey: '',
});
const main = async () => {
const result = await api.chat.completions.create({
model: 'deepseek/deepseek-non-thinking-v3.2-exp',
messages: [
{
role: 'system',
content: 'You are an AI assistant who knows everything.',
},
{
role: 'user',
content: 'Tell me, why is the sky blue?'
}
],
});
const message = result.choices[0].message.content;
console.log(`Assistant: ${message}`);
};
main();
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.ai.cc/v1",
api_key="",
)
response = client.chat.completions.create(
model="deepseek/deepseek-non-thinking-v3.2-exp",
messages=[
{
"role": "system",
"content": "You are an AI assistant who knows everything.",
},
{
"role": "user",
"content": "Tell me, why is the sky blue?"
},
],
)
message = response.choices[0].message.content
print(f"Assistant: {message}")
-
AI Playground
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Product Detail
Model Overview
DeepSeek-V3.2-Exp Non-Thinking, launched in September 2025, is an experimental transformer-based large language model. Designed as an evolution of DeepSeek V3.1-Terminus, it introduces the innovative DeepSeek Sparse Attention (DSA) mechanism. This enables efficient and scalable long-context understanding, delivering faster and more cost-effective inference by selectively attending to essential tokens.
Technical Specifications
- ⚙️ Model Generation: Experimental intermediary development from DeepSeek V3.1
- 🧠 Architecture Type: Transformer with fine-grained sparse attention (DeepSeek Sparse Attention - DSA)
- 📏 Parameter Alignment: Training aligned to V3.1-Terminus for benchmarking validity
- 📖 Context Length: Supports up to 128,000 tokens, suitable for multi-document and long-form text processing
- 📤 Max Output Tokens: 4,000 default, supports up to 8,000 tokens per response
Performance Benchmarks
Performance remains on par with or better than V3.1-Terminus across multiple domains such as reasoning, coding, and real-world agentic tasks, while delivering substantial efficiency gains.
- ✅ GPQA-Diamond (Question Answering): Scores 79.9, slightly below V3.1 (80.7)
- 💻 LiveCodeBench (Coding): Reaches 74.1, close to 74.9 of V3.1
- ➕ AIME 2025 (Mathematics): Scores 89.3, surpassing V3.1 (88.4)
- 🏆 Codeforces programming benchmark: Performs at 2121, better than V3.1 (2046)
- 🛠️ BrowseComp (Agentic Tool Use): Achieves 40.1, better than V3.1 (38.5)
Key Features
- ✨ DeepSeek Sparse Attention (DSA): Innovative fine-grained sparse attention mechanism focusing computation only on the most important tokens, dramatically reducing compute and memory requirements.
- 📚 Massive Context Support: Processes up to 128,000 tokens (over 300 pages of text), enabling long-form document understanding and multi-document workflows.
- 💰 Significant Cost Reduction: Inference cost reduced by more than 50% compared to DeepSeek V3.1-Terminus, making it highly efficient for large-scale usage.
- ⚡ High Efficiency and Speed: Optimized for fast inference, offering 2-3x acceleration on long-text processing compared to prior versions without sacrificing output quality.
- 🏆 Maintains Quality: Matches or exceeds DeepSeek V3.1-Terminus performance across multiple benchmarks with comparable generation quality.
- ⚖️ Scalable and Stable: Optimized for large-scale deployment with improved memory consumption and inference stability on extended context lengths.
- 🚀 Non-Thinking Mode: Prioritizes direct, fast answers without generating intermediate reasoning steps, perfect for latency-sensitive applications.
API Pricing
- Input Tokens (CACHE HIT): $0.0294 per 1M tokens
- Input Tokens (CACHE MISS): $0.294 per 1M tokens
- Output Tokens: $0.441 per 1M tokens
Use Cases
- 💬 Fast Interactive Chatbots & Assistants: Ideal for applications where responsiveness is critical.
- 📝 Long-form Document Summarization & Extraction: Efficiently handles large texts without explanation overhead.
- 💻 Code Generation/Completion: Rapidly processes large repositories where speed is key.
- 🔍 Multi-document Search & Retrieval: Provides low-latency results across multiple sources.
- 🔗 Pipeline Integrations: Delivers direct JSON outputs without intermediate reasoning noise, perfect for automated workflows.
Code Sample
<snippet data-name="open-ai.chat-completion" data-model="deepseek/deepseek-non-thinking-v3.2-exp"></snippet>
Comparison with Other Models
VS. DeepSeek V3.1-Terminus: V3.2-Exp introduces the DeepSeek Sparse Attention mechanism, significantly reducing compute costs for long contexts while maintaining nearly identical output quality. It achieves similar benchmark performance but is about 50% cheaper and notably faster on large inputs compared to DeepSeek V3.1-Terminus.
VS. GPT-5: While GPT-5 leads in raw language understanding and generation quality across a broad range of tasks, DeepSeek V3.2-Exp notably excels in handling extremely long contexts (up to 128K tokens) more cost-effectively. DeepSeek’s sparse attention provides a strong efficiency advantage for document-heavy and multi-turn applications.
VS. LLaMA 3: LLaMA models offer competitive performance with dense attention but typically cap context size at 32K tokens or less. DeepSeek's architecture targets long-context scalability with sparse attention, enabling smoother performance on very large documents and datasets where LLaMA may degrade or become inefficient.
Frequently Asked Questions
❓ What is DeepSeek V3.2-Exp Non-Thinking and how does it differ from standard models?
DeepSeek V3.2-Exp Non-Thinking is a specialized variant optimized for fast, direct responses without extensive reasoning chains. Unlike standard models that engage in multi-step reasoning, this version prioritizes speed and efficiency by providing immediate answers without the 'thinking' process, making it ideal for applications requiring rapid responses where elaborate reasoning isn't necessary.
❓ What are the primary use cases for a non-thinking AI model?
Primary use cases include: high-volume customer service responses, simple Q&A systems, content classification tasks, basic information retrieval, straightforward translation requests, and any scenario where speed and throughput are more critical than deep analytical reasoning. It's particularly valuable for applications with strict latency requirements or when serving many concurrent users with simple queries.
❓ What performance advantages does the non-thinking version offer?
The non-thinking variant provides significant advantages in: reduced inference latency (often 2-3x faster), lower computational costs, higher throughput for concurrent requests, improved scalability, and more predictable response times. These benefits come from skipping the computational overhead of generating and processing extended reasoning steps before delivering answers.
❓ What types of queries are not suitable for non-thinking models?
Queries requiring complex problem-solving, multi-step reasoning, mathematical proofs, logical deductions, creative brainstorming, or nuanced ethical considerations are not ideal for non-thinking models. These scenarios benefit from standard models that can engage in chain-of-thought reasoning to arrive at more accurate and well-considered responses through systematic analysis.
❓ How can developers choose between thinking and non-thinking model variants?
Developers should choose based on: response time requirements (non-thinking for sub-second needs), query complexity (thinking for analytical tasks), cost constraints (non-thinking for budget-sensitive applications), user experience goals, and whether the application benefits from transparent reasoning processes. Many applications use a hybrid approach, routing simple queries to non-thinking models while reserving thinking models for complex tasks.
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