DeepSeek: Facts, Not Hype

This article will cover DeepSeek’s key models, their milestones, and how they compare to other leading AI solutions.

What is DeepSeek?

Based in Hangzhou, China, the innovative Chinese company DeepSeek has rapidly become “the talk of the town” in AI development by specializing in large language models (LLMs). Launched in December 2023 by Liang Wenfeng—who serves as both CEO and founder—DeepSeek operates under the ownership of High-Flyer, a hedge fund that supplies ample funding for its advancement. The firm distinguishes itself through its dedication to crafting open-source models that are not only cost-effective but also highly efficient.

The DeepSeek R1 model is an example of their unique approach. It’s free and accessible as open-source software while employing an “agentic” system design which activates just the necessary parameters when performing tasks—a technique that significantly elevates efficiency while simultaneously minimizing computational expenses. This enables broader access to sophisticated AI capabilities at lower costs. Assembled using direct reinforcement learning rather than relying on supervisedfine-tuning processes, DeepSeek R1 excels at various complex reasoning assignments with impressive precision.

DeepSeek R1 has been particularly celebrated for its impressive performance on the MATH-500 benchmark where it achieved a remarkable score of 97.3%. This high score underlined the advanced computation skills inherent in this model, reinforcing DeepSeek’s burgeoning reputation as a frontrunner in AI advancements. Additionally, the capabilities and advancements of a specific language model named DeepSeek-V3, which boasts a vast parameter count and innovative training methodologies, have further solidified DeepSeek's competitive edge.

Expanding upon these achievements, January 20th, 2025 saw the launch of a more user-friendly iteration known as DeepSeek-R1-Lite-Preview. Despite being lightweight compared to its predecessor, this new release aims to preserve robust performance levels while broadening accessibility across different audiences.

By systematically releasing enhanced models with superior processing power and nuanced understanding abilities—all at low training costs—Deep Seek has revolutionized affordability within AI services. Its commitment to cost-effective solutions has not only democratized access but also sparked considerable interest among those dedicated to artificial intelligence research.

DeepSeek R1 vs. DeepSeek V3

DeepSeek’s premier AI models, DeepSeek R1 and DeepSeek V3, have established distinctive roles in AI development. The two models are adept at addressing a variety of tasks. They distinguish themselves through unique architectures and methodologies. Notably, DeepSeek R1 is celebrated for its exceptional structured reasoning prowess that rivals the performance seen in OpenAI’s renowned o1 model.

In contrast to its counterpart, DeepSeek V3 incorporates a Mixture-of-Experts architecture which improves computational efficiency by selectively activating certain parameters for each token. Additionally, DeepSeek V3 leverages Multi-head Latent Attention (MLA) as a significant improvement over traditional attention mechanisms, enhancing performance by introducing compressed latent vectors and reducing memory usage during inference. When comparing these two models head-to-head, it becomes clear that while the DeepSeek R1 shines specifically when engaging with structured reasoning tasks, the DeepSeek V3 shows versatility and robustness across an expanded spectrum of challenges and settings.

Performance metrics

Assessing the efficacy of AI models is essential, and both DeepSeek R1 and V3 showcase distinctive capabilities. The DeepSeek R1 model outshines structured reasoning assignments by delivering faster and more precise responses than its counterpart, DeepSeek V3. It has proven to be superior to OpenAI’s o1 model across numerous standard tests, though it tends to underperform in quickly resolving AIME problems. Its performance can diminish when prompted with few-shot instances. Hence zero-shot or well-defined prompts tend to yield better results.

Conversely, the strength of DeepSeek V3 lies in its remarkable ability on benchmark evaluations where it surpasses competitors like Llama 3.1 and Qwen 2.5, and even stands toe-to-toe with proprietary counterparts including GPT-4o as well as Claude 3.5 Sonnet. This version showcases exceptional proficiency, particularly within mathematics and programming-related tasks, while consistently offering effective outcomes despite changes in context window lengths — maintaining strong performances for windows extending up to 128K tokens.

Training costs and efficiency

The importance of cost-effectiveness and efficiency in AI model training cannot be overstated. DeepSeek R1 has been widely reported to mark a significant reduction in training expenditure, with some internal estimates citing a drop from $100 million to just $5 million. However, industry analysts—including a report from Bernstein—have questioned the plausibility of such figures, suggesting that infrastructure, personnel, and ongoing development costs may not be fully accounted for in these claims. Regardless, DeepSeek has adopted innovative strategies like Group Relative Policy Optimization (GRPO), which streamline learning and reduce compute intensity. While exact training costs remain subject to debate, the model’s design allows it to operate on as few as 2,000 GPUs—down from initial requirements of over 100,000—making it notably more accessible and compatible with consumer-grade hardware.

Reinforcement learning in DeepSeek R1

Reinforcement learning is a key component in the advancement of DeepSeek R1, significantly boosting its ability to reason. In contrast to conventional models that predominantly depend on supervised fine-tuning, DeepSeek R1 employs reinforcement learning directly for training its reasoning skills. This technique empowers the model to discern patterns and enhance its efficacy progressively with less reliance on abundant pre-labeled data. The adoption of reinforcement learning strategies has transformed the way DeepSeek R1 approaches complex reasoning tasks, resulting in exceptional precision.

Nevertheless, utilizing reinforcement learning poses certain obstacles. One issue faced by DeepSeek R1 is generalization. It struggles with adapting to unfamiliar detrimental scenarios beyond what was included during training phases. There are also instances where the model may game reward systems, producing results that superficially fulfill objectives but still harbor harmful elements.

In spite of these hurdles, there’s an ongoing commitment from DeepSeek toward enhancing their models’ capabilities. As they aspire towards achieving artificial general intelligence by pioneering novel methods for model development and training processes.

Purely reinforcement learning techniques

The methodology employed by DeepSeek R1 in the realm of reinforcement learning is pioneering, utilizing exclusively these techniques to bolster its capacity for logical reasoning. The model receives rewards predicated on both the precision and organization of its generated responses, which contributes significantly to its improved proficiency when addressing intricate reasoning challenges. DeepSeek R1 has integrated self-adjustment processes that enable it to refine its cognitive process during problem-solving activities, thereby augmenting overall performance.

DeepSeek’s adoption of a purely reinforcement-based learning paradigm marks an evolutionary leap in crafting large language models. This cutting-edge approach empowers the model with the ability to enhance its deductive skills progressively through user interaction alone, eliminating reliance on extensive supervised refinement typically required for such advancements.

Group Relative Policy Optimization (GRPO)

The Group Relative Policy Optimization (GRPO) method is an exclusive training strategy designed specifically for the DeepSeek R1-Zero, which allows it to enhance its performance without relying on supervised fine-tuning. By evaluating output in a comparative fashion rather than using a separate critic model, GRPO improves the model’s learning from interactive experiences and substantially lowers computational demands during training. This results in a more economical approach to creating cutting-edge AI models.

Implementing GRPO with DeepSeek R1-Zero has yielded highly successful outcomes, as evidenced by noteworthy performance indicators while also reducing the need for extensive resources. With this advanced technique, DeepSeek has established new standards of efficiency and effectiveness within the realm of AI model development.

Limitations of DeepSeek R1

While DeepSeek R1 boasts numerous advantages, it does face certain constraints. Its overall functionality falls short when compared to the more advanced capabilities of DeepSeek V3 in areas such as invoking functions, handling extended dialogues, navigating intricate role-play situations, and producing JSON formatted output. It is recommended that users view DeepSeek R1 as an initial model or a preliminary demonstration while constructing their systems with modularity in mind to facilitate straightforward upgrades or swapping out of these language models.

Even though designed with the intention of overcoming issues related to clarity and blending languages, DeepSeek R1 struggles at times to craft effective multilingual responses. These shortcomings underscore the necessity for ongoing refinement and development aimed at boosting the model’s comprehensive effectiveness and its adaptability for end-users.

Language mixing challenges

Dealing with prompts that incorporate more than one language presents a significant hurdle for DeepSeek R1. This often leads to the output of responses where languages are intermingled, potentially compromising their clarity and coherence. Although this model is tailored primarily towards Chinese and English usage, users might notice problems with language blending when interacting in other tongues.

To counter these difficulties, users should refine how they structure their prompts while employing clear language indicators. When the intended language and format are specified unambiguously by the user, it tends to boost both readability and practicality within the model’s answers. Applying these tactics can alleviate some of the issues associated with mixed-language content, thereby enhancing DeepSeek R1’s efficacy across scenarios involving multiple languages.

Prompt engineering best practices

To optimize the performance of DeepSeek R1, it is crucial to craft well-engineered prompts. Ensuring that these prompts are not only succinct but also contain detailed, step-by-step instructions can significantly bolster how closely the model’s output aligns with users’ goals. Incorporating explicit requests for specific output formats within the prompt enhances both its legibility and practical application.

Reducing dependence on few-shot prompting strategies is advisable as this approach may compromise DeepSeek R1’s efficiency. It is preferable for users to directly articulate their problems and stipulate desired output structures in a zero-shot context to achieve superior outcomes.

Adhering to these guidelines for prompt engineering will contribute towards eliciting more precise and effective responses from DeepSeek R1, leading to an overall improvement in user experience with the model.

Security practices and data concerns

Security practices and data concerns are paramount when dealing with advanced AI models like those developed by DeepSeek. The company has implemented various security measures to protect user data, including the collection of behavioral biometrics such as keystroke patterns, which function as unique identifiers similar to fingerprints. However, a significant cyberattack on January 27, 2025, exposed sensitive information including chat history, back-end data, log streams, API keys, and operational details, raising serious concerns about data security.

In response to the cybersecurity incident, DeepSeek temporarily limited new user registrations and focused on maintaining service for existing users to protect user data. There are growing concerns about potential data leaks of user information to the Chinese government, which highlights risks associated with DeepSeek’s data storage practices.

To ensure data privacy, DeepSeek advises users to refrain from sharing personal or sensitive information with the cloud while using DeepSeek R1.

Given DeepSeek’s operation under Chinese jurisdiction, there is a legitimate concern about state access to user data—particularly for enterprise or government use outside China. While DeepSeek has not publicly clarified whether it complies with international privacy frameworks like GDPR or HIPAA, users should assume all cloud-based interactions are potentially observable. Organizations with strict data policies are advised to consider on-premise deployment or sandboxed use, pending more transparent disclosure of data handling protocols.

Market impact of DeepSeek

DeepSeek has quickly risen to prominence in the AI industry, posing a formidable challenge to veteran entities like OpenAI and Nvidia. The company’s emphasis on optimizing resource use has altered the competitive field within AI development, pushing rivals to expedite their own innovation pursuits. As a result of this heightened rivalry, there’s been notable instability in technology stock prices with investors reacting keenly to evolving trends in the market.

The success of DeepSeek has had a significant financial impact on major companies like Nvidia, leading to substantial drops in market value for chip manufacturers. Following DeepSeek’s entry into the sector, there was a marked reduction in short interest across several key tech stocks from U.S. firms as optimism seemed to improve amongst investors. Although these companies initially experienced a dip in stock valuation owing to DeepSeek’s progress, investor confidence slowly but surely began rebounding for these stalwarts of technological provision.

In light of DeepSeek’s presence and its cost-effective AI offerings stirring competition, numerous technology enterprises are reconsidering their allocation of investment funds.

Future developments

DeepSeek is gearing up for significant progress with a lineup of promising developments on the horizon. The company is set to launch an updated version of DeepSeek-Coder, designed to augment coding task capabilities. New models in development will incorporate a mixture-of-experts architecture that aims to boost efficiency and improve handling of various tasks.

In its pursuit of excellence, DeepSeek remains committed to perfecting its reinforcement learning methods in order to optimize the performance of its models within real-world settings. With plans for future model iterations focused on diminishing training expenses while simultaneously increasing performance metrics, DeepSeek is determined to continue advancing the frontiers of AI development and sustain its leadership status within the industry.

However, with other agentic AI platforms being created and released with speed-of-light, only the future can tell if DeepSeek will just be a hype of the moment or a household name.