Today, we’re exploring DeepSeek, a Chinese AI startup that’s making waves in the generative AI (GenAI) field. Forget the usual big names DeepSeek is a potential game-changer, not just another model in the mix.
Founded in 2023 and based in Hangzhou, China, DeepSeek is quickly establishing itself as a major player in generative AI. The company has caught attention for its innovative approach to creating large language models (LLMs) that are not only affordable but also open-source. In this article, we’ll delve into what makes DeepSeek unique, how it compares to other AI models, its architectural advancements, and the possible impact on GenAI applications.
What Is DeepSeek?
DeepSeek was created by Liang Wenfeng and focuses on developing open-source AI models to challenge the dominance of companies like OpenAI. Their flagship model, DeepSeek-R1, launched in January 2025, is praised for its advanced reasoning abilities while being significantly cheaper than models from competitors.
More Than Just Competition
DeepSeek isn’t just trying to keep up—it’s redefining the game. The company’s suite of LLMs and multimodal models are designed for efficiency and open accessibility. Its mission is clear: democratize AI by making powerful models affordable and available to those without extensive computational resources.
Architectural Innovations
DeepSeek’s models include several revolutionary features:
- Mixture-of-Experts (MoE): Activates only certain parts of the model based on the task, reducing computational costs.
- Multi-Head Latent Attention (MLA): Optimizes attention mechanisms to boost performance in environments with limited resources.
- Cost Efficiency: DeepSeek models are optimized to run on less powerful hardware, which may challenge Nvidia’s current dominance in AI hardware.
These innovations make DeepSeek’s models more efficient and accessible, opening doors for smaller organizations to compete with tech giants.
Why DeepSeek Stands Out
Several key factors make DeepSeek noteworthy:
- Affordability: DeepSeek-R1, for example, is said to be 20 to 50 times more cost-effective than OpenAI’s models for certain tasks. This dramatic cost-saving makes cutting-edge AI accessible to a broader range of users.
- Open Source Commitment: By releasing its models and code under open-source licenses, DeepSeek encourages community-driven innovation.
- Multimodal Capabilities: The Janus-Pro model, available in 1B and 7B parameter sizes, is a significant step forward. It can handle both text and image generation, surpassing models like DALL-E 3 and Stable Diffusion in certain benchmarks.
- Impressive Performance: DeepSeek’s models not only offer affordability but also excel in tasks such as coding and mathematical reasoning.
Advantages and Drawbacks of DeepSeek Models
Pros:
- Affordability: DeepSeek’s models are cost-effective, making advanced AI more accessible.
- Efficiency: Models are optimized to run on less powerful hardware.
- Multimodal Capabilities: Janus-Pro offers both text and image generation.
- Open Source: Models are available under open-source licenses, allowing for community contributions.
- Strong Performance: Early tests show that DeepSeek’s models perform well in coding and reasoning tasks.
Cons:
- Newer Models: Since DeepSeek’s models are still relatively new, they may not have undergone as much real-world testing as older models.
- Smaller Ecosystem: Although open-source, DeepSeek’s community is still growing.
- Performance Gaps: While strong in some areas, performance in others still needs refinement.
- Biases and Privacy Concerns: As with many AI models, DeepSeek’s models may face issues related to bias and data privacy.
- Limited Integration: Some features may not yet be as integrated as those found in platforms like ChatGPT.
- Market Reactions: The rise of DeepSeek has impacted stock prices of established companies, with investors reassessing their positions.
DeepSeek Compared to other AI models
1. Primary Focus and Application
- DeepSeek AI specializes in code generation, technical tasks, and excels in Chinese NLP. It is particularly well-suited for AI research and domains where cost efficiency and specialized language processing are critical.
- ChatGPT offers a balanced mix of general-purpose conversation, creative content, and robust code assistance. Its broad application makes it ideal for general-purpose use, creative writing, and complex dialogues.
- Gemini is engineered for multimodal tasks and productivity, with tight integration into Google services like Docs, Gmail, and Google Cloud. This makes it especially attractive for users deeply embedded in the Google ecosystem
- Perplexity AI focuses on real-time fact retrieval and academic research. It excels in providing sourced, verified answers and is best for research and data-driven inquiries.
2. Training and Data
DeepSeek AI has been trained on multilingual datasets with a strong emphasis on Chinese NLP and technical content at a much lower cost—sometimes training for as little as $6 million compared to hundreds of millions for its competitors.
ChatGPT and Gemini, meanwhile, benefit from vast web-scale training datasets and multimodal data, ensuring robust general language and context understanding. Perplexity AI leverages up-to-date web data for real-time responses, ensuring accuracy but relying heavily on external sources.
3. Multimodal and Context Capabilities
Gemini’s extended context window (up to 1 million tokens in some iterations) and multimodal input capability distinguish it from ChatGPT, which, despite improvements, typically supports a smaller context window and focuses primarily on text. DeepSeek AI, while highly capable in technical and reasoning tasks, shows limited multimodality, focusing more on code and Chinese language tasks.
Perplexity AI is largely text-based, designed to integrate real-time search data rather than handle images or audio.
4. Strengths and Weaknesses
- DeepSeek AI
- Strengths: Highly cost-effective, specialized in coding and Chinese NLP, open-source, and resource-efficient.
- Weaknesses: Limited multimodal support and a smaller ecosystem beyond its specialized areas.
- ChatGPT
- Strengths: Versatile, strong conversational abilities, robust code generation, and creative content capabilities.
- Weaknesses: Occasional hallucinations, reliance on heavy computational resources, and performance behind paywalled advanced features.
- Gemini
- Strengths: Excellent multimodal integration, expansive context capabilities, and seamless Google service integration.
- Weaknesses: Accuracy issues can occur, and some advanced features may be locked behind subscriptions.
- Perplexity AI
- Strengths: Real-time web search integration, transparent citation of sources, and precise fact-based answers.
- Weaknesses: Less adept at creative or open-ended tasks and limited to text-based interactions.
5. Best Use Cases
- DeepSeek AI: Ideal for AI research, technical coding tasks, and applications that require strong Chinese NLP capabilities.
- ChatGPT: Best for general-purpose conversation, creative writing, brainstorming, and code assistance.
- Gemini: Suited for users needing multimodal functionality and tight integration with Google’s suite, making it excellent for productivity and complex data analysis.
- Perplexity AI: Perfect for academic research, real-time fact-checking, and information retrieval with verifiable sources.
DesigningIT User Case: DeepSeek vs ChatGPT
To compare these two powerful AI tools, our team conducted an experiment by selecting a section from our website and submitting the same text to both AI models. We asked each AI to evaluate whether the webpage needed any improvements and, if so, to provide specific suggestions.
The question to both AI models was the following: https://www.designingit.com/our-approach: Give me a suggested change to this web page.
Below is a detailed comparison of their responses. We will be utilizing comparable basic versions of ChatGPT (ChatGPT-4-turbo) and DeepSeek (DeepSeek-V3).
ChatGPT
DeepSeek
Comparison of Two Suggestions
1. Structural Differences
- Suggestion 1 is highly structured, following a clear format:
- Current Issue → Suggested Change → Why? → How? → Example → Additional Enhancements → Expected Outcome
- This breakdown makes it easier for the reader to follow the reasoning behind the recommendation and visualize the implementation.
- Suggestion 2 follows a list-based approach, where different recommendations are presented as bullet points.
- It’s less structured than Suggestion 1, as it does not explicitly follow a problem-solution-impact format.
- However, it is still well-organized and easy to skim.
2. Number of Suggestions
- Suggestion 1 focuses on one major change (adding an interactive timeline/flowchart) but goes into deep detail on execution.
- Suggestion 2 provides multiple smaller recommendations (jargon simplification, visual hierarchy, branding consistency, testimonials, etc.).
3. Quality Differences
- Suggestion 1 is more persuasive and well-structured, explaining the reasoning behind the change with clear examples and expected outcomes.
- Suggestion 2 is concise and practical, providing multiple actionable improvements but without diving into each in depth.
Which One is Better?
- For clarity and ease of implementation → Suggestion 2 (multiple, easy-to-follow ideas).
- For persuasive impact and depth → Suggestion 1 (more detailed, structured, and strategic).
- For overall effectiveness → A combination of both approaches would be ideal. Suggestion 2 could be enhanced with more depth and expected outcomes like Suggestion 1.
DeepSeek is rapidly emerging as a strong competitor in generative AI, offering cost-effective, open-source models that challenge industry giants. While still growing, its innovations in efficiency and accessibility make it a game-changer, shaping the future of AI development.
Which AI do you use?