DeepSeek V3 and DeepSeek R1: A Look at the Open-Source AI Revolution

DeepSeek, a Chinese AI startup founded in 2023 by Liang Wenfeng, a former co-founder of a major Chinese hedge fund, High-Flyer, has been making waves in the AI world. They're known for their innovative and efficient large language models (LLMs). Like OpenAI, DeepSeek's goal is to make Artificial General Intelligence (AGI) a reality. However, unlike their counterparts in US Big Tech, they are accomplishing this through open-source development. This article explores two of their most well-known models, DeepSeek V3 and R1, as well as their code-generating model, DeepSeek Coder. We'll look at their features, how they were created, how they perform, and what they mean for the future of AI.

DeepSeek: The Company and its Mission

DeepSeek has quickly risen in prominence for its Open Source LLMs, which are different from what we've seen before in AI. The company’s contributions include:

• Open-Source Development: DeepSeek believes in open-source, which means more collaboration and faster AI development. This lets researchers and creators use, change, and build on their models' code, even for commercial purposes.
• Affordable Solutions: DeepSeek's models offer high performance at a fraction of the cost of comparable models, making AI more widely accessible. This disruptive pricing strategy has sent ripples through the AI market, prompting competitors to re-evaluate their own pricing models.
• Great Reasoning Abilities: DeepSeek R1 is as capable as other well-known state-of-the-art models like GPT-4 at solving complex problems. The model isn't just about generating text; it applies chain-of-thought reasoning to optimize its responses.

DeepSeek's efficient implementation of these pioneering concepts has positioned them to be a major disruptor in a landscape dominated by Big Tech. Numerous Chinese tech companies, like Moore Threads, Hygon Information Technology, and Huawei Technologies, have started using DeepSeek's models in their AI offerings and cloud services. DeepSeek's motivation for focusing on efficient reasoning models stems from a belief that many AI research teams are caught in "traps" that hinder progress: the pressure to deliver frequent results, the tendency to prioritize scaling as a measure of importance, and the reliance on existing models for generating synthetic data. Nevertheless, critics often cite evidence that DeepSeek itself also made use of other LLMs when generating synthetic data for training.

DeepSeek V3: Efficient and Versatile

DeepSeek V3, released in December 2024, is a massive model with 671B parameters. It uses a Mixture-of-Experts (MoE) approach, which means it is efficient without sacrificing performance. During inference, this model uses only the parameters necessary to generate relevant responses rather than all 671B, thereby saving computational resources.

Key Features and Innovations

• Mixture-of-Experts (MoE) Architecture: DeepSeek V3 is built with an MoE architecture, which means it's divided into specialized sub-networks, each good at different things. This allows it to only use the parts it needs for a task, kind of like how a company assigns different teams to different projects.
• Multi-head Latent Attention (MLA): This helps the model find the important information in the text multiple times, making it more accurate and understanding. Think of it like reading something multiple times, each time focusing on something different to understand it better.
• Multi-Token Prediction: DeepSeek V3 can generate multiple tokens at the same time, which makes it faster. This means you get quicker and smoother interactions with the model.
• 128K Context Window: DeepSeek V3 has a large context window, meaning it can handle a lot of information at once. This is important for tasks that involve long documents or complex conversations.

DeepSeek R1: The Reasoning Pro

DeepSeek R1, released in January 2025, builds on DeepSeek V3 with the focus of enhancing reasoning abilities. It has a special "think format" that shows you all the steps that the model goes through while reasoning. R1 powers DeepSeek's own chatbot app, which soared to the number one spot on Apple App Store after its release, dethroning ChatGPT.

Key Features and Innovations

• Reinforcement Learning (RL) Focus: DeepSeek R1 uses a reinforcement learning approach, meaning it tries to get better at reasoning on its own, based on algorithmic feedback.
• "Think Format": This feature lets you peer into the model's reasoning process, making it easier for humans to understand how it makes decisions.
• Open-Source Availability: eepSeek R1 is open-source, so developers and creators can use, change, and build on its code.

Training Approaches

Both DeepSeek V3 and R1 use huge datasets and advanced training methods. But they have different focuses on reinforcement learning and fine-tuning.

Standard Approach (DeepSeek V3)

DeepSeek V3's training is more traditional and has three main steps:

• Pre-training: First, the model is trained on a massive amount of text and code to learn general language patterns. This data covers many languages and topics, giving it a broad understanding of human knowledge. This is a standard step for all modern language models. (Fun fact: the “P” in GPT stands for “Pre-trained”, and is a reference to this step.)
• Supervised Fine-tuning (SFT): Experts then refine the model using data that humans have annotated (or machine synthesized) to improve its grammar, coherence, and accuracy. This step makes sure the model's output meets human expectations.
• Reinforcement Learning (RL): After SFT, they use reinforcement learning to further improve the model and align it with human’s preferences. This allows the model to learn from its interactions and get better over time.

RL-focused Approach (DeepSeek R1)

DeepSeek R1 takes a different path, focusing on reinforcement learning from the beginning:

• Base Model: DeepSeek R1 starts with the same base model as DeepSeek V3 (DeepSeek-V3-Base).
• Reinforcement Learning (RL): RL is the core of R1's training, allowing it to learn reasoning patterns and human preferences on its own. This helps the model develop its reasoning organically.
• Supervised Fine-tuning (SFT): SFT is used after RL mainly to polish the model's output and make it easier to read. This different approach resulted in a model with strong Chain-of-Thought (CoT) reasoning capabilities. While developing R1, DeepSeek also experimented with an R1-Zero model, which was good at reasoning and math but had some issues with language. DeepSeek also uses model distillation techniques, taking the best parts of DeepSeek-R1 to create smaller, more efficient models based on architectures like Llama and Qwen. This approach allows for wider accessibility and deployment of reasoning capabilities across various devices and platforms.

Performance Comparison

Both DeepSeek V3 and R1 have shown state-of-the-art performance across various benchmarks. But they have different strengths:

DeepSeek V3

• Efficiency: DeepSeek V3 is efficient, requiring fewer resources than models of similar performance. This is due to its MoE architecture and optimized training.
• Generalization: V3 performs well on a wide range of tasks and generalizes effectively to different domains and contexts . This makes it a versatile tool for various applications, from language translation to content generation.
• Scalability: V3 is designed for large-scale deployments and can handle high-throughput workloads in cloud environments. This makes it suitable for enterprise-level applications and services that require high availability and responsiveness.

DeepSeek R1

• Reasoning: DeepSeek R1 demonstrates superior reasoning abilities, particularly in structured tasks like mathematics and coding . This strength is attributed to its RL-focused training approach.
• Speed: R1 is fast, especially on hardware with limited computational power, making it perfect for applications that need quick responses.
• Accuracy: In niche tasks like code debugging and data analysis, R1 often achieves higher accuracy than V3.

Benchmark Results

These benchmarks highlight the strengths of each model. DeepSeek V3 performs well across a broader range of general knowledge and language understanding tasks, while R1 excels in specialized areas like mathematics and coding.

DeepSeek vs. ChatGPT

DeepSeek's models, especially R1, are often compared to OpenAI's GPT Series. Both are good at generating language, but they have key differences in performance, accessibility, and overall philosophy.

DeepSeek's open-source nature and focus on efficiency make it a good option if you care about cost and customization. But ChatGPT's user-friendly design and accessibility make it better for casual users and those who want easy integration with other tools.

DeepSeek Coder

Besides V3 and R1, DeepSeek also has DeepSeek Coder, a model specifically for coding. It was trained on a dataset consisting of mostly code (87%) and some natural language (13%), making it good at understanding and writing code in different programming languages. DeepSeek Coder is great for developers, offering efficient and accurate code generation, help with debugging, and code completion.

Pricing

DeepSeek's pricing is a big part of its rise to prominence. Here's a comparison of the costs for DeepSeek-R1 and DeepSeek-V3:

When it comes to creating the models themselves. DeepSeek claims to have spent only $5.58 million to train the R1 model, which is much less than what companies like OpenAI incurred for their GPT models.

What This Means for AI

DeepSeek V3 and R1 are changing the AI world in big ways:

• Increased Accessibility: DeepSeek's open-source approach and affordable pricing make advanced AI available to more people, including researchers, developers, and businesses. This can lead to faster innovation and wider adoption of AI across different industries.
• You can access DeepSeek's models in several ways, including an API, a website (deepseekv3.com/chat), and by deploying them locally (assuming you have enough computational resources to run them).
• Shifting Focus to Efficiency: DeepSeek's success with efficient models like V3 challenges the idea that bigger is always better in AI. This encourages developers to create AI solutions that use less resources, which could change how we think about AI development. Conclusion DeepSeek V3 and R1 represent significant advancements in the field of large language models. V3 prioritizes efficiency and versatility, making it a practical choice for various applications, while R1, with its focus on reasoning and transparency, pushes the boundaries of AI capabilities. Both models contribute to the growing open-source AI movement, increasing accessibility and fostering innovation. DeepSeek's approach to AI development differs significantly from that of other leading companies like OpenAI. While OpenAI focuses on building large, highly capable models under closed source, DeepSeek prioritizes efficiency, open-source availability, and cost-effectiveness. The potential long-term impact of DeepSeek's models is substantial. Increased accessibility can lead to wider adoption of AI across various industries, while the focus on efficiency can drive the development of more sustainable and resource-conscious AI solutions. However, ethical considerations surrounding censorship, data privacy, and responsible use need to be carefully addressed. DeepSeek's journey is a testament to the evolving nature of the AI field, where both performance and efficiency are becoming increasingly important. As DeepSeek continues to develop and refine its models, GPT- trainer will be closely following its evolution and introduce relevant integrations where appropriate.