The world of generative AI (GenAI) has evolved immensely in the last two years and its impact can be seen across the globe. While the U.S. has led the charge with large language models (LLMs) like GPT-4o, Gemini, and Claude, France made it big with Mistral AI. But the GenAI landscape that was majorly ruled by the US and Europe, now has new contenders. Recently, Chinese companies Baidu and Alibaba took center stage, unveiling their respective champions – DeepSeek and Qwen. These models not only challenge the dominance of American tech giants but also signal a bold shift in the global GenAI narrative. So, what happens when two of China’s biggest tech titans step into the global arena of generative AI? Let’s find out, as we compare DeepSeek’s latest model – V3 vs Qwen2.5, diving deep into their features, strengths, and performance.
Table of Contents
What is DeepSeek-V3?
DeepSeek-V3, developed by Baidu, is an open-source LLM with 671 billion parameters. The model has been trained on 14.8 trillion high-quality tokens and designed for research and commercial use with deployment flexibility. It excels in mathematics, coding, reasoning, and multilingual tasks, and supports a context length of up to 128K tokens for long-form inputs.
The very first DeepSeek model came out in 2023 and since then there has been no stopping. The latest V3 model has been shown to beat giants like GPT-4o and Llama 3.1 across various parameters.
Learn More: Andrej Karpathy Praises DeepSeek V3’s Frontier LLM, Trained on a $6M Budget
How to Access DeepSeek-V3?
- Head to: https://www.deepseek.com/.
- Sign up and click on Start Now.
- Get started.
What is Qwen2.5?
Qwen2.5, developed by Alibaba Cloud, is a dense, decoder-only LLM available in multiple sizes ranging from 0.5B to 72B parameters. It is optimized for instruction-following, structured outputs (e.g., JSON, tables), and coding and mathematical problem-solving. It supports more than 29 languages and a context length of up to 128K tokens, making it versatile for multilingual and domain-specific applications.
Qwen models were only available through platforms like Hugging Face and Github. But last week the company launched its web interface to allow users to test its various models.
How to Access Qwen2.5?
- Head to: https://chat.qwenlm.ai/.
- Sign in to create your account.
- Get started.
DeepSeek-V3 Vs Qwen2.5
I will compare the two Chinese LLMs on 5 tasks involving reasoning, image analysis, document analysis, content creation, and finally coding. We will then review the results and find out the winner.
Reasoning
Prompt: “Your team processes customer requests through three stages:
Data Collection (Stage A): Takes 5 minutes per request.
Processing (Stage B): Takes 10 minutes per request.
Validation (Stage C): Takes 8 minutes per request.
The team currently operates sequentially, but you are considering parallel workflows.
If you assign 2 people to each stage and allow parallel workflows, the output per hour increases by 20%. However, adding parallel workflows costs 15% more in operational overhead. Should you implement parallel workflows to optimize efficiency, considering both time and cost?”
Output:
Response by DeepSeek-V3:
Response by Qwen2.5:
Observations:
| Model | DeepSeek-V3 | Qwen2.5 |
| Response | I found the output to be stronger due to its clarity, concise calculations, and structured explanation. It provides accurate results and actionable insights without complicating the problem statement. | I found that this LLM showed deeper reasoning and correctly identified the potential discrepancies, which was great. However, the response was verbose and slightly over-detailed, which diluted the overall impact. |
Both the models gave the same result and it was the right answer. So when it comes to accuracy – both Qwen2.5 and DeepSeek-V3 hit it out of the park! In fact both the models took equal amounts of time to think through the problem and provide a detailed explanation of the proposed solution with proper calculations. But Deepseek’s crisp explanation stood out for me.
Verdict: DeepSeek-V3: 1 | Qwen2.5: 0
Image Analysis
Prompt: “Which team won and by what margin? When is the winning team’s next match?”
Output:
Response by DeepSeek-V3:
Response by QVQ-72B- Preview:
Observations:
| Model | DeepSeek-V3 | QVQ-72B-Preview |
| Response | This model was not able to analyze the image and hence did not generate any useful response. | This model analyzed the image properly and read the result accurately. Further, it also searched and gave the correct information regarding the winning team’s next match! |
Deepseek V3 is currently only capable of reading the text from the image but for this image, it was unable to do so. Qwen2.5 model is also currently incapable of analyzing images but Qwen chat allows you to choose from the list of various other LLMs, which can do image analysis. So for this task, from the top left side of the screen, I chose – QVQ-72B-Preview and got great results.
Verdict: DeepSeek V3: 1 | Qwen2.5 (QVQ-72B-Preview (Qwenchat)): 1
Document Analysis
Prompt: “Give me 2 main insights from this document and a brief summary of the entire document.”
Output:
Response by DeepSeek-V3:
Response by Qwen2.5:
Observations:
| Model | DeepSeek-V3 | Qwen2.5 |
| Response | I found the response of the model to be concise and clear. Its summary, although crisp, missed a few points that could have provided better insights into the agentic program being discussed in the document. | I found the response to be detailed, capturing the right nuances of the document. Its summary had all the key features from the document providing detailed insights into the agentic program. |
Both the models did a great job going through the document. The two key points that the models stated were quite similar. While the DeepSeek model is not yet able to read through files over 100 MB or lengthy documents, Qwen2.5 takes some time while processing documents. However, the results for both models are quite good but Qwen 2.5 had a slight edge over DeepSeek with all its details.
Verdict: DeepSeek V3: 1 | Qwen2.5: 2
Content Creation
Prompt: “ I’m launching a new wellness brand – “Mind on Top” that provides healing support for mental support to over thinkers. Create a business pitch for my brand. Make it concise and engaging – ensuring that the investors would invest in my business.”
Output:
Response by DeepSeek-V3:
Response by Qwen2.5:
Observations:
| Model | DeepSeek-V3 | Qwen2.5 |
| Response | I found that its response is for investors who value concise, data-driven pitches. It’s straightforward, highlights traction, and clearly outlines the investment ask, which makes it ideal for those preferring a crisp data-backed pitch. | I found its output to be suitable for investors who appreciate a more narrative-driven approach. It provides greater depth and emotional engagement, which could resonate well with those invested in the mission of mental wellness. |
The results of both the models were pretty good. Each response had some stand-out factors but some shortcomings too. I wanted a pitch that is data-backed and has a story behind it with a growth plan and an investment strategy so that it captures the interest of the investors. I found bits and pieces of my ask in the two responses but it was Deepseek V3 that covered most of these points.
Verdict: DeepSeek V3: 2 | Qwen2.5: 2
Coding
Prompt: “Write the code to build a simple mobile-friendly word completion app for kids between the age group 10-15”
Output:
Response by DeepSeek-V3:
Response by Qwen2.5:
Observations:
| Model | DeepSeek-V3 | Qwen2.5 |
| Response | I found its response to be well structured with a clear explanation. It comes with dynamic features and provides engagement features for my key audience. However, the code itself seems a bit advanced and would require developer support. | I found its output to be simple which is ideal for beginners. It’s simple to understand but lacks the advanced capabilities or features that would make the app engaging for my core audience. |
Both LLMs gave good results and each one had its pros and cons. The app should be simple, and sophisticated and yet have the space to include enhancements in the future. Since the app is for kids, it should also have some fun elements to keep them engaged. Finally, along with the code I would want the explanation to help me understand the code. I found most of these points being covered in the response generated by DeepSeek V3.
Verdict: DeepSeek V3: 3 | Qwen 2.5: 2
DeepSeek-V3 or Qwen2.5: Which One is Better?
The overall result shows DeepSeek V3 leading with a score of 3, while Qwen2.5 follows closely with a score of 2. Here’s a detailed comparison of both models to provide further insights into their performance and capabilities:
| Feature | DeepSeek | Qwen |
| Image Generation | Doesn’t allow image generation. | Doesn’t allow image generation. |
| Image Analysis | Only text parsing is available for image analysis. | Can analyze both visual and textual parts of an image using suitable model. |
| Features | Has Deep Think and Search Options, but they don’t work in cohesion. | Offers Web Search, Image Generation, and other artifacts, though not fully live. |
| Prompt Editing | You can’t edit the prompt once submitted. | You can edit the prompt after submission. |
| Model Choice | Supports only one model | Allows working with multiple models simultaneously. |
| Model Strength | Focuses heavily on reasoning and detailed analysis. | Excels in modularity, with task-specific models for diverse applications. |
| Target Audience | Best suited for academic and research-oriented tasks. | Designed for developers, businesses, and dynamic workflows needing modular flexibility. |
Also Read: DeepSeek V3 vs GPT-4o: Can Open-Source AI Compete with GPT-4o’s Power?
Conclusion
Both DeepSeek-V3 and Qwen2.5 are quite promising and show immense potential. A lot of features of the two models are still in the works, which could add more value to them. Yet, even at present, both models are quite at par with each other. With each task, both the models took their time to generate the response, ensuring the responses were well thought through. While DeepSeek-V3 captures the eye with its crisp responses, Qwen2.5 impresses with its depth and detail. Needless to say, these two Chinese models are set to give giants like ChatGPT, Gemini and Claude, a run for their money!
Frequently Asked Questions
Q1. What is DeepSeek-V3?
A. DeepSeek-V3 is an open-source large language model (LLM) developed by Baidu. It is designed to handle tasks like reasoning, coding, mathematics, and multilingual inputs.
Q2. What is Qwen2.5?
A. Qwen2.5, developed by Alibaba Cloud, is a dense, decoder-only LLM. It excels in multilingual tasks, coding, mathematical problem-solving, and producing structured outputs like JSON and tables.
Q3. Which model performs better in reasoning tasks?
A. Both DeepSeek-V3 and Qwen2.5 performed well on reasoning tasks, providing accurate and detailed walkthroughs. However, DeepSeek-V3 edges out slightly due to its concise calculations and structured explanations, which were easier to interpret.
Q4. Which of the two models can generate images?
A. Currently neither DeepSeek-V3 nor Qwen2.5 can generate images.
Q5. What is the strength of the Qwen2.5 model?
A. Qwen2.5 is strong in modularity, allowing for task-specific applications across various domains.
Q6. What is the strength of the DeepSeek-V3 model?
A. DeepSeek-V3 excels in reasoning and detailed analysis, making it suitable for research and academic tasks.
Anu Madan has 5+ years of experience in content creation and management. Having worked as a content creator, reviewer, and manager, she has created several courses and blogs. Currently, she working on creating and strategizing the content curation and design around Generative AI and other upcoming technology.
