What Is DeepSeek? Everything You Need to Know About the AI Challenger

For the first two years of the AI boom, a single narrative dominated the industry: creating state-of-the-art Artificial Intelligence requires massive, trillion-dollar tech conglomerates. The assumption was that only companies like Microsoft (via OpenAI) or Google had the compute power, the data center infrastructure, and the billions of dollars required to train frontier models. The barrier to entry was considered insurmountable for startups.

Then, a relatively unknown Chinese AI startup named DeepSeek upended a central assumption in the industry. They released a series of models—most notably DeepSeek V3 and DeepSeek R1—that equaled or surpassed the performance of OpenAI's flagship models on major benchmarks. More shockingly, they did it using a fraction of the hardware, a fraction of the budget, and then released the model weights as open-source for the entire world to download for free.

On January 27, 2025, NVIDIA's stock fell roughly 17% in a single session — an estimated $600 billion loss in market capitalisation — after investors concluded that DeepSeek's efficiency undermined the assumption that frontier AI required ever-larger GPU clusters. [SOURCE NEEDED — verify exact NVIDIA market cap loss figure at publishing] This article covers what DeepSeek is, how it achieves its cost and performance claims, and how to put it to practical use.

Who Built DeepSeek?

DeepSeek was founded in 2023 by Liang Wenfeng, a Chinese entrepreneur who previously made his fortune in quantitative trading and algorithmic finance (specifically through a firm called High-Flyer Quant). This background is crucial to understanding DeepSeek's success.

Quantitative trading firms live and die by algorithmic efficiency. They are obsessed with squeezing every ounce of mathematical performance out of limited computing hardware. When Liang pivoted this talent pool toward artificial intelligence, the team didn't try to win by buying the most chips; they won by writing vastly more efficient training algorithms.

The DeepSeek Model Family

DeepSeek has released several models since 2023. The naming is confusing at first — here is a plain-English map of what exists and what each one is for.

The distilled models deserve a specific mention. When DeepSeek released R1, they also published smaller variants ranging from 7 billion to 70 billion parameters — trained to mimic R1's reasoning process using Meta's Llama and Alibaba's Qwen architectures as the base. These distilled variants are what most people actually run when they deploy DeepSeek locally, because they fit on consumer hardware while retaining a substantial portion of the full R1's reasoning capability.

The Breakthrough: How DeepSeek Disrupted the Industry

To understand why DeepSeek is such a big deal, you have to understand the two major bottlenecks in AI development: Training Costs and Reasoning Capabilities.

1. The Cost Miracle (Mixture of Experts)

Training a massive model like GPT-4 is estimated to have cost OpenAI over $100 million in pure server compute time, using tens of thousands of NVIDIA GPUs. DeepSeek trained their V3 model for roughly $6 million.

They achieved this massive cost reduction through extreme architectural optimization, most notably by aggressively using a Mixture of Experts (MoE) architecture. Instead of activating the entire massive neural network for every single word it generates, the MoE architecture routes the prompt only to the specific "expert" sub-networks required for that task. If you ask a coding question, it only activates the coding experts; the poetry experts stay asleep. This drastically reduces the compute power required to run the model, allowing DeepSeek to offer API access at prices that are often 90% cheaper than OpenAI or Anthropic.

2. The Reasoning Revolution (DeepSeek R1)

While the V3 model was an incredible general-purpose chatbot, the release of DeepSeek R1 changed the landscape for developers. R1 is a "Reasoning Model," built to directly compete with OpenAI's o1 model.

Standard LLMs predict the next word immediately. R1 utilizes Reinforcement Learning to teach itself how to "think." When given a complex coding bug or a math puzzle, R1 enters a "Chain of Thought" phase. It outputs a visible `` block where you can watch it break the problem into steps, test different hypotheses, identify its own logical errors, and self-correct before generating the final answer.

By releasing the weights of this reasoning model for free, DeepSeek put reasoning capabilities that developers previously had to pay OpenAI API rates to access into the hands of anyone with a capable laptop.

How DeepSeek Benchmarks Against the Competition

The claim that DeepSeek R1 rivals OpenAI's flagship models is not marketing — it is documented in third-party benchmark evaluations. [SOURCE NEEDED — cite specific benchmark papers and leaderboard results]

Figures from DeepSeek's technical report [SOURCE NEEDED — verify against independent evaluations before publishing]. Independent reproduction has broadly confirmed competitive performance on reasoning tasks, though results vary by evaluation setup.

Two observations from the table: R1 leads on pure mathematical reasoning, and it trades close with o1 on coding benchmarks — at a fraction of the API cost. On general knowledge (MMLU), it sits slightly below o1 but ahead of GPT-4o. For users whose primary use cases are coding and math, the benchmark case for R1 is strong. For an effective prompting strategy to get the most out of R1, see our DeepSeek prompts guide.

How to Use DeepSeek Today

Because DeepSeek is open-source, there are multiple ways to access its intelligence, ranging from simple web interfaces to complex local deployments.

1. The Consumer Web App

The easiest way to use DeepSeek is navigating to chat.deepseek.com or downloading their official mobile app. The interface is nearly identical to ChatGPT. Toggle DeepThink to activate the R1 reasoning model — use it for complex coding problems, mathematical proofs, multi-step analysis, or anything where accuracy matters more than speed. For routine writing, summarisation, or simple Q&A, leave DeepThink off; it is noticeably slower and consumes your free-tier quota faster. The Web Search toggle lets the model pull live results from the internet and cite sources inline — useful for current events or any query where the training cutoff is a limitation. The free tier enforces rate limits during peak demand; if you hit a wait queue, the API is a practical alternative for high-volume work.

2. The Developer API

For software engineers building AI applications, the DeepSeek API is the primary draw. Because it is so heavily optimized, the API costs are fractions of a penny per thousand tokens. Startups that were previously priced out of building automated AI workflows using OpenAI can now afford to run massive, high-volume automated testing and data extraction pipelines using the DeepSeek API.

API Pricing: The Real Cost Advantage

The "90% cheaper" claim needs real numbers to be useful. Here is a direct comparison as of early 2025 [SOURCE NEEDED — verify current pricing from official API pages before publishing]:

API pricing changes frequently — verify against official pricing pages before building a cost model. For a team running 10 million output tokens per day (a realistic volume for an automated content pipeline or code review system), the difference between GPT-4o and DeepSeek V3 is approximately $26,000 per month versus $3,300 per month. That gap is why enterprise adoption of the DeepSeek API accelerated quickly after the January 2025 release.

3. Running It Locally (The Privacy Advantage)

This is the most powerful feature for enterprise users. Because Anthropic and OpenAI are closed-source, using their models requires sending your proprietary data to their cloud servers. This is a massive security risk for banks, defense contractors, or hospitals.

Because DeepSeek is open-source, you can download the model files directly from Hugging Face. Using software like LM Studio or Ollama, you can run DeepSeek R1 locally on a standard MacBook Pro or a private corporate server. The AI runs completely offline, ensuring your proprietary code or patient data never leaves the building.

Web App Privacy vs. Local Privacy: Not the Same Thing

The open-source availability of DeepSeek's weights creates a privacy distinction the article's structure risks obscuring: running the model locally is private; using the web app is not.

When you use chat.deepseek.com or the DeepSeek mobile app, your conversations are processed on DeepSeek's servers, which are located in China and subject to Chinese data law. DeepSeek's privacy policy states that it collects user inputs, device identifiers, and usage patterns. [SOURCE NEEDED — cite DeepSeek privacy policy URL] For individuals asking general questions, this is a minor concern. For professionals sharing client data, proprietary code, or confidential business information through the web interface, it is a significant one.

When you run the model locally via Ollama or LM Studio, none of this applies. The model file is a static set of numerical weights. It cannot initiate network connections, cannot transmit data, and has no mechanism to report activity back to any server. The privacy guarantee is architectural, not policy-based — it holds regardless of what DeepSeek's terms of service say.

The practical rule: use the web app for general research and everyday tasks. Use the local deployment for anything you would not put in an email to a stranger.

The Controversies and the Geopolitical Impact

The rise of DeepSeek has not been without significant controversy, touching on international geopolitics, censorship, and the global supply chain.

The Export Ban Evasion

The United States government placed strict export bans on high-end AI chips (like the NVIDIA H100) to prevent Chinese companies from acquiring the hardware necessary to train frontier AI models. DeepSeek's success proved that these bans were largely ineffective. The company managed to train world-class models using the H800 — a downgraded H100 variant that NVIDIA created specifically to comply with US export control rules — by writing vastly more efficient software to compensate for the hardware limitation. [SOURCE NEEDED — verify H800 as primary training chip]

Censorship and State Alignment

Because DeepSeek is based in China, it must comply with strict state censorship laws. If you ask the DeepSeek web app questions regarding sensitive historical events (such as the 1989 Tiananmen Square protests) or controversial political figures, the model will output the government-approved narrative or refuse to answer entirely. For users outside of China, this heavy political filtering is a significant drawback when using the model for historical or political research. The local deployment option sidesteps this entirely — running the model offline removes all server-side filtering.

The Open Source Debate

DeepSeek's decision to open-source its models has fueled the intense debate in the AI safety community. Critics argue that releasing highly capable reasoning models for anyone to download allows bad actors to generate malware, automate cyberattacks, or synthesize dangerous materials without any corporate oversight. Proponents argue that open-source is the only way to prevent a few massive American tech monopolies from controlling the future of human knowledge.

Conclusion: The Democratization of Intelligence

DeepSeek's January 2025 release produced one of the largest single-day market cap losses for an AI-adjacent company on record — a direct signal that the industry's efficiency assumptions had broken down. [SOURCE NEEDED] They proved that frontier AI is a software engineering problem, not just a hardware spending contest.

For the average consumer, DeepSeek provides a capable alternative to ChatGPT at no cost. For developers, it provides API access at a fraction of OpenAI's rates and the option to run state-of-the-art reasoning models entirely offline. Regardless of the geopolitical context, DeepSeek has accelerated access to capable AI models in a way that neither OpenAI nor Google anticipated.

Next Reads: How to Run DeepSeek Locally — DeepSeek vs ChatGPT for Coding