Is DeepSeek R1 Right for Your Business?

The landscape of Large Language Models (LLMs) is evolving rapidly, with models like GPT-4o, OpenAI’s o1, and the more recent o3-mini, Google’s Gemini, Anthropic’s Claude, and Meta’s Llama becoming integral to business applications. Now, a new player has entered the field: DeepSeek R1. Developed by a Chinese AI firm, this model has gained attention for its cost efficiency, reasoning capabilities, and open-source nature. But does it offer enough to disrupt the market, or is it simply another alternative among many?

This article, based on thorough research by the Plain Concepts Research team, examines DeepSeek R1’s core technology, compares it with its main competitors, and explores its best use cases. Business professionals will gain a clear perspective on whether DeepSeek R1 fits their specific needs or if they should consider a different model.

For those who want the full analysis, keep reading as we break down DeepSeek R1’s technology, strengths, and limitations. If you want to know whether DeepSeek R1 is right for your business, jump to the conclusions.

DeepSeek R1 stands out due to its Mixture-of-Experts (MoE) architecture [1], which differs from the standard transformer-based models used by most competitors. Instead of processing all parameters for every query, only a subset (37 billion out of 671 billion) is activated per request, improving efficiency and reducing computational costs.

Other key innovations include:

• Multi-head Latent Attention (MLA): Reduces the Key-Value cache, optimizing inference.
• DeepSeekMoE: A specialized approach for handling expert activation, ensuring efficient learning.
• Auxiliary-loss-free load balancing: Prevents inefficiencies in training without degrading performance.
• Multi-token prediction (MTP): Enables predicting multiple future tokens simultaneously, boosting efficiency and inference speed.

DeepSeek R1 was trained on 14.8 trillion high-quality tokens, with an emphasis on mathematics, programming, and multilingual content. It supports a context length of 128K tokens, enabling effective handling of long documents, though it is still behind Gemini 1.5 Pro’s 1 million tokens.

A key advantage is its reinforcement learning approach using Group Relative Policy Optimization (GRPO). This eliminates the need for a separate value function model, making the fine-tuning process more efficient. Comparatively, OpenAI’s o1 also uses reinforcement learning and is specifically designed for complex reasoning tasks, while GPT-4o remains a more general-purpose model with broader applications.

Performance Benchmarks

1. Retrieval-Augmented Generation (RAG) Capabilities: Our tests using Azure Search and Azure Foundry with a hotel database indicate that DeepSeek R1 performs as well as GPT-4o in retrieval-augmented generation tasks. DeepSeek R1 stands out in this area due to its explicit chain-of-thought reasoning, which provides greater clarity and transparency in its responses.
2. Reasoning & Mathematics: DeepSeek R1 demonstrates exceptional logical reasoning, often outperforming GPT-4o in math-heavy benchmarks. OpenAI’s o1 is its main competitor in STEM reasoning, scoring higher in mathematical assessments and scientific reasoning tasks.
3. Coding Capabilities: DeepSeek R1 ranks among the top for code generation, rivaling Claude 3.5 Sonnet and OpenAI’s o1-mini, which is optimized for coding.
4. General Knowledge & Language Understanding: While DeepSeek R1 excels in factual accuracy, particularly in Chinese, it trails GPT-4o in English-language comprehension.
5. Long-Context Processing: With a 128K token window, DeepSeek R1 surpasses Claude and GPT-4o but is still behind Gemini 1.5 Pro (1M tokens).
6. Multilingual Support: DeepSeek R1 performs well in English and Chinese, although in some English tests, it can mix some Chinese characters. Although other sources state that it also performs well across multiple languages, in our tests the performance is bad in other languages. Ifa  language other than these two is a priority, Llama 3.1 offers broader multilingual coverage.
7. Velocity of Response: While other sources suggest that DeepSeek R1 offers faster processing than OpenAI’s o1, particularly in technical queries, at the moment the reality is that it is much slower. On the contrary, the other models such as GPT-4o balance speed and adaptability, as both DeepSeek R1 and o1 take longer due to their detailed reasoning process.
8. Custom System Prompts & Function Calling: DeepSeek R1 does not support custom system prompts well, which limits its flexibility in structured AI interactions. Additionally, it currently does not support function calling, a feature that is available in GPT-4o and some other models, potentially restricting its use in complex automation and integration scenarios.

Cost Efficiency

DeepSeek R1’s operational costs are significantly lower than its competitors. API pricing shows that it is 100 (input tokens) and 200 (output tokens) times cheaper per token than OpenAI’s o1, making it an attractive option for businesses seeking to minimize expenses.

Comparison of API costs:

• DeepSeek R1: $0.14 per million input tokens, $0.28 per million output tokens [2]. Local deployment of the full model is possible, for example, in Azure, it would need two Standard_NC24ads_A100_v4 instances, which would cost €2572 per month running 24/7. On Azure AI Foundry and NVIDIA build it is deployed for free at the moment [3], [4].
• OpenAI’s o1: $15 per million input tokens, and $60 per million output tokens [5].
• OpenAI’s o3-mini: $1.10 per million input tokens, and $4.40 per million output tokens [5].
• GPT-4o: $2.50 per million input tokens, and $10 per million output tokens [5].
• Claude 3.5 Sonnet: $3 per million input tokens, and $15 per million output tokens [6].

Unique Features & Considerations

• Autonomous Agent Capabilities & Coordination: Our tests indicate that DeepSeek R1 currently cannot function as an autonomous agent or coordinate with other agents. This limitation is primarily due to its lack of function calling support, which restricts its ability to execute structured tasks collaboratively. In contrast, GPT-4o is capable of performing both tasks successfully.
• Image Analysis Limitations: Unlike OpenAI’s Operator, DeepSeek R1 does not support image analysis, further reducing its applicability in multimodal AI workflows.
• Architecture: DeepSeek R1’s MoE architecture enhances efficiency. OpenAI’s o1 is designed specifically for deep reasoning, whereas GPT-4o and Claude rely on traditional transformers.
• Speed: While DeepSeek R1 was expected to offer fast processing, in reality, it highly depends on the deployment used. For example, our tests using Azure Foundry showed that DeepSeek R1 api calls range between 20 and 100 seconds [3], while o1 api calls range between 4 and 6 seconds. NVIDIA’s deployment matches o1’s speed at the moment [4].
• Custom System Prompts & Function Calling: DeepSeek R1 lacks robust support for these features, making it less flexible for structured automation and application integration.
• Open-Source Advantage: Unlike GPT-4o and Claude, DeepSeek R1 is open-source, allowing full transparency and customization.

DeepSeek R1 in Edge Computing

DeepSeek R1 has been tested by our team in edge environments using distilled models running locally with WebGPU. The results reveal important insights:

• Llama-Based Distillation (8B parameters): Shows slight improvement in reasoning over source code and recognizing greetings in Spanish, but worsens in logical problem-solving.
• Qwen-Based Distillation (7B parameters): Performs worse in all types of problems. In fact, the base Qwen model (without DeepSeek distillation) is the best in edge environments and the only one that successfully answered our test queries.
• Multilingual Performance: All models running on edge perform worse when queried in Spanish.
• Reasoning Speed: DeepSeek models take an excessively long time to process reasoning tasks, making them impractical given the results obtained.
• Inference Stability: DeepSeek often enters infinite loops during local reasoning, causing inference failures.

When to Choose DeepSeek R1

• Mathematical & Technical Problem-Solving: Ideal for scientific research, engineering, and finance.
• Cost-Conscious AI Development: Suitable for startups and companies needing low-cost, high-efficiency models.
• Software Development & Coding: Competitive with Claude and OpenAI o1-mini in automated programming tasks.
• Open-Source Customization: Businesses needing custom AI solutions will benefit from DeepSeek R1’s transparency.
• Chinese Market Applications: Optimized for Chinese language comprehension.
• Retrieval-Augmented Generation (RAG) Tasks: Performs as well as GPT-4o in retrieval-augmented generation using Azure Search and Azure Foundry, with the added advantage of explicit chain-of-thought reasoning, which enhances transparency and clarity in responses.

When to Consider Other LLMs

• For Advanced Logical & Scientific Reasoning: OpenAI’s o1 remains the strongest alternative, particularly for complex problem-solving and STEM applications.
• For Creative & Marketing Tasks: GPT-4o and Claude 3.5 Sonnet are superior in storytelling and conversational AI.
• For Unrestricted Content Generation: OpenAI models offer broader coverage without censorship concerns.
• For Google Ecosystem Integration: Gemini 1.5 Pro provides the best enterprise connectivity.
• For Multilingual Enterprises: Llama 3.1 supports more languages overall.
• For Advanced Automation & Integration: If function calling and system prompt customization are critical, GPT-4o or Claude would be better choices.

Bias & Censorship

• DeepSeek R1 is subject to political censorship and content restrictions.
• GPT-4o and Claude 3.5 have biases linked to Western datasets.
• OpenAI’s o1 focuses on reasoning but may have hidden moderation filters.

Transparency & Data Privacy

DeepSeek R1’s open-source nature allows greater transparency, but its Chinese origins raise data privacy concerns, particularly regarding GDPR compliance. In contrast, OpenAI and Anthropic implement strict data privacy policies.

Responsible AI Deployment

• DeepSeek R1’s open-source nature allows businesses to implement their own safeguards.
• Claude emphasizes ethical AI, reducing risks in sensitive applications.
• Businesses handling regulated industries should carefully assess each model’s compliance policies.

DeepSeek R1 is an attractive alternative for businesses prioritizing cost efficiency, technical problem-solving, and customization. Its superior STEM capabilities, strong RAG performance, and open-source framework make it a strong choice for AI development, coding, research applications, and retrieval-augmented generation.

Which one should I use?

However, there are key limitations:

• Multilingual performance is poor outside of English and Chinese.
• The velocity of response is much slower than GPT-4o, contrary to some initial claims.
• The lack of function calling and weak custom system prompt handling make it less ideal for advanced automation.
• Local AI deployment has significant drawbacks according to our experience. Businesses requiring local AI deployment should consider Qwen or other alternatives
• Cannot function as an autonomous agent or coordinate with other agents, unlike GPT-4o, which excels in both areas.
• No support for image analysis, limiting its ability to work in multimodal AI applications like OpenAI’s Operator.

For those needing complex reasoning capabilities, OpenAI’s o1 remains the primary alternative to DeepSeek R1, offering high precision and reliability in logical problem-solving. If retrieval-augmented generation is a priority, DeepSeek R1 and GPT-4o are equally strong choices. If speed, multilingual support, and flexible integrations are crucial, GPT-4o or Claude 3.5 Sonnet might be the better option.

Ultimately, the choice depends on your business priorities. If technical performance and affordability matter most, DeepSeek R1 is a top contender. If deep reasoning is essential, o1 should be considered. If speed, multilingual support, and automation flexibility are critical, GPT-4o or Claude may be the better fit. By weighing these factors, business leaders can make informed decisions in the rapidly evolving AI landscape.

References

• [1] DeepSeek-V3 Technical Report
• [2] DeepSeek Models & Pricing
• [3] DeepSeek on Azure AI Foundry
• [4] NVIDIA’s DeepSeek R1
• [5] OpenAI API Pricing
• [6] Anthropic API Pricing