The Rise of Chinese AI Models: Qwen 2.5 Max Features and DeepSeek V3 Comparison.

Hey, Coredge.io community!

The stormy dust that erupted out of the Chinese DeepSeek’s volcanic revolution in the world of AI was yet to be settled; meanwhile, another bomb was exploded by Alibaba with his most advanced AI model, Qwen2.5-Max, which also belongs to China. Let’s dive into the latest buzz in the AI world: Qwen 2.5 Max. This powerhouse from Alibaba Cloud is making waves, though this is not a reasoning model like DeepSeek R1 or OpenAI’s o1, which means the thinking process isn’t there. Rather, it’s regarded as a generalist model and competes with GPT-4o, DeepSeek V3, or Claude 3.5 Sonnet.

In this newsletter, we will understand its standout features, how it stacks up against the competition, and what it means for the future of AI.

What Is Qwen2.5-Max?

Alibaba Cloud's Qwen 2.5 Max is the most formidable latest edition AI model to date, boasting impressive capabilities that are turning heads, and is designed to compete with top-tier models like DeepSeek V3, GPT-4o, and Claude 3.5 Sonnet.

Alibaba, renowned for its e-commerce platforms, is one of China’s largest tech companies. However, it has also left a strong impression in the artificial intelligence and cloud computing domain. The latest arsenal from Alibaba’s Qwen series, Qwen2.5-Max, is a segment of its broader AI ecosystem that ranges from smaller open-weight models to large-scale proprietary systems.

How Does it Work?

Qwen2.5-Max taps a Mixture-of-Experts (MoE) architecture, the same technique exercised by DeepSeek V3, and aims to deliver high performance and scaling up while optimizing computational efficiency.

To understand it easily, let’s break down its key components.

?Mixture-of-Experts (MoE) architecture:

A machine learning technique is known as the mixture-of-experts (MoE) architecture, where an AI model is divided into several sub-networks, or "experts," each majoring in different aspects of the input data. In a traditional AI model, for every task, all parameters are utilized, but at any given time, MoE models like Qwen2.5-Max and DeepSeek V3, activate only the most pertinent parts of the model.

This technique marks Qwen2.5-Max as both mighty and scalable. While being more resource-efficient, it allows competition with intense models like GPT-4o and Claude 3.5 Sonnet. A model in which all parameters are activated for every input is called an intense model.

Training and perfection:

A very deep training was entrusted to Qwen2.5-Max with 20 trillion tokens, which roughly amounts to 15 trillion words, an amount so vast it’s hard to hold, and that covers a massive range of topics, languages, and contexts.

Still, to come out with a high-quality AI model, only raw training data isn’t a guaranteed parameter, so Alibaba further refined it with:

Supervised fine-tuning (SFT): In producing more precise and valuable outputs, human annotators provided high-quality responses to guide the model.

Reinforcement learning from human feedback: With human preferences, the model was trained to align its responses, ensuring answers are more organic and relevant to the context.

Qwen2.5-Max Yardsticks:

To make it outstanding against the competitors, Qwen2.5-Max has been tested and judged against other leading AI models to gauge its abilities across various tasks. Both instruct models (perfected for tasks like chat and coding) and base models (serving as the raw foundation before refinement) are evaluated by these benchmarks. The distinction will help in getting clarity on what the numbers indicate.

Instruct models benchmarks:

For real-world applications, instruct models are honed for real-world applications, including coding, conversation, and general knowledge tasks. Below is the comparison of Qwen2.5-Max with models like GPT-4o, Claude 3.5 Sonnet, LIama 3.1 405B, and DeepSeek V3.

Here is the breakdown of the results:

• Arena-Hard (preference benchmark): Qwen2.5-Max got a score of 89.4, ahead of DeepSeek V3 (85.5) and Claude 3.5 Sonnet (85.2).
• MMLU-Pro: In knowledge and reasoning, Qwen2.5-Max scored 76.1, slightly performing better than DeepSeek V3 (75.9) but also a little behind the groundbreaker Claude 3.5 Sonnet (78.0), and the runner-up GPT-4o (77.0).
• GPQA-Diamond: In general knowledge QA, Qwen2.5-Max scored 60.1, slightly edges out DeepSeek V3 (59.1), while trailblazer Claude 3.5 Sonnet leads at 65.0.
• LiveCodeBench- In coding ability, Qwen2.5-Max scored 38.7, which is approximately at par with DeepSeek V3 (37.6) but lagged against Claude 3.5 Sonnet (38.9).
• LiveBench- In overall capabilities, Qwen2.5-Max is a clear winner with a score of 62.2, beating DeepSeek V3 (60.5) and Claude 3.5 Sonnet (60.3).

Overall, Qwen2.5-Max demonstrates to be a well-versed AI model, while upholding competitive knowledge and coding abilities surpassing in preference-based tasks and general AI capabilities.

Base models benchmark:

Unlike open-weight models, Qwen2.5-Max, DeepSeek V3, LLaMA 3.1-405B, and Qwen 2.5-72B, the comparison is limited to GPT-4o and Claude 3.5, because they are proprietary models with no publicly available base versions. The picture is quite clear that Qwen2.5-Max outshined leading large-scale open models.

If you look closely at the graph above, it’s divided into three sections based on the type of benchmarks being evaluated:

1. General knowledge and language understanding (MMLU, MMLU-Pro, BBH, C-Eval, CMMU): In this category, Qwen2.5-Max outperformed others across all benchmarks, scoring 87.9 on MMLU and 92.2 on C-Eval.
2. Coding and problem-solving (HumanEval, MBPP, CRUX-I, CRUX-O): In coding-related jobs, Qwen2.5-Max also leads in all benchmarks and performs well.
3. Mathematical problem solving (GSM8K, MATH): One of the strongest areas of Qwen2.5-Max is mathematical reasoning; got 94.5 on GSM8K, much ahead of DeepSeek V3 (89.3) and Llama 3.1-405B (89.0).

Conclusion:

And this is it, guys! Whether you're an AI developer, a tech enthusiast, or simply a researcher, Qwen 2.5 Max, an exciting new development in the world of AI, is worth checking out.

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