DeepSeek is a growing star in the AI scene, having successfully integrated creativity, efficiency, and accessibility into its large language models (LLMs). Liang Wenfeng launched DeepSeek in May 2023 in Hangzhou, China, with help from the hedge fund High-Flyer. Since then, the company has quickly become a major player in the AI industry.
DeepSeek's software jumped to the top of the Apple Software Store in January 2025, replacing ChatGPT and racking up an uncommon 2.6 million downloads daily. Its most recent model, DeepSeek-V3, has an attractive 671 billion parameters while costing only 1/30th of OpenAI's API pricing—$2.19 per million tokens against $60.00.
DeepSeek Mission
DeepSeek's primary goal is to fill the gap between advanced AI research and practical applications. DeepSeek is created to tackle strong, industry-specific challenges rather than general-purpose activities. DeepSeek focuses on vertical domains or specific businesses.
It works in finance, healthcare, and legal services, where subject expertise, accuracy, and regulatory compliance are unavoidable. This contrasts broad-spectrum models, such as GPT-4o and o1, which aim to communicate fluently on nearly any topic.
Technical Foundation
The transformer-based model that creates the foundation of DeepSeek's architecture is the same as that of ChatGPT-4, although it has undergone essential efficiency and accuracy improvements:
Sparse Attention Mechanisms: Sparse attention mechanisms are significant for processing long legal contracts or financial reports because they minimize computing prices by focusing only on the most pertinent portions of input data.
Hybrid Training Data: DeepSeek creates industry knowledge by combining domain-specific corpora, including millions of medical articles, SEC filings, court decisions, and engineering manuals, with large, general datasets, such as webpages and books, on which models like ChatGPT-4 train.
Customizable Fine-Tuning: Companies can train DeepSeek on proprietary data, such as hospital medical records, without disclosing private information to other parties.
Unique Value Preposition
- Cost Efficiency: DeepSeek’s ability to meet the performance of OpenAI’s o1 at 10% of the cost has modified the economics of AI.
- Customization: Businesses can safely train and improve DeepSeek models on proprietary data to guarantee industry relevance.
- Open-Source Accessibility: Developers and organizations may utilize DeepSeek's models without requiring significant infrastructure investments, encouraging innovation in various sectors.
- Chinese Language Expertise: DeepSeek’s models successfully process Chinese-language tasks, providing them a significant edge in regional and multilingual environments.
DeepSeek isn’t just another AI startup; it’s an activity aimed at growing AI. Challenging the established authority of proprietary organizations like OpenAI sets new standards for what’s achievable with effective, specialized, and accessible language models.
How to Access DeepSeek?
Whether you are a marketer, a company owner, or a part of an expanding team, DeepSeek is intended to blend in perfectly with your operations. There are multiple methods to include it into your workflow, and getting started is easy:
Use the App: You can communicate with the AI by utilizing the app Access DeepSeek directly through its online platform or app, eliminating the need for installations or downloads. This is a perfect choice for companies that desire easy setup and quick access to its features.
DeepSeek API Key Access: The platform offers reasonably priced API plans if you want to scale or use DeepSeek in your current systems. These plans facilitate the automation of campaign analysis, user insights, and content creation.
DeepSeek AI Modals
DeepSeek uses cutting-edge architectures and effective training methods to develop its models strategically to fulfill particular functions. This is a brief:
- DeepSeek Prover V1.5
DeepSeek-Prover-V1. 5 is an outstanding language model for the Lean 4 theorem, proving that it improves on DeepSeek-Prover-V1 by highlighting the inference and training procedures.
- DeepSeek V2 vs Coder V2
A significant Mixture-of-Experts (MoE) language model, DeepSeek-V2, is identified by its cost-effective training and effective inference. It has about 236 billion parameters, 21 billion of which are accessible for every token.
DeepSeek Coder v2 performs admirably on several coding tests. The DeepSeek-Coder-Instruct 33B model, for example, exceeds OpenAI's GPT-3.5-Turbo on the HumanEval and MBPP benchmarks, showing its work in code generation and completion times. Ollama run Ollama run DeepSeek-coder:33b, which is used by almost all DeepSeekers.
- DeepSeek v2.5
DeepSeek-AI launched Ollama DeepSeek coder 2. 5, a potent Mixture of Experts (MOE) model with 238 billion parameters—160 experts and 16 billion active parameters for optimal performance. DeepSeek 2.5 has seen significant improvements in tasks, including writing and instruction-following. The model is now accessible on the web and API, with backward-compatible API final spots.
- DeepSeek V3
DeepSeek-V3 is a universal model that performs well in various tasks, such as coding, basic problem-solving, and natural language understanding. It utilizes the Mixture-of-Experts (MoE) design, which lowers computational prices while maintaining high performance by selectively activating only the necessary parameters. Because of its flexibility can be utilized in a broad array of sectors, including education, corporate automation, and content production.
- DeepSeek R1
DeepSeek-R1 is a model that develops on the foundations of V3 but extends beyond logical thinking. It is also called Ollama DeepSeek Radeon. It is designed explicitly for reasoning capabilities. It operates exceptionally well for jobs that require extensive Chains-of-Thought (CoT) reasoning, like examining multi-step scenarios, diagnosing complicated issues, and creating insights from sizable datasets. DeepSeek R1 lite provides faster processing speeds and far lower prices while competing with OpenAI's o1 in reasoning challenges.
- DeepSeek R2
Market reports and regulatory documents are examples of domain-specific data employed to train R2, a specialized model focused on financial applications. It assists with creating compliance reports, automating audit workflows, and identifying market risks.
- DeepSeek-Legal
This model, refined on legal datasets, is designed to support case law research, precedent analysis, and contract assessment. It benefits legal professionals and law companies that handle much text-intensive paperwork.
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Frequently Asked Questions
Q. How to use Cursor DeepSeek chat?
Once cursor chat mode is opened, begin typing. Other collaborators in your file will see what you're typing as you type.
Q. What is Neovim DeepSeek coder?
It Enhances Productivity. DeepSeek works with Neovim, a recognized coding tool, to make coding even smoother.
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