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Chinese AI models are really giving its American competitors a hard time. In less than a week, two models were released: the DeepSeek-V3 and now Qwen2.5 Max, which have shown some promising results compared to top-performing models like the GPT-4. The Qwen2.5 Max is another ultra-large language model that was independently developed and released by Alibaba Cloud on the Chinese New Year, and it is making some serious noise in the tech market.

What is Qwen2.5 Max?
It is a Mixture of Experts (MoE) model that was pretrained on more than 20 trillion tokens and then further refined using Supervised Fine Tuning (SFT) and Reinforcement Learning for Human Feedback (RLHF). It supports inputs in multiple languages including English and Chinese with an input limit of 6000 tokens. It also supports a chat context of up to 8000 tokens. The Qwen2.5 Max is updated in rolling mode, which basically means that any update is continuously and incrementally applied rather than being released in large, distinct versions.
Is it really that good?
Not only is it completely free, but according to stats, it even outperforms DeepSeek V3 and GPT-4.0 in live benchmarking. Let’s talk about that in more detail. Before we even get to the actual figures, you can clearly see Qwen2.5 as the tallest bar for almost every benchmark below.

The Qwen 2.5 Max model demonstrates outstanding performance across multiple benchmarks, consistently surpassing or matching its competitors.
We evaluate Qwen2.5-Max alongside leading models, whether proprietary or open-weight, across a range of benchmarks that are of significant interest to the community. These include MMLU-Pro, which tests knowledge through college-level problems, LiveCodeBench, which assesses coding capabilities, LiveBench, which comprehensively tests the general capabilities, and
- For Arena-Hard, Qwen 2.5 Max achieves the highest score of 89.4, outperforming DeepSeek-V3 and GPT-4. This highlights its superior problem-solving capabilities in challenging tasks according to human preferences.
- MMLU-Pro tests the knowledge of models through college-level problems. Here, Qwen 2.5 Max leads with 76.1, slightly ahead of DeepSeek-V3 and GPT-4 showcasing its robustness in multi-tasking and comprehensive understanding.
- In GPQA-Diamond benchmarking, with a score of 60.1, Qwen 2.5 Max performs on par with DeepSeek-V3 (59.1) but falls slightly behind GPT-4-0806 (65.0). This suggests that it slightly lacks in demonstrating a strong general question-answering capability.
- For LiveCodeBench: Qwen 2.5 Max achieves 38.7, narrowly surpassing DeepSeek-V3 (37.6) and Llama-3.1 (35.1) while outperforming GPT-4-0806 (30.2) by a significant margin. This proves its capabilities in real-time code-related tasks.
- The LiveBench benchmark comprehensively tests the general capabilities of models. Here Qwen 2.5 Max stands out with 62.2, maintaining a slim edge over DeepSeek-V3 (60.5) and Llama-3.1 (60.3).
If you want to learn about popular benchmarks like MMLU and HumanEval, check out these comprehensive guides to help you understand how models are evaluated and ranked:
Using Qwen2.5 Max
There are two main ways you can use the Qwen2.5 Max model. If you are creating an application with generative capabilities, you can incorporate this model using its API, but if you just want to use it as an assistant or helper with your work, you can just use it in your browser. We will discuss both these methods.
API
To use Qwen2.5 Max in your application, you can access it using its API. Another great thing about Qwen is that its APIs are OpenAI-API compatible, so if you have already worked with them, you just need to make some minor adjustments.
Here is a simple example of accessing Qwen2.5 Max using Python:
from openai import OpenAI
import os
client = OpenAI(
api_key=os.getenv("API_KEY"),
base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
model="qwen-max-2025-01-25",
messages=[
{'role': 'system', 'content': 'You are a knowledgeable AI tutor.'},
{'role': 'user', 'content': 'Can you explain the theory of relativity in simple terms?'}
]
)
print(completion.choices[0].message)
This code first initializes an OpenAI client with an API key stored in environment variables and specifies a base URL for request handling. It then sends a chat request containing two messages:
- A system message that defines the AI as a “knowledgeable AI tutor”, and
- A user message asking for a simple explanation of the theory of relativity.
The model processes the request and generates a response, which is then extracted and printed. This setup allows for dynamic AI-powered conversations, making it useful for educational or informational applications.
For more information, check out the official documentation.
Demo
If you want to try out Qwen2.5 Max, visit the Qwen Chat page and select the model you want to use. You need to be logged in to use the chat feature so make sure to Sign up first.

I tried generating an image using the Qwen2.5 Max model, and I think it did a pretty amazing job. Here is the result below:

What you say? Looks great doesn’t it? You should also check it out.
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