What Is DeepSeek? Architecture, Usage, and Comparison with ChatGPT

What Is DeepSeek?

DeepSeek is a family of large language models (LLMs) and an AI platform developed by a Chinese artificial intelligence company headquartered in Hangzhou, China. Founded in July 2023 by Liang Wenfeng, DeepSeek is backed by the quantitative hedge fund High-Flyer. The company has rapidly emerged as a serious competitor to OpenAI’s GPT series and Google’s Gemini, drawing attention from developers, researchers, and enterprise teams worldwide.

The defining characteristic of DeepSeek is its Mixture-of-Experts (MoE) architecture, which delivers performance comparable to or exceeding GPT-4 at a fraction of the cost. The flagship DeepSeek V3 model contains 671 billion total parameters but activates only 37 billion parameters per token during inference, dramatically reducing computational requirements. Equally important is DeepSeek’s commitment to open source: all major models are released under the MIT license, allowing anyone to use, modify, and deploy them freely. This is a key point to keep in mind when evaluating AI platforms for your projects.

DeepSeek-R1, released in January 2025, is a reasoning-specialized model that excels at solving complex mathematical and scientific problems. It achieved scores of 79.8% on AIME 2024 and 97.3% on MATH-500, establishing new benchmarks for reasoning capability. The release sent shockwaves through the technology industry, contributing to a roughly $600 billion decline in NVIDIA’s market capitalization and forcing a reassessment of AI development economics across the sector.

The DeepSeek model family has continued to evolve rapidly. V3.1 introduced a hybrid architecture supporting 128K context length with switchable thinking and non-thinking modes. V3.2-Speciale achieved gold medal levels on both the 2025 International Mathematical Olympiad (IMO) and the International Olympiad in Informatics (IOI), demonstrating performance on par with Google’s Gemini 3.0 Pro. Looking ahead, DeepSeek V4 is expected in spring 2026, with approximately 1 trillion parameters, 1 million token context length, and native multimodal capabilities.

How to Pronounce DeepSeek

How DeepSeek Works

At the core of DeepSeek’s technical innovation is the Mixture-of-Experts (MoE) architecture. Unlike traditional dense Transformer models that use all parameters for every token, MoE selectively activates only the most relevant expert sub-networks for each input. This architectural choice is what enables DeepSeek to achieve remarkable performance at significantly lower computational costs. Understanding this mechanism is important for anyone evaluating DeepSeek for production use.

This architecture allows DeepSeek V3 to deliver the capabilities of a 671 billion parameter model while incurring computational costs closer to a 37 billion parameter model during inference. The training cost efficiency is equally remarkable: DeepSeek V3 was trained for approximately $6 million, compared to an estimated $100 million for OpenAI’s GPT-4, representing roughly a 17x cost reduction.

DeepSeek-R1: Chain-of-Thought Reasoning

DeepSeek-R1 is a reasoning-specialized model that employs enhanced Chain-of-Thought (CoT) techniques. When confronted with complex mathematical problems, scientific questions, or programming challenges, R1 generates explicit step-by-step reasoning chains before arriving at its final answer. This transparent reasoning process allows users to verify the model’s logic and identify potential errors, which is particularly valuable in academic and research contexts. Note that this reasoning capability represents a significant advancement over standard language models that produce answers without showing their work.

The R1 model’s reasoning capabilities have been validated through rigorous benchmarks. On AIME 2024, a prestigious mathematical competition, R1 scored 79.8%. On MATH-500, a comprehensive mathematics benchmark, it achieved 97.3%, surpassing GPT-4o’s performance. These results demonstrate that DeepSeek’s approach to reasoning is not merely competitive but genuinely state-of-the-art in mathematical domains.

V3.1: Hybrid Thinking Modes

Released in 2025, DeepSeek V3.1 introduced a hybrid architecture that supports both thinking (deep reasoning) and non-thinking (fast response) modes within a single model. This flexibility allows developers to optimize the trade-off between response quality and latency depending on the complexity of each task. The model supports a 128K token context window, enabling it to process extensive documents, codebases, and conversation histories in a single session. This dual-mode approach is important because it eliminates the need to maintain separate model deployments for different use cases.

V3.2-Speciale: Competition-Level Performance

DeepSeek V3.2-Speciale represents the latest evolution in the model family, achieving performance comparable to Google’s Gemini 3.0 Pro. Most notably, it earned gold medal-level scores on both the 2025 International Mathematical Olympiad (IMO) and the International Olympiad in Informatics (IOI), competitions that test the highest levels of mathematical and computational reasoning. This achievement underscores DeepSeek’s rapid progress and its ability to compete with the most capable models from well-funded Western AI labs.

How to Use DeepSeek: Practical Examples

DeepSeek can be accessed through its official web interface, API, or by deploying the open-source models on your own infrastructure. The API follows the OpenAI-compatible format, making migration from existing GPT-based applications straightforward. Below are practical code examples demonstrating common usage patterns.

Basic Python API Usage

DeepSeek provides an OpenAI-compatible API, which means you can use the standard OpenAI Python client library with minimal configuration changes. This compatibility is a significant advantage for teams looking to evaluate DeepSeek as an alternative to GPT models without rewriting their application code.

from openai import OpenAI

# Initialize the client with DeepSeek's endpoint
client = OpenAI(
    api_key="your-deepseek-api-key",
    base_url="https://api.deepseek.com"
)

# Standard chat completion request
response = client.chat.completions.create(
    model="deepseek-chat",
    messages=[
        {"role": "system", "content": "You are a helpful programming assistant."},
        {"role": "user", "content": "Write a Python function to generate the Fibonacci sequence."}
    ],
    temperature=0.7,
    max_tokens=1024
)

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

Using the DeepSeek-R1 Reasoning Model

The R1 model provides explicit reasoning traces alongside its final answers. This is particularly useful for mathematical problem-solving, logical reasoning, and any task where understanding the model’s thought process is valuable. You should keep in mind that the reasoning model may take longer to respond due to its extended thought process.

# Using the reasoning model for mathematical problem-solving
response = client.chat.completions.create(
    model="deepseek-reasoner",
    messages=[
        {"role": "user", "content": "Solve 3x^2 + 5x - 2 = 0. Show your work step by step."}
    ]
)

# Access the reasoning chain and final answer separately
print("Reasoning:", response.choices[0].message.reasoning_content)
print("Answer:", response.choices[0].message.content)

cURL Example for Quick Testing

For quick API testing or integration with non-Python environments, you can use cURL to interact with the DeepSeek API directly. The request format is identical to OpenAI’s API format.

curl https://api.deepseek.com/chat/completions   -H "Content-Type: application/json"   -H "Authorization: Bearer your-deepseek-api-key"   -d '{
    "model": "deepseek-chat",
    "messages": [
      {"role": "user", "content": "Explain the Mixture-of-Experts architecture."}
    ],
    "temperature": 0.7,
    "max_tokens": 512
  }'

Streaming Responses for Real-Time Applications

For applications that require real-time output display, such as chatbots or interactive tools, DeepSeek supports streaming responses. This is important for user experience in production applications.

# Streaming example for real-time output
stream = client.chat.completions.create(
    model="deepseek-chat",
    messages=[
        {"role": "user", "content": "Write a comprehensive guide to Python decorators."}
    ],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Advantages and Disadvantages of DeepSeek

Advantages

• Exceptional Cost Efficiency: DeepSeek’s API pricing is a fraction of OpenAI’s rates while delivering comparable performance. The V3 model was trained for approximately $6 million, compared to GPT-4’s estimated $100 million training cost. This cost advantage extends to inference as well, making DeepSeek particularly attractive for high-volume enterprise deployments.
• Open Source Under MIT License: All major DeepSeek models are released under the MIT license, providing maximum flexibility for commercial use, modification, and self-hosting. Organizations can deploy models on their own infrastructure, fine-tune them for specific domains, and integrate them into proprietary products without licensing restrictions. This is an important consideration for enterprises with strict data governance requirements.
• State-of-the-Art Reasoning: The R1 model demonstrates world-class mathematical reasoning with 79.8% on AIME 2024 and 97.3% on MATH-500. V3.2-Speciale has achieved gold medal levels on the 2025 IMO and IOI, confirming DeepSeek’s position at the frontier of AI reasoning capabilities.
• OpenAI-Compatible API: The API follows the OpenAI format, enabling teams to migrate existing GPT-based applications with minimal code changes. This compatibility significantly reduces the barrier to evaluation and adoption.
• Efficient MoE Architecture: By activating only 37B of 671B total parameters per token, DeepSeek delivers large-model capabilities with smaller-model computational costs, resulting in faster inference and lower operational expenses.

Disadvantages

• Content Restrictions Due to Chinese Regulations: As a Chinese company, DeepSeek is subject to Chinese government content regulations. Responses on certain politically sensitive topics may be restricted, filtered, or declined. Teams working on applications that require uncensored output on all topics should be aware of this limitation.
• Data Privacy Considerations: When using the cloud API, data is processed through servers operated by a Chinese entity, which may conflict with data sovereignty requirements in certain jurisdictions and regulated industries. Organizations handling sensitive data should carefully evaluate whether self-hosted deployment is more appropriate for their compliance needs.
• Japanese and Other Language Support Still Maturing: While DeepSeek performs excellently in English and Chinese, support for other languages including Japanese is still improving. Some tasks may show reduced accuracy in languages other than the primary training languages, so you should test thoroughly before deploying in multilingual contexts.
• Smaller Plugin and Tool Ecosystem: Compared to OpenAI’s mature ecosystem of plugins, integrations, and third-party tools, DeepSeek’s ecosystem is still in its early stages. Teams requiring extensive pre-built integrations may find the current ecosystem lacking, though this is rapidly evolving.
• Self-Hosting Resource Requirements: While the models are open source, deploying the full 671B parameter V3 model requires substantial GPU infrastructure. Smaller distilled versions are available but may not match the full model’s performance.

DeepSeek vs. ChatGPT: Key Differences

DeepSeek and ChatGPT (powered by OpenAI’s GPT models) are both leading AI platforms built on large language models, but they differ significantly in their architecture, business model, and approach to AI development. The following comparison table highlights the most important differences that developers and decision-makers should consider when choosing between the two platforms.

As the comparison shows, DeepSeek holds clear advantages in cost efficiency and openness, while ChatGPT offers a more mature ecosystem with broader multimodal capabilities and extensive third-party integrations. The best choice depends on your specific requirements: DeepSeek excels for cost-sensitive projects, self-hosted deployments, and advanced reasoning tasks, while ChatGPT remains the more versatile option for general-purpose applications requiring a rich plugin ecosystem. Many organizations are finding value in using both platforms for different use cases.

Common Misconceptions About DeepSeek

Misconception 1: DeepSeek Is a Copy of ChatGPT

This is one of the most persistent misconceptions. DeepSeek uses a fundamentally different architecture (MoE) from OpenAI’s GPT series (dense Transformer). While DeepSeek provides an OpenAI-compatible API, this is an interface compatibility decision to ease migration, not an indication of copied technology. The underlying model architecture, training methodology, and optimization techniques are independently developed. It is important to understand that API compatibility and technological copying are entirely different things.

Misconception 2: Open Source Means Lower Quality

The assumption that open-source models are inherently inferior to proprietary ones is unfounded. DeepSeek-R1 achieves 97.3% on MATH-500, outperforming GPT-4o in mathematical reasoning. Some of the world’s most critical software infrastructure, including Linux, Python, and TensorFlow, is open source. The quality of an AI model depends on its training data, architecture, and optimization, not on its licensing model. You should evaluate models based on benchmark performance and fitness for your specific use case, not on whether they are open or closed source.

Misconception 3: Chinese Origin Makes It Inherently Unsafe

Security concerns should be evaluated based on deployment architecture, not country of origin. Since DeepSeek is released under the MIT license, the source code and model weights are publicly available for inspection. Organizations can deploy the models entirely on their own infrastructure, ensuring that no data leaves their environment. When self-hosted, DeepSeek operates identically to any other locally deployed model. The key security consideration is how you deploy and configure the model, not where it was developed. This is an important nuance that is often overlooked in discussions about AI security.

Misconception 4: DeepSeek Is Completely Free to Use

While DeepSeek’s web chat interface at chat.deepseek.com is free for general use, the API operates on a pay-per-use model. The pricing is significantly lower than OpenAI’s rates, but it is not zero. Self-hosting the open-source models eliminates API costs but introduces infrastructure expenses for GPU hardware, electricity, and maintenance. Organizations should calculate the total cost of ownership based on their expected usage volume and deployment model before making procurement decisions.

Real-World Use Cases

Automated Code Review in Software Development

DeepSeek V3’s strong code comprehension capabilities make it well-suited for automated code review workflows. Development teams can integrate DeepSeek into their CI/CD pipelines to automatically review pull requests, identify potential bugs, suggest optimizations, and enforce coding standards. The MIT license allows unrestricted integration into internal tooling without licensing concerns. Several engineering teams have reported significant improvements in code review throughput after incorporating DeepSeek into their development workflows. This is a practical application that directly impacts development team productivity.

Academic Research and Mathematical Reasoning

DeepSeek-R1’s exceptional mathematical reasoning capabilities make it a valuable tool for researchers working with complex mathematical problems. The model can assist with proof verification, hypothesis generation, equation solving, and mathematical modeling. V3.2-Speciale’s gold medal performance on the 2025 IMO and IOI demonstrates that the model can handle competition-level mathematical and algorithmic challenges, making it suitable for graduate-level research support and educational applications.

Enterprise Document Analysis and Summarization

V3.1’s 128K token context window enables comprehensive analysis of lengthy documents such as legal contracts, technical specifications, research papers, and financial reports. The hybrid thinking and non-thinking modes allow organizations to optimize for speed on routine summarization tasks while engaging deep reasoning for complex analytical work. This flexibility is particularly valuable in legal, financial, and consulting firms that process large volumes of technical documentation. Note that the ability to switch between modes without changing model deployments significantly simplifies infrastructure management.

Startup Product Development

DeepSeek’s combination of low cost, open-source availability, and high performance makes it particularly attractive for startups building AI-powered products. Early-stage companies can prototype with the API at minimal cost, then transition to self-hosted deployment as they scale, avoiding vendor lock-in. The OpenAI-compatible API means that prototypes built with GPT models can be quickly tested with DeepSeek as an alternative backend, enabling informed technology decisions before committing to a long-term AI infrastructure strategy.

Multilingual Content Generation and Translation

While DeepSeek’s strongest performance is in English and Chinese, its multilingual capabilities make it useful for content generation and translation workflows, particularly for content bridging between English, Chinese, and other major languages. Organizations operating across Asian and Western markets can leverage DeepSeek for draft translations, content localization, and cross-lingual document summarization, though human review remains important for quality-critical content in less-supported languages.

Frequently Asked Questions (FAQ)

Q: Is DeepSeek free to use?

A: DeepSeek’s web chat interface at chat.deepseek.com is free for general use. The API operates on a pay-per-use model with pricing significantly lower than OpenAI’s rates. Additionally, since DeepSeek’s models are open source under the MIT license, you can download and deploy them on your own servers at no software cost, though you will need to provide the necessary GPU infrastructure. For most developers and small teams, the API’s low pricing makes it an accessible starting point.

Q: Should I use DeepSeek or ChatGPT?

A: The best choice depends on your specific requirements. DeepSeek is ideal if you prioritize cost efficiency, need open-source flexibility for self-hosting or customization, or require strong mathematical reasoning capabilities. ChatGPT is better suited if you need a mature plugin ecosystem, multimodal capabilities (image, audio, video), or comprehensive third-party integrations. Many organizations use both platforms for different purposes, which is an increasingly common and practical strategy. You should evaluate both against your specific use cases before making a decision.

Q: When will DeepSeek V4 be released?

A: DeepSeek V4 is expected to launch in spring 2026. Based on available information, V4 will feature approximately 1 trillion parameters, a 1 million token context window, and native multimodal support for processing text, images, and other data types within a single model. These enhancements represent a substantial leap forward and should significantly expand DeepSeek’s applicability across use cases.

Q: Can DeepSeek handle Japanese and other non-English languages?

A: DeepSeek supports multiple languages including Japanese, Korean, French, German, and others. However, performance is strongest in English and Chinese, which are the primary training languages. For Japanese and other secondary languages, accuracy may vary depending on the task complexity. It is advisable to conduct thorough testing with your specific use cases before deploying in production for non-English language workloads. Language support continues to improve with each model update.

Q: How does DeepSeek handle data privacy?

A: Data privacy with DeepSeek depends on your deployment model. When using the cloud API, data is processed on DeepSeek’s servers. For organizations with strict data governance requirements, the recommended approach is to deploy the open-source models on your own infrastructure, ensuring complete data isolation. The MIT license grants full rights to self-host without restrictions. This self-hosted approach eliminates all data sovereignty concerns and provides the same level of data control as any locally deployed software.

Summary

DeepSeek is a rapidly evolving AI platform that has fundamentally challenged assumptions about the cost and accessibility of cutting-edge language models. By leveraging its innovative Mixture-of-Experts architecture, DeepSeek delivers performance competitive with the world’s leading AI models at a fraction of the training and inference cost. Here are the key points to remember about DeepSeek.

• The MoE architecture activates only 37B of 671B total parameters per token, achieving large-model performance at small-model costs
• V3 was trained for approximately $6 million, roughly 17 times cheaper than GPT-4’s estimated $100 million training cost
• R1 achieves 97.3% on MATH-500 and 79.8% on AIME 2024, establishing state-of-the-art reasoning performance
• All models are open source under the MIT license, enabling commercial use, customization, and self-hosted deployment
• The OpenAI-compatible API makes migration from existing GPT-based applications straightforward
• V4, expected in spring 2026, will bring approximately 1T parameters, 1M token context, and native multimodal capabilities

The AI landscape is evolving at an unprecedented pace, and DeepSeek has positioned itself as a leading force in democratizing access to state-of-the-art AI technology. Whether you are a developer evaluating API options, an enterprise planning AI infrastructure, or a researcher seeking powerful reasoning tools, DeepSeek deserves serious consideration. Keep in mind that the best approach is often to evaluate multiple platforms against your specific requirements and use cases, and to stay informed about the rapid developments in this space.

References and Sources

• DeepSeek Official Website
• DeepSeek GitHub Repository (Official)
• DeepSeek-R1 Technical Paper (arXiv)
• DeepSeek API Documentation
• DeepSeek Models on Hugging Face