Who's Leading the LLM Race? A Quick-fire Comparison of OpenAI, DeepSeek, Qwen, and Other Latest Models

If you thought 2024 was the year of ultimate AI innovation, 2025 has already delivered that, and then some AI models breaking benchmarks previously thought impossible. (And we’re not even done with the first quarter yet!)!?

The Large Language Model ecosystem is a battleground of innovation. New models are constantly emerging, pushing the boundaries of natural language understanding and generation. The first wave was stirred by Claude 3.5 Sonnet, and it never stopped after that. From DeepSeek R1 to OpenAI o1 and now Qwen 2.5 is the latest offering.?

“The open-source AI movement is accelerating faster than we expected. Models like Qwen 2.5 and DeepSeek are proving that proprietary dominance isn’t the only path forward.”

— Yann LeCun, Chief AI Scientist at Meta

We embark on the journey to compare and contrast the leading LLMs: OpenAI's frequently-updated models, DeepSeek, Qwen 2.5, and models representing the current top performer.

We'll delve into technical details, analyze benchmark performance, and what makes each one of them a strong contender for complex quantitative tasks.?

Benchmark Comparison of LLM Models

Let’s break it down—how do these top AI models actually stack up against each other??

But first, according to a renowned AI scientist,

“The open-source AI movement is accelerating faster than we expected. Models like Qwen 2.5 and DeepSeek are proving that proprietary dominance isn’t the only path forward.”

— Yann LeCun, Chief AI Scientist at Meta

The following table presents a side-by-side comparison of their capabilities, including mathematics, coding, general knowledge, cost, and scalability.

Architectural and Training Data Comparison

Let's take a closer look at how these models are built and trained. Their underlying architectures and training approaches play a crucial role in shaping their performance, scalability, and real-world applications.

With a lot happening around LLMs, there have been a lot of opinions, forecasts, and discussions too.?

“According to a 2025 Stanford AI Index report, open-source models like Qwen 2.5 are gaining traction due to their cost-efficiency and adaptability.”

A former OpenAI researcher, Andrej Karpathy recently noted,

“Mixture-of-Experts architectures like DeepSeek’s R1 offer efficiency gains that will shape the future of AI scalability.”

Parting Thoughts

The LLM race is no longer just about who has the biggest model—it’s about efficiency, cost-effectiveness, and real-world applications. While OpenAI continues to push proprietary frontiers, DeepSeek and Qwen are making waves with open-source innovation.?

As AI capabilities evolve, the question is no longer just ‘Which model is the best,’ but rather, ‘Which model is the best for your needs?’ The future of AI won’t be dominated by a single winner—it will be shaped by diverse solutions catering to different users.?

As the experts put it,?

“The future of LLMs isn’t just about scaling up parameters—it’s about efficiency, domain specialization, and real-world usability.”

— Geoffrey Hinton, AI Pioneer & Deep Learning Researcher

Stay tuned and follow arbisoft, because the next breakthrough might just be around the corner.