DeepSeek vs. Qwen: A Detailed Analysis With Versions

The AI world is in quite the frenzy, as January was an extremely eventful month. DeepSeek made its mark in the world with its revolutionary open-source R1 model, while Alibaba’s Qwen has a new version out called Qwen 2.5 Max.

DeepSeek and Qwen seem to be gunning for the title of “Best AI Model” at this point, given how both releases were days apart. Based on some testing and early reviews, it seems both Qwen 2.5 and DeepSeek V3 are doing very well.

Hence, let’s compare the two AI platforms and see what is the best AI model currently available in the market.

An Overview of DeepSeek and Qwen

Before we start with the comparisons of DeepSeek and Qwen, let’s give a bit of an overview of both AI chatbots.

DeepSeek is a company that is the talk of the town after it released it’s R1 model. Utilizing DeepSeek V3 and R1, their chatbot has ascended to new heights, capable of outperforming any other AI platform in the market.

On the other hand, Qwen has been a mainstay in the AI world for a while, despite not being as well-known as its competitors. Created by Alibaba, Qwen has been steadily improving over the years, with Qwen 2.5 Max reportedly outperforming DeepSeek V3 and OpenAI GPT 4o.

The interesting fact about both DeepSeek and Qwen is that both have come out with new versions in January, within days of each other. So, let’s compare DeepSeek V3 and Qwen 2.5 Max and see which one reigns supreme.

Differences Between DeepSeek and Qwen

Let’s start off with a comparison table of DeepSeek and Qwen in terms of architecture, performance, and pricing.

As the table above shows, both DeepSeek and Qwen have many available models. For DeepSeek, their R1 model has received a lot of acclaim for being adept at handling mathematical and technical tasks. Their latest Janus Pro 7B has shown a lot of promise for multimodal capabilities.

Meanwhile, Qwen has their latest 2.5 Max, which is seemingly outperforming the likes of DeepSeek V3 and Open AI’s 4o. Also, their 2.5 VL 72B Instruct shows a good image generation process.

Architecture

DeepSeek focuses on using Mixture of Elements (MoE) and Reinforcement Learning (RL). Through RL, DeepSeek models are capable of learning about certain topics through content, adapting their knowledge base over time. This means DeepSeek’s responses and solutions improve as they learn more information through user interaction

Qwen, on the other hand, is based on Mixture of Elements, but their data is pre-trained. So, there is no adaptive learning mechanism when it comes to any of Qwen’s models at this moment. However, Qwen does offer processes like fine-tuning to improve certain knowledge gaps the model may have.

Performance in Tasks

In terms of performance, both DeepSeek and Qwen have different strong points. Hence, for different tasks, one model is better than the other.

For Text Generation

DeepSeek generates technical and detailed responses when asked questions. For topics related to mathematics, reports, and such, DeepSeek can provide some insightful content. However, they lack the versatility in terms of topical coverage, as DeepSeek is better at certain categories than others.

Qwen is a more general-purpose model in this regard. Its versatility is very apparent when you start asking the AI different types of questions. Also, their generated content is focused on being creative and more thought-provoking than DeepSeek. While Qwen does provide details in their content, it is still lacking compared to DeepSeek.

For Coding and Technical Tasks

DeepSeek excels at this category, as the R1 model is designed to be excellent for coding and technical tasks. The code they create is efficient and is explained at the end. Also, when posed mathematical questions, DeepSeek resolves them by providing an extensive solution.

Qwen is also no slouch in this area, but it does not exactly perform as well as DeepSeek does. It is capable of generating code in any programming language and also solves technical questions quite well. However, the solutions are not optimized in some instances.

For Image and Video

DeepSeek’s latest Janus Pro 7B shows some promise in this regard. Through this model, DeepSeek will be able to generate images and video with ease. However, it is not available at DeepSeek Chat at the moment, and you can only find it in Hugging Face as an open-source model.

Qwen’s 2.5 Max is really good at this front as it supports multimodal capabilities. With Qwen 2.5 Max, you can generate an image through prompts or providing a document. Furthermore, the Qwen 2.5 VL 72B Instruct allows users to break down images and explain that in text. At this moment, no Qwen model has video generation capabilities.

Scalability

Qwen is more scalable than DeepSeek, and there are two reasons for that. The first of it is that Qwen is designed to handle high volumes of queries for large-scale industries. Through Alibaba Cloud, Qwen can seamlessly be integrated into any business. DeepSeek is more suited for specialized use cases, so smaller deployments suit their AI models.

Secondly, Qwen’s knowledge base is more versatile than DeepSeek’s. Hence, Qwen can handle any types of queries at any given point. Thus, due to a lack of a deep knowledge base and optimization in terms of business, DeepSeek is a little lacking compared to Qwen.

Cost and API Pricing

As for pricing, DeepSeek is cheaper than Qwen in terms of training and API integration. Another advantage with DeepSeek is that it’s the latest models are all open-source, whereas Qwen 2.5 in particular is not open-source.

However, you can use both for free on their chatbot platforms. Hardware efficiency is also higher for DeepSeek, as their models are tailored to not need much computational power. So, DeepSeek is a more cost-efficient model that you can use at lower costs.

Comparing DeepSeek V3 and Qwen 2.5 Max With Examples

So let’s move on to comparing the two main models of DeepSeek and Qwen with some examples for different kinds of queries.

Example 1 (Writing a Story)

To test DeepSeek V3 and Qwen 2.5-Max in terms of  creative storytelling, I asked the query to “Write a story about the changing seasons (200 words)”. Here are the results.

Advantages and Disadvantages of DeepSeek and Qwen

DeepSeek and Gwen have some advantages and disadvantages of their own. Here they are as follows:.

DeepSeek

As the table shows, DeepSeek models are more aligned to solve specialized tasks and improve their knowledge base through Reinforcement Learning (RL). Also, its cost-efficient models allow businesses to implement AI with fewer expenses, getting more value in return.

However, DeepSeek is not diverse enough to handle all queries and requires some technical know-how, especially when implementing their open-source models.

Qwen

Qwen’s ability to provide creative solutions using its diverse information base is a huge boon. Also, their models can handle large amounts of queries, making it more scalable for businesses. Overall, their ability to provide general-purpose answers makes the models more desirable.

However, its versatility makes it lacking in terms of depth of information for many niche topics. Also, the costs to implement Qwen are higher, and it is not always the best at technical queries.

Which Model to Use?

In terms of when to use either DeepSeek or Qwen, it depends on your use cases. Here are the situations when you should use either platform.

When to Use DeepSeek?

DeepSeek is best for:

• Cost-Efficiency: DeepSeek’s open source models and lower API pricing make their products more cost-effective for businesses.
• Technical Solutions: DeepSeek is adept at coding and mathematical queries; hence, it is better suited for these use cases.
• Comprehensive Solution: DeepSeek provides more details in their solutions that help with technical documentation and creating reports.

When to Use Qwen?

Qwen is best for:

• Diverse and Versatile Solutions: Qwen is excellent for answering queries for a huge number of topics.
• Scalability: Qwen is built to serve large-scale industries; hence, their models are more scalable than other products in the market.
• Fine-Tuning: Qwen can be fine-tuned to fill up knowledge gaps for specific use cases.

Key Takeaways

Qwen and DeepSeek are both excellent solutions, especially with their new models (2.5 Max and R1, respectively). Thus, choosing either AI platform based on usage is the best way to go about it.

DeepSeek is a more specialized solution for technical queries and the like, while Qwen is more general-purpose. Also, Qwen is more suited for businesses due to its scalability as opposed to DeepSeek.

Lastly, DeepSeek is more cost-effective due to its lightweight nature. Hence, both models are great to use, provided you know what you need out of an AI chatbot.