Qwen 2.5 Max, a cutting-edge AI model competing with GPT-4o, Claude 3.5 Sonnet, and DeepSeek V3.
Alibaba has just launched Qwen2.5-Max, its most advanced AI model to date. Unlike reasoning models like DeepSeek R1 or OpenAI’s o1, Qwen2.5-Max doesn’t reveal its thought process. Instead, it’s best seen as a general-purpose model, designed to rival GPT-4o, Claude 3.5 Sonnet, and DeepSeek V3.To the
In this blog, I’ll explain what Qwen2.5-Max is, how it was developed, how it stacks up against the competition, and how you can access it.
What is Qwen2.5-Max?
Qwen2.5-Max is Alibaba’s most powerful AI model yet, built to compete with leading models like GPT-4o, Claude 3.5 Sonnet, and DeepSeek V3.
Alibaba, one of China’s largest tech companies, is best known for its e-commerce platforms but has also established a strong presence in cloud computing and artificial intelligence. The Qwen series is part of its broader AI ecosystem, ranging from small, open-weight models to large-scale proprietary systems.
Unlike some earlier Qwen models, Qwen2.5-Max is not open-source, meaning its weights are not publicly accessible.
Trained on 20 trillion tokens, Qwen2.5-Max boasts a vast knowledge base and strong general AI capabilities. However, it’s not a reasoning model like DeepSeek R1 or OpenAI’s o1, meaning it doesn’t explicitly show its thought process. That said, given Alibaba’s ongoing AI expansion, we might see a dedicated reasoning model in the future—perhaps with Qwen 3.
How Does Qwen2.5-Max Work?
Qwen2.5-Max uses a Mixture of Experts (MoE) approach, a technique also employed by DeepSeek V3. This method allows the model to scale efficiently while keeping computational costs manageable. Let’s break it down in simple terms.
Mixture of Experts (MoE) Architecture
Unlike traditional AI models that use all their parameters for every task, MoE models like Qwen2.5-Max and DeepSeek V3 activate only the most relevant parts of the model at any given time.
Think of it as a team of specialists: if you ask a complex physics question, only the physics experts respond, while the rest of the team stays idle. This selective activation allows the model to handle large-scale processing more efficiently without requiring extreme computational power.
This approach makes Qwen2.5-Max both powerful and scalable, enabling it to compete with dense models like GPT-4o and Claude 3.5 Sonnet while being more resource-efficient.
Training and Fine-Tuning
Qwen2.5-Max was trained on 20 trillion tokens, covering a wide range of topics, languages, and contexts.
To put 20 trillion tokens into perspective, that’s about 15 trillion words—a quantity so vast it’s hard to grasp. For comparison, George Orwell’s 1984 contains around 89,000 words, meaning Qwen2.5-Max was trained on the equivalent of 168 million copies of 1984.
However, raw training data alone doesn’t guarantee a high-quality AI model, which is why Alibaba further refined it with:
- Supervised Fine-Tuning (SFT): Human annotators provided high-quality responses to help the model produce more accurate and useful outputs.
- **PureReinforcement Learning from Human Feedback (RLHF): The model was trained to align its responses with human preferences, ensuring more natural and context-aware answers.
Qwen2.5-Max Benchmarks
Qwen2.5-Max has been tested against other leading AI models to measure its capabilities across various tasks. These benchmarks evaluate both instruction-tuned models (fine-tuned for tasks like chat and coding) and base models (the raw foundation before fine-tuning). Understanding this distinction clarifies the real meaning behind the numbers.
Instruction-Tuned Model Benchmarks
Instruction-tuned models are refined for real-world applications, including conversation, coding, and general knowledge tasks. Qwen2.5-Max is compared here to models like GPT-4o, Claude 3.5 Sonnet, Llama 3.1-405B, and DeepSeek V3.
Let’s quickly analyze the results:
- Arena-Hard (Preference Benchmark): Qwen2.5-Max scores 89.4, outperforming DeepSeek V3 (85.5) and Claude 3.5 Sonnet (85.2). This benchmark aligns closely with human preferences in AI-generated responses.
- MMLU-Pro (Knowledge and Reasoning): Qwen2.5-Max scores 76.1, slightly ahead of DeepSeek V3 (75.9) but trailing Claude 3.5 Sonnet (78.0) and GPT-4o (77.0).
- GPQA-Diamond (General Knowledge QA): With a score of 60.1, Qwen2.5-Max edges out DeepSeek V3 (59.1), while Claude 3.5 Sonnet leads at 65.0.
- LiveCodeBench (Coding Capability): At 38.7, Qwen2.5-Max is on par with DeepSeek V3 (37.6) but behind Claude 3.5 Sonnet (38.9).
- LiveBench (Overall Capabilities): Qwen2.5-Max takes the lead with a score of 62.2, surpassing DeepSeek V3 (60.5) and Claude 3.5 Sonnet (60.3), indicating strong real-world AI task performance.
Overall, Qwen2.5-Max proves to be a well-rounded AI model, excelling in preference-based tasks and general AI capabilities while maintaining competitive knowledge and coding skills.
Base Model Benchmarks
Since GPT-4o and Claude 3.5 Sonnet are proprietary models whose base versions aren’t publicly available, the comparison is limited to open-weight models like Qwen2.5-Max, DeepSeek V3, LLaMA 3.1-405B, and Qwen 2.5-72B. This provides a clearer picture of Qwen2.5-Max’s position among leading large-scale open models.
The benchmarks are divided into three categories:
- General Knowledge and Language Understanding (MMLU, MMLU-Pro, BBH, C-Eval, CMMU): Qwen2.5-Max leads all benchmarks here, scoring 87.9 on MMLU and 92.2 on C-Eval, outperforming DeepSeek V3 and Llama 3.1-405B.
- Coding and Problem-Solving (HumanEval, MBPP, CRUX-I, CRUX-O): Qwen2.5-Max also tops these benchmarks, scoring 73.2 on HumanEval and 80.6 on MBPP, slightly ahead of DeepSeek V3 and well ahead of Llama 3.1-405B.
- Mathematical Problem-Solving (GSM8K, MATH): Qwen2.5-Max excels in math, scoring 94.5 on GSM8K, far ahead of DeepSeek V3 (89.3) and Llama 3.1-405B (89.0). However, on MATH, which focuses on more complex problem-solving, it scores 68.5, slightly ahead of competitors but with room for improvement.
How to Access Qwen2.5-Max
Accessing Qwen2.5-Max is straightforward, and you can try it for free without any complicated setup.
Qwen Chat
The quickest way to experiment with Qwen2.5-Max is through Qwen’s Qwen Chat. This is a web interface that lets you interact with the model directly in your browser—much like using ChatGPT.
To use Qwen2.5-Max, simply select it from the model dropdown menu.
API Access via Alibaba Cloud
For developers, Qwen2.5-Max is available through the Alibaba Cloud Model Studio API. To use it, you’ll need to sign up for an Alibaba Cloud account, enable the Model Studio service, and generate an API key.
Since the API follows OpenAI’s format, integration should be seamless if you’re already familiar with OpenAI models. For detailed setup instructions, visit the official Qwen2.5-Max blog.
Conclusion
Qwen2.5-Max is Alibaba’s most powerful AI model to date, designed to compete with leading models like GPT-4o, Claude 3.5 Sonnet, and DeepSeek V3.
Unlike some earlier Qwen models, Qwen2.5-Max isn’t open-source, but you can test it via Qwen Chat or through API access on Alibaba Cloud.
Given Alibaba’s continued investment in AI, it wouldn’t be surprising to see a reasoning-focused model in the future—perhaps with Qwen 3.
