The rapid development of Artificial Intelligence (AI) presents a variety of tools that can help us in various activities. DeepSeek is one of the AIs that has been widely discussed lately, touted as having capable capabilities at a more efficient cost.
DeepSeek is here as one of the chatbots that offers a different approach and is able to compete with various types of chatbots that are no less interesting, you know! Each has its own uniqueness and advantages, starting from the ability to answer, the context of understanding, to how to interact with users.
One of the important innovations in the world of artificial intelligence technology is the presence of DeepSeek which offers a unique approach to language processing.
So what are the advantages of the various innovations that have succeeded in enriching this chatbot ecosystem? Let's discuss the advantages and comparison of DeepSeek with GPT and Claude in the AI chatbot ecosystem in the following article.
DeepSeek vs. ChatGPT vs. Claude: Which Better?
DeepSeek is one of the latest innovations in the world of artificial intelligence designed to process language in depth. Compared to conventional chatbot models, DeepSeek is designed to more efficiently capture the deeper meaning of a sentence, as well as provide more relevant and contextual responses.
In addition, DeepSeek offers more sophisticated algorithms and learning approaches. By prioritizing context and semantic analysis, this model is not only able to parse sentences, but also understand the more complex meaning of a statement.
Some of the advantages of DeepSeek are as follows.
- Deeper understanding of context.
- High information processing speed.
- Ability to integrate with other systems for advanced data analysis.
So, is it true that DeepSeek is superior to other AIs? Is this claim in accordance with reality, or is it just a marketing strategy? Let's explore further to find the answer.
1. DeepSeek Performance: Speed and Accuracy
When we use AI, two important things are how fast the AI processes and provides answers, and how accurate the answers are. DeepSeek offers quite good performance in terms of speed and accuracy, especially when compared to its more affordable price.
Based on the data, DeepSeek R1 is capable of processing 21 tokens per second. In comparison, OpenAI's o1 model is capable of processing 182 tokens per second, and Gemini 2.0 Flash (a fast version of Gemini) is capable of processing 168 tokens per second.
So, in terms of processing speed, DeepSeek R1 is still below o1 and Gemini 2.0 Flash. However, keep in mind that this speed does not necessarily mean better in everything.
In terms of accuracy, DeepSeek R1 shows impressive results in various tests. For example, in the AIME 2024 math test, DeepSeek R1 scored 79.8%, higher than o1 which scored 72.6%. In the Codeforces coding test, DeepSeek R1 also excels with a score of 96.3, compared to o1 which scored 90.6.
For the GPQA Diamond test, DeepSeek R1 also scored 71.5, while o1 scored 62.1. Likewise, in the MATH-500 test, DeepSeek R1 again excels with a score of 97.2, compared to o1 which only scored 96.4.
In the MMLU test, DeepSeek R1 scored 90.8, slightly below o1 which scored 91.8. Finally, on the SWE-bench Verified test, DeepSeek R1 scored 49.2, better than o1 with a score of 48.9.
2. Language Ability: Multilingual and Context Understanding
DeepSeek is often cited as excelling in multilingualism. To assess this claim, let’s look at data from Artificial Analysis presented in two graphs. The first graph shows the multilingualism index of various AI models across eight languages: English, Spanish, French, German, Swahili, Bengali, Mandarin, and Japanese.
DeepSeek V3 appears to perform consistently well across all of these languages, coming close to matching GPT-4o and Claude 3.5 Sonnet in some languages, as indicated by the different colors on the graph.
The second graph shows the average multilingualism index across languages. DeepSeek V3 scored 86, third only to Claude 3.5 Sonnet (88) and GPT-4o (87). These results show that DeepSeek V3 has competitive multilingualism, though it is not always the best in every language.
3. Coding Skills: Precision and Algorithm Efficiency
When it comes to coding skills, DeepSeek performs very well. Based on data from Artificial Analysis, DeepSeek R1 (in blue) came out on top with a score of 98%, surpassing Claude 3.5 Sonnet (96%), GPT-4o (93%), and Gemini 2.0 Flash (91%) on the HumanEval benchmark.
DeepSeek V3 also showed impressive results with a score of 91%, on par with Gemini 2.0 Flash. This shows that both DeepSeek models are very adept at understanding and generating code.
Data from LiveCodeBench (LCB) shows slightly different coding capabilities. Here, DeepSeek R1 scored a coding average of 66.74, LCB_generation of 79.49, and coding_completion of 54. DeepSeek V3 was slightly below R1 with scores of 61.77, 61.54, and 62 respectively.
However, it is worth noting that DeepSeek R1 still outperforms several other models such as Gemini-Exp-1206 (63.41, 62.82, 64) and GPT-4o (51.44, 44.87, 58).
4. Mathematical Problem-Solving Ability
DeepSeek also shows impressive ability in solving complex mathematical problems. Based on data from Artificial Analysis, DeepSeek R1 (in blue) leads in the MATH-500 benchmark with an outstanding score of 97%.
This score far exceeds other AI models, including Gemini 2.0 Flash (90%), DeepSeek V3 (86%), and GPT-4o mini (79%). This shows that DeepSeek R1 has excellent ability in understanding and solving mathematical problems.
Data from LiveCodeBench (LCB) also shows interesting results. Here, DeepSeek R1 scored a mathematics average of 79.54, AMPS_Hard of 88, math_comp of 88.54, and olympiad of 62.07.
Meanwhile, DeepSeek V3 scored a mathematics average of 60.54, AMPS_Hard of 67, math_comp of 60.42, and olympiad of 54.20. Although not as high as R1, V3 still shows a decent performance.
Of note, other models such as GPT-4o, Claude 3.5 Sonnet, and Claude 3.5 Haiku are not in the LCB table, indicating that their scores are likely below the models listed.
5. Logical Reasoning: Deductive and Structured
Logical reasoning ability is an important aspect of artificial intelligence. According to data from Artificial Analysis, OpenAI’s o1 model leads the MMLU (Massive Multitask Language Understanding) benchmark with a score of 92%.
DeepSeek R1 is in second place with a score of 91%, indicating excellent logical reasoning ability as well. Claude 3.5 Sonnet follows with a score of 89%, followed by Gemini 2.0 Flash and DeepSeek V3 with a score of 87%.
Data from LiveCodeBench (LCB) provides additional perspective. Here, o1 again leads with a reasoning average score of 91.58, web_of_lies_v2 100, zebra_puzzle 88.75, and spatial 86. DeepSeek R1 is in second place with scores of 83.17, 100, 75.50, and 74 respectively.
Meanwhile, DeepSeek V3 scored 56.75, 86, 34.25, and 50 respectively. These results show that although DeepSeek R1 has strong reasoning capabilities, its performance is still below o1 in some aspects.
6. Cost Efficiency: API Price
One of the main attractions of DeepSeek is its cost efficiency, including in terms of API price. Here is a comparison of the API price of DeepSeek R1 and the o1-class model from OpenAI per 1 million tokens in tabular form:
| Kategori | DeepSeek R1 | o1-mini | o1-preview | o1 |
|---|---|---|---|---|
| Input (Cache Hit) | $0.14 | $1.5 | $7.5 | $7.5 |
| Input (Cache Miss) | $0.55 | $3 | $15 | $15 |
| Output | $2.19 | $12 | $60 | $60 |
Description:
Price in US dollars (USD) per 1 million tokens.
- Cache Miss: A situation where the data you requested (input) is not yet available in the AI model's temporary "memory" (cache). Because the data is not yet available, the AI model needs to process the data first before providing a response (output). This takes more time and resources, so it costs more. It's like asking someone about something they don't know yet, so they need to find information first before they can answer.
- Input: Data that you provide to the AI model to be processed. For example, when you ask a question to a chatbot, the question is input.
- Output: Data generated by the AI model in response to the input provided. For example, the answer given by the chatbot to your question is output.
- Cache Hit: A situation where the data you requested (input) is already available in the AI model's temporary "memory" (cache). Because the data is already available, the AI model can provide a response (output) more quickly and efficiently. It’s like asking someone something they already know the answer to, so they can answer it without having to think too much.
7. Accessibility and Flexibility of Use
DeepSeek’s R1 model is open-source. This means that developers can download, modify, and run the model locally without any restrictions. This gives developers great flexibility to customize the model to their specific needs. In contrast, AI models like GPT-4 are proprietary and can only be accessed through a limited API.
DeepSeek also offers an API that is compatible with OpenAI, so developers can easily switch. Plus, DeepSeek doesn’t impose strict rate limits on its API, allowing for large-scale use without any barriers.
8. Limitations of DeepSeek
Despite its many advantages, DeepSeek also has some limitations. This AI is still less mature in general conversation compared to ChatGPT.
In addition, DeepSeek may still have certain biases or censorship due to being bound by regulations in China. User data is also stored on servers located in China. This can raise privacy concerns for some users. DeepSeek also lacks multimodal capabilities such as generating images or sounds, unlike GPT-4o or Gemini.
Technical and Functional Comparison
Note: This comparison is based on various sources and the author's personal experience.
In comparing DeepSeek with other models, there are several technical and functional aspects that need to be evaluated as follows.
1. Language Understanding Accuracy
DeepSeek has an advantage in terms of understanding context and language nuances, while GPT prioritizes creativity in generating text. Claude, on the other hand, focuses more on presenting information safely and ethically.
2. Speed and Efficiency
The DeepSeek algorithm is optimized for data processing speed, making it very suitable for real-time applications. Although GPT also shows excellent performance, the output produced is highly dependent on the prompt and context written by the user. Claude is more focused on security than high speed.
3. Resource Usage
The use of resources in training DeepSeek is relatively more efficient, while models like GPT are known to require intensive computing. Claude is in the middle of the two approaches with an emphasis on energy efficiency.
4. Applications and Integrations
DeepSeek can be easily integrated into various data analytics platforms and chatbot services. Meanwhile, GPT is widely used in creative applications and content publishing, while Claude is often used in industries that require compliance with high ethical standards.
Case Studies and Implementations
To better understand the differences between DeepSeek and other language models, here are some relevant case studies.
1. Implementation of DeepSeek in Customer Service
In general, DeepSeek can help companies reduce the average first response time in customer service. Fast and accurate responses are obtained thanks to deep algorithms that are able to capture the context of customer requests well.
2. Application of GPT in the Creative Industry
Many content and social media platforms utilize GPT to generate creative writing, including in creating blog articles and social media captions. GPT's advantages in flexibility allow for unlimited exploration of ideas.
3. Use of Claude in Education Systems
In the field of education, Claude is often used to provide relevant and safe answers. Claude's approach to avoiding incorrect content helps increase user trust, making it very useful in education and online learning systems.
Conclusion
DeepSeek offers impressive capabilities, especially in terms of technical reasoning, cost-efficiency, and flexibility. However, it also has limitations, such as general conversation and potential privacy concerns.
In a world increasingly dependent on artificial intelligence, the choice between DeepSeek, GPT, Claude, or other language models largely depends on the needs of the application. Here are some key points to summarize.
- DeepSeek offers deep understanding and efficiency in language processing, suitable for real-time applications and complex data analysis.
- GPT is known for its creativity and flexibility, ideal for applications that require innovation and exploration of ideas.
- Claude emphasizes security and ethics, suitable for environments that require high compliance and information accuracy.
By understanding the characteristics of each model, users can determine the most appropriate choice for their needs.
Choosing the right AI depends on your specific needs. If you need AI for technical tasks, coding, math, and cost-efficiency is a priority, DeepSeek is an excellent choice. However, if you are looking for AI for natural, creative conversations and more user-friendly interactions, ChatGPT or Claude may be a better fit.
Ultimately, each AI has its strengths and weaknesses, and DeepSeek is certainly a strong player worth considering...
