What is DeepSeek? Is It Really Better than ChatGPT?

• Blockchain Council
• January 30, 2025

In recent years, artificial intelligence (AI) has been advancing at a breakneck pace, with companies striving to build more powerful and efficient models. Among the newer players in the space, DeepSeek has emerged as a noteworthy competitor to established platforms like OpenAI’s ChatGPT. Known for its high performance, efficiency, and affordability, DeepSeek has attracted global attention for its technical capabilities.

What is DeepSeek?

DeepSeek began as an experimental offshoot of High-Flyer, a hedge fund specializing in quantitative trading. Following regulatory crackdowns in China’s financial markets in 2021, High-Flyer shifted its focus to AI. The transition was seamless due to the company’s prior expertise in machine learning. By early 2025, DeepSeek had developed a suite of advanced AI models, including DeepSeek-R1 (also referred to as deepseek-reasoner) for reasoning tasks and DeepSeek-V3 for general conversational AI.

What sets DeepSeek apart is its efficiency during training. While leading models like OpenAI’s GPT-4 reportedly cost upwards of $100 million to train, DeepSeek achieved comparable results at just $6 million. This efficiency is attributed to optimized algorithms, innovative use of lower-grade hardware, and shorter training times (around 55 days).

How DeepSeek Achieves Low-Cost AI Training

DeepSeek’s training process is a masterclass in efficiency. Unlike OpenAI’s GPT-4, which reportedly required $100 million and months of training using thousands of A100 GPUs, DeepSeek trained its models with just 2,000 Nvidia H800 GPUs over 55 days at a cost of $6 million. This cost reduction was achieved through:

• Efficient Hardware Utilization: DeepSeek relied on Nvidia H800 GPUs, which are designed for high-throughput machine learning tasks.
• Algorithmic Optimization: By refining its training algorithms, DeepSeek minimized computational overhead, reducing the cost without compromising model performance.
• Scalability of Resources: Instead of deploying supercomputers, DeepSeek optimized resource distribution, training its models on less powerful but sufficient hardware.

This approach not only made DeepSeek’s models more affordable but also highlighted the company’s commitment to achieving high performance without excessive resource consumption.

To understand such models better, you need to understand what AI is all about. The Master Artificial Intelligence Learning Path can help you learn everything about AI and its branches.

What are DeepSeek’s Core Features and Models?

DeepSeek has developed the following flagship models:

DeepSeek-R1 (Reasoning Model)

Purpose and Features

DeepSeek-R1, also known as deepseek-reasoner, is designed for tasks that require logical reasoning and problem-solving. It incorporates Chain of Thought (CoT) reasoning, a mechanism that enables the model to generate intermediate steps before delivering the final answer. This structured reasoning process improves accuracy, especially for complex tasks like mathematical problem-solving and logical analysis.

Applications

• Mathematics: Solving equations, proving theorems, and tackling advanced mathematical competitions like AIME.
• Logic Puzzles: Breaking down and solving multi-step problems.
• Data Analysis: Parsing and interpreting structured datasets.

Key Achievements

• Achieved 91.6% accuracy on the MATH-500 benchmark.
• Outperformed competitors in reasoning-heavy benchmarks like GPQA Diamond, scoring 58.5% in pass@1 compared to GPT-4’s 53.6%.

DeepSeek-V3 (Conversational Model)

Purpose and Features

DeepSeek-V3 powers the company’s AI assistant app, designed for general conversational AI tasks. It supports context lengths of up to 64,000 tokens, making it particularly adept at handling lengthy dialogues and complex document-based queries. This model balances conversational fluency with technical precision.

Applications

• Customer Support: Engaging in multi-turn conversations while retaining context.
• Text Analysis: Summarizing long texts, legal documents, and reports.
• Education: Answering queries and providing explanations in a clear and concise manner.

Key Features

• Compatible with OpenAI’s API, allowing seamless integration into existing systems.
• Enhanced conversational capabilities optimized for both casual and professional use cases.

DeepSeek-V2.5

Overview

DeepSeek-V2.5 is the latest version in the company’s open-source series, combining the strengths of DeepSeek-V2-0628 and DeepSeek-Coder-V2-0724. This hybrid model merges conversational abilities with robust coding capabilities, offering a unified solution for general AI tasks and technical applications.

Improvements Over Previous Versions

• Enhanced Writing: Improved performance in instruction-following and content generation.
• Stronger Coding: Retains the advanced code generation and debugging capabilities of DeepSeek-Coder-V2 while aligning better with human preferences.
• Safety Enhancements: Strengthened safeguards against jailbreak attacks and more balanced safety policies.

Applications

• Content Creation: Writing essays, reports, and creative pieces.
• Software Development: Generating code, debugging, and completing programming tasks.
• Research Assistance: Summarizing scientific papers and generating hypotheses.

Benchmark Performance

Task	DeepSeek-V2.5 Score	Improvement Over V2-0628
Writing	89.6%	+24.9%
Roleplay	43.2%	+15.4%
Math (Math-500)	82.8%	+8.0%
Coding (LiveCodeBench)	34.38%	+5.14%

DeepSeek-R1-Lite-Preview

Purpose

A streamlined version of the DeepSeek-R1 model, optimized for shorter inference times and higher throughput. It is particularly well-suited for tasks requiring rapid reasoning and decision-making.

Features

• Inference Scaling Laws: Demonstrates improved performance as reasoning length increases, making it adaptable to a variety of problem complexities.
• Low Computational Requirements: Designed to function efficiently on mid-tier hardware.

Performance Highlights

• AIME 2024: Achieved 52.5% pass@1, outperforming GPT-4 and other competing models.
• CodeForces: Scored 1450 in coding benchmarks, showcasing strong capabilities in competitive programming.

DeepSeek-Coder-V2

Purpose

Built for advanced programming tasks, DeepSeek-Coder-V2 specializes in code generation, debugging, and multilingual coding challenges. It supports features like Fill-In-the-Middle (FIM) Completion, allowing users to specify prefixes and suffixes for code, with the model completing the intermediate logic.

Applications

• Software Engineering: Writing and optimizing code across multiple programming languages.
• Debugging: Identifying and resolving code errors efficiently.
• Data Science: Writing and executing scripts for data analysis and visualization.

Performance

• HumanEval Python: Achieved notable improvements over earlier models.
• DS-Arena-Code Evaluation: Demonstrated a significant win rate against competitors in subjective coding tasks.

How Does DeepSeek Ensure Model Optimization?

DeepSeek’s iterative development process focuses on refining its models to enhance performance across diverse applications. For example:

• Hybrid Model Integration: DeepSeek-V2.5 combines conversational and coding capabilities, bridging gaps between general-purpose AI and technical specialization.
• Safety Enhancements: With each version, the models gain stronger resistance to adversarial attacks while maintaining user-friendly interaction.
• Open-Source Collaboration: DeepSeek models are open-source, fostering community-driven improvements and wider adoption.

What Sets DeepSeek Apart from Competitors?

1. Efficiency and Cost-Effectiveness

DeepSeek’s models are designed to deliver high performance at a fraction of the cost. Unlike OpenAI, which required upwards of $100 million and thousands of GPUs to train GPT-4, DeepSeek achieved comparable results using 2,000 Nvidia H800 GPUs at a cost of just $6 million over 55 days. This efficiency stems from:

• Optimized Algorithms: Reducing computational overhead during training.
• Hardware Utilization: Employing cost-effective yet powerful GPUs.
• Context Caching: Implementing disk-based caching technology to reuse input tokens, drastically lowering API costs.

2. Long Context Lengths

DeepSeek supports a maximum context length of 64,000 tokens, surpassing GPT-4’s 32,000-token limit. This capability is particularly beneficial for applications requiring long-document processing, such as legal analysis, financial reporting, and research summarization.

3. Chain of Thought (CoT) Reasoning

DeepSeek-R1 incorporates CoT reasoning, enabling the model to break down complex problems step-by-step before delivering the final answer. For example, in solving mathematical equations or debugging code, this feature enhances accuracy and interpretability.

How Does DeepSeek Compare to ChatGPT in Performance?

DeepSeek’s performance has been benchmarked against ChatGPT across several domains. Below is a breakdown of key metrics:

Benchmark	DeepSeek-R1	GPT-4
AIME 2024 (Math)	79.8% (Pass@1)	63.6% (Pass@1)
CodeForces (Coding)	1820 rating	1316 rating
MATH-500 (Reasoning)	94.3% (Pass@1)	90.0% (Pass@1)
SWE-bench Verified	49.2% (Resolved)	41.6% (Resolved)

Key Observations:

• Mathematical and Logical Tasks: DeepSeek consistently outperforms GPT-4 in solving mathematical problems and logic-based tasks, thanks to its CoT reasoning.
• Programming and Coding: DeepSeek’s superior debugging and optimization capabilities give it an edge in coding tasks, especially in competitive programming environments like CodeForces.
• Creative Tasks: ChatGPT outshines DeepSeek in creative writing and general conversational contexts, where nuanced understanding and emotional intelligence are required.

How Affordable is DeepSeek Compared to ChatGPT?

DeepSeek is designed to be significantly more affordable than ChatGPT, making it an attractive option for startups, researchers, and developers with budget constraints. Below is a comparison of pricing for one million tokens:

Model	Input Price	Output Price
DeepSeek-V3	$0.07 (Cache Hit)	$0.14 (Cache Hit)
DeepSeek-R1	$0.14	$2.19
GPT-4	Higher Rates (Varies)	Higher Rates (Varies)

DeepSeek’s context caching further reduces costs for repetitive queries by storing frequently used input, allowing users to benefit from reduced computational requirements.

What are the Limitations of DeepSeek?

Despite its strengths, DeepSeek is not without limitations:

1. Censorship and Content Moderation

As a Chinese company, DeepSeek adheres to strict government regulations. Its models avoid politically sensitive or controversial topics, which may limit their usability for some users. For example, when asked about contentious subjects, the model may simply refuse to answer.

2. Multi-Language Support

DeepSeek excels in English and Chinese but lags behind OpenAI in supporting a wider range of languages. This limitation can affect its adoption in multilingual markets.

3. Scalability Concerns

DeepSeek’s infrastructure, while efficient, may face challenges as global demand grows. Meeting the needs of a larger user base without compromising latency or availability will require significant scaling efforts.

How Does DeepSeek Handle Long Contexts?

DeepSeek’s support for long contexts is one of its standout features. With a maximum context length of 64,000 tokens, it can process and analyze extensive input data. This is particularly useful in applications like:

• Legal Document Analysis: Summarizing and interpreting lengthy contracts and case files.
• Financial Reporting: Extracting insights from large datasets or annual reports.
• Scientific Research: Processing and summarizing extensive academic papers.

DeepSeek achieves this through its context caching system, which reduces redundancy by reusing repeated input. For example, in multi-turn conversations, earlier messages are stored and retrieved from cache, minimizing processing time.

What Are the Latest Features in DeepSeek API?

1. Context Caching on Disk

DeepSeek’s innovative disk-based caching reduces latency and costs by reusing repetitive input. For example, in multi-turn conversations or document analysis, cached content eliminates the need for recomputation. API costs for cache hits are as low as $0.014 per million tokens.

2. JSON Output

DeepSeek enforces valid JSON formatting, making it ideal for applications requiring structured output, such as data pipelines and automation workflows.

3. Function Calling

This feature allows models to interact with external tools, enabling tasks like data retrieval or web scraping. For example, DeepSeek’s function-calling API can fetch live data and integrate it into responses.

4. Chat Prefix Completion

This beta feature lets users specify the starting text for responses, making it easier to control the format and tone of the output. It’s particularly useful for code generation and technical documentation.

How Has DeepSeek Improved Safety and Reliability?

DeepSeek has made significant strides in balancing safety and usability:

• Safety Spillover Reduction: DeepSeek-V2.5 reduced the spillover of safety measures into normal queries, improving user experience.
• Jailbreak Resistance: The model’s resistance to attacks has been enhanced, reducing the risk of generating harmful or unintended outputs.

Safety Metrics:

Model	Safety Score	Spillover Rate
DeepSeek-V2-0628	74.4%	11.3%
DeepSeek-V2.5	82.6%	4.6%

What are the Use Cases for DeepSeek?

DeepSeek’s strengths make it a preferred choice for specific technical and analytical tasks:

• Competitive Programming: Its superior performance in coding contests like CodeForces makes it invaluable for developers.
• Advanced Mathematics: DeepSeek-R1’s ability to generate CoT reasoning makes it ideal for solving complex mathematical problems.
• Data Analysis: DeepSeek excels in summarizing and interpreting large datasets, such as financial reports or survey data.
• Academic Research: Researchers can use DeepSeek for tasks like hypothesis generation, literature review, and data visualization.

How Does DeepSeek Handle Coding Tasks?

DeepSeek has consistently demonstrated strong performance in coding, particularly with its latest V2.5 model. Key capabilities include:

• FIM Completion: Fill-In-the-Middle (FIM) tasks allow users to provide a prefix and suffix for code, enabling the model to generate the missing content.
• Code Debugging: DeepSeek excels in identifying and resolving errors in complex codebases.
• Multilingual Coding: While DeepSeek-Coder-V2-0724 performed slightly better in multilingual tasks, V2.5 retains robust capabilities across major programming languages.

Is DeepSeek Truly Better Than ChatGPT?

The answer depends on the use case. DeepSeek outshines ChatGPT in technical domains like mathematics, coding, and logic-based reasoning. Its efficient resource utilization, cost-effective pricing, and long-context capabilities make it an excellent choice for developers and researchers.

However, for tasks requiring creativity, nuanced conversation, or multi-language support, ChatGPT remains a stronger contender. Additionally, OpenAI’s models have broader integrations and a more established ecosystem, making them more accessible for general-purpose applications.

Conclusion

DeepSeek has proven itself as a disruptive force in the AI landscape, offering high-performance models at a fraction of the cost of competitors like OpenAI. Its technical innovations, such as CoT reasoning and context caching, set it apart in domains like mathematics, coding, and data analysis. 

While it may not entirely replace ChatGPT in creative or conversational tasks, DeepSeek represents a viable and efficient alternative for developers and businesses seeking technical precision and affordability. As it continues to evolve, DeepSeek will likely play a pivotal role in shaping the future of AI.