Developed in China, DeepSeek has quickly gained global attention for its powerful generative AI capabilities. Many are asking, “What sets DeepSeek apart from other models?” and “What should I know when using it?”
In this column, we’ll explore DeepSeek’s key features, its applications, and essential tips for effective use. Since its release, DeepSeek has been adopted by over 1,000 companies worldwide, praised for its advanced language processing and automation potential.
What is DeepSeek?
DeepSeek is an LLM (Large Scale Language Model) developed by a Chinese AI startup. LLM is an AI technology that generates natural sentences and images just like a human would.
Famous generative AI models that utilize LLM include ChatGPT, Gemini, and Claude, and many of you may have already used them. As you can see, there are many models for generative AI, but what makes DeepSeek different? Let’s take a closer look at DeepSeek’s capabilities:
Lower cost with higher performance
DeepSeek has managed to dramatically reduce development costs compared to other large AI models. Typically, training an AI model requires enormous resources, with development costs reportedly running into the hundreds of thousands of dollars.
However, DeepSeek used efficient learning methods and best-in-class hardware, and this development only cost an extremely low cost of about $5.5 million and took about two months to complete.
Huge data scale
With approximately 671 billion parameters, DeepSeek (V3) has reached a training data volume of approximately 14.8 trillion tokens, which is roughly 1.6 times the size of Meta’s “Llama 3.1 405B” (405 billion parameters), making it one of the largest generative AI models available today.
Larger models enable more complex language understanding and reasoning, allowing them to handle a wider range of tasks with greater accuracy. Thus, taking advantage of its large size, DeepSeek (V3) is expected to be used in a wide range of applications such as programming, analyzing, translating, and writing.
Multi-modal Support
DeepSeek also supports multimodal browsing. Multi-modal refers to the ability to process a variety of information, including images, audio, code, etc., in addition to generating text.
For example, it can read images and describe their contents, convert speech to text, generate code, debug, and more. This is expected to expand its use in creative and educational fields.
Comparison of DeepSeek Model
DeepSeek offers several models. Two of the models that have gained attention as high performance models are “DeepSeek V3” and “DeepSeek-R1”. Here we will explain the features of each model in an easy-to-understand way.
Below is a comparison table of DeepSeek R1 and V3 models based on team collaboration and workflow automation needs, analyzing the key dimensions of performance, efficiency, and cost from the perspective of real-world application scenarios for the average employee:
| Comparison Dimension | DeepSeek V3 | DeepSeek R1 |
| Core orientation | General-purpose language model that specializes in rapid processing of everyday textual tasks | Complex reasoning expert specializing in logical reasoning and deep analysis tasks |
| Applicable Task Types | – Automated document generation (reports, emails, copywriting) – Multi-language translation and cross-team communication – Simple code assistance (scripting, formatting fixes) | – Complex code generation and optimization (algorithm design, bug fixing) – Data modeling and logic analysis (finance, research) – Multi-step problem disassembly (e.g., process automation logic design) |
| Response Speed | Supports multi-tasking in parallel (e.g. handling customer service conversations, content generation at the same time) Low resource consumption, suitable for small and medium-sized teams | Slower response (requires deep inference time) Suitable for non-real-time scenarios (e.g., offline analysis, complex problem research) |
| Multitasking Capability | Supports multi-tasking in parallel (e.g. handling customer service conversations, content generation at the same time) Low resource consumption, suitable for small and medium-sized teams | Deep single-task processing (focusing on a single complex problem) Higher resource consumption, requires high performance computing support |
| Collaboration Support | – Quickly generate meeting minutes and project documents – Automatically respond to emails and work orders to reduce duplication of effort | – Solve technical challenges (e.g. code logic error analysis) – Generate automated solutions for complex processes (requiring multi-step reasoning) |
| Cost and Value for Money | Low API cost (input $0.14/million tokens) Ideal for daily high-frequency tasks with limited budget | High API cost (input $0.55/million tokens) Ideal for high accuracy requirements of critical complex tasks |
| Integration and Development Difficulty | Supports lightweight deployments (e.g. FP8 inference mode), adapts to multiple hardware (AMD GPU, Huawei NPU) | Requires high-performance hardware (e.g., multiple A100 graphics cards) for localized deployments or cloud clustering |
| Typical Scenario Examples | – Automated generation of weekly report templates – Translation for cross-language team communication – Simple data cleansing script generation | – Fixing complex code bugs – Designing automated test framework logic – Financial data modeling and predictive analytics |
Prefer V3 scenarios:
- Need fast response and high concurrency to handle daily tasks (e.g. document collaboration, multi-language communication).
- Small and medium-sized teams with limited resources are looking for cost-effective automation tools. 2.
Prefer R1 scenarios:
- Complex problems requiring in-depth logical reasoning (e.g. algorithm optimization, code error repair).
- Research or technical teams that require high precision and can accept higher costs. 3.
Mixed use strategy:
- In the automated workflow, V3 handles front-end interactions and lightweight tasks (e.g., generating the first version of the document), while R1 is responsible for back-end complex analysis (e.g., data modeling), and improves the overall efficiency through the API crosstalk.
How can Workflow Automation Tool integrate with DeepSeek AI models?
API Ecosystem Building
This layer focuses on enabling basic automation and system integration, often relying on RPAs and APIs to optimize traditional enterprise applications and processes.The introduction of AI helps to improve the efficiency and flexibility of integration.
Application Scenarios:
- Data Synchronization and Transfer: use RPA robotic automation to collect data from different applications and systems and synchronize it via APIs to ensure cross-platform data consistency and reduce manual errors. For example, RPA can be used to synchronize customer data from CRM to ERP systems and update inventory information via API.
- Automated Report Generation: Combining RPA and APIs, decentralized data sources are automatically extracted through a data acquisition interface and pushed to a report generation system via API to automatically create periodic financial, sales or operational reports.
- Automated document management and storage: RPA and API can be combined to automate the uploading, downloading and organizing of documents, and store them to a cloud platform or document management system via API for efficient management and retrieval.
- Customer Support System Integration: Use RPA and API to integrate different customer support tools (e.g., chatbots, CRM systems, work order systems) to automate the initial categorization, assignment, and feedback processes for customer issues.
LLM/NLP/Computer Vision Model Deployment
The cognitive layer involves the application of AI technologies such as Natural Language Processing (NLP), Computer Vision (CV) and Large-scale Language Modeling (LLM). These technologies are able to process unstructured data and enhance the intelligence of automation.
Application Scenario:
- Intelligent Customer Service and Chatbots: utilizing LLM and NLP technologies, combined with RPA bots, to automate the processing of customer voice or text messages, understand intent and automatically generate responses. It can be used to handle frequently asked questions, pre-process support requests or provide 24/7 customer service.
- Document Automation Processing: Using computer vision and NLP technology to automatically extract key information from documents, such as invoices, contracts, orders, etc., combined with RPA for categorization and data entry, thus reducing manual intervention.
- Sentiment Analysis and Public Opinion Monitoring: Combined with NLP technology, RPA can automate the capture of information on social media and news sites, analyze customer feedback, public opinion comments, etc. through sentiment analysis models, and take timely action.
- Automated email categorization and response: NLP technology is used to automatically categorize and filter email content (e.g., handling customer inquiries, complaints, etc.), and trigger corresponding automated replies or task assignments based on the email content.
- Intelligent OCR (Optical Character Recognition) Application: Apply computer vision technology to scan paper documents and convert them into structured data, which can then be automatically processed and stored by RPA. Suitable for document processing in banking, insurance, medical and other industries.
Reinforcement Learning Dynamic Optimization Engine
At the decision-making layer, reinforcement learning (RL) and dynamic optimization engines can deal with complex decision-making problems by self-learning and optimizing so that the system can continuously adjust and improve according to the changing environment.
Application Scenario:
- Supply chain optimization: Combined with the reinforcement learning engine, RPA can perform dynamic scheduling and inventory management throughout the supply chain, optimizing inventory levels, distribution routes, and supplier selection in real time based on market demand, production capacity, logistics conditions, and other factors.
- Intelligent Pricing and Sales Optimization: Based on the reinforcement learning model, RPA is able to automatically adjust pricing strategies and sales processes, and optimize price change strategies to increase sales or profits. For example, on e-commerce platforms, RPA can automatically adjust product pricing based on demand fluctuations, competitive prices and other factors.
- Personalized Recommendation System: Through reinforcement learning models, RPA can dynamically adjust recommendation algorithms on e-commerce or content platforms to push the most relevant products, content, or advertisements in real time based on user behavior and preferences.
- Automated Risk Control and Credit Approval: Combining reinforcement learning and RPA, an automated risk assessment engine can optimize the credit approval process based on historical data and real-time information, reducing manual intervention and improving decision-making accuracy.
- Intelligent Manufacturing and Equipment Maintenance: Through reinforcement learning models, combined with RPA, real-time data collection and analysis of the production line is performed to dynamically adjust the production plan and automatically initiate warnings or maintenance requests in case of equipment failure or abnormality.
- Financial trading optimization: In the financial field, the reinforcement learning engine can analyze market fluctuations and historical data in real time, automatically generate optimal trading strategies, and execute these trades through RPA robots to maximize investment returns.
Octoparse AI: AI-Driven Workflow Automation Tool
Octoparse AI is now a perfect collection of Deepseek’s AI capabilities. Through Deepseek’s API connection, you can use AI to generate the corresponding text and build the steps in the workflow.
LinkedIn Auto Connect, this application, which has a process already set up, incorporates AI capabilities to automatically send connection requests with personalized notes to help you expand your LinkedIn network more effectively. It uses advanced AI algorithms to grab LinkedIn profile data based on keyword queries you give, allowing you to export that information to Google Sheets.
With its AI-driven features, you can effortlessly identify and reach out to genuine prospects, making meaningful, targeted connections while saving time.
