Introduction
The artificial intelligence landscape is evolving rapidly, with researchers and industry experts striving to push the boundaries of model intelligence by scaling both data and model sizes. While it is well established that increasing the scale of language models enhances their capabilities, there has been limited experience in effectively scaling extremely large models—whether dense or Mixture-of-Experts (MoE) architectures. With the recent release of DeepSeek V3, some insights into large-scale model scaling have been unveiled, and concurrently, the Qwen team has been developing its own breakthrough model: Qwen2.5-Max.
Qwen2.5-Max is a state-of-the-art MoE model, trained on an extensive dataset of over 20 trillion tokens. It has undergone further enhancements through Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). Today, we are excited to share the performance benchmarks of Qwen2.5-Max and announce its availability via Alibaba Cloud’s API. Users can now interact with the model directly through Qwen Chat and explore its cutting-edge capabilities firsthand.
Benchmarking Qwen2.5-Max: How It Stacks Up Against the Best
To gauge the effectiveness of Qwen2.5-Max, we compared it with both proprietary and open-weight models using a diverse set of benchmarks that assess general intelligence, coding proficiency, and human preference alignment.
The benchmarks used in this evaluation include:
MMLU-Pro – Tests knowledge through college-level questions.
LiveCodeBench – Evaluates coding capabilities.
LiveBench – Measures general AI capabilities.
Arena-Hard – Assesses AI models based on human preference approximations.
Instruct Model Performance
When evaluating instruct models (those optimized for downstream applications like chat and coding), Qwen2.5-Max was tested alongside DeepSeek V3, GPT-4o, and Claude-3.5-Sonnet. The results showed that Qwen2.5-Max outperforms DeepSeek V3 across multiple benchmarks, including Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond. Additionally, the model delivered highly competitive results in other categories, such as MMLU-Pro.
Base Model Performance
For base models (raw pretrained models before fine-tuning), proprietary models like GPT-4o and Claude-3.5-Sonnet were inaccessible for direct comparison. Instead, Qwen2.5-Max was benchmarked against leading open-weight models, including:
DeepSeek V3 – A top open-weight MoE model.
Llama-3.1-405B – The largest open-weight dense model.
Qwen2.5-72B – A highly capable open-weight dense model from the Qwen series.
Qwen2.5-Max demonstrated significant advantages across most benchmarks, reinforcing its position as one of the most capable large-scale AI models available. With ongoing improvements in post-training methodologies, future versions of Qwen2.5-Max are expected to further surpass current standards.
How to Use Qwen2.5-Max
Qwen2.5-Max is now publicly accessible via Qwen Chat, allowing users to engage with the model in real-time for various applications, including chat, coding, and information retrieval. Additionally, developers can integrate Qwen2.5-Max into their applications using its API on Alibaba Cloud.
API Access
To use the Qwen2.5-Max API:
Register an Alibaba Cloud account.
Activate Alibaba Cloud Model Studio.
Generate an API key via the Alibaba Cloud console.
Since Qwen APIs are OpenAI-API compatible, users can seamlessly integrate Qwen2.5-Max into existing workflows. Below is a simple example of using Qwen2.5-Max in 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 helpful assistant.'},
{'role': 'user', 'content': 'Which number is larger, 9.11 or 9.8?'}
]
)
print(completion.choices[0].message)
This simple implementation allows developers to interact with the model programmatically, making it a powerful tool for building AI-driven applications.
Future Prospects: Scaling Intelligence Beyond Human Capabilities
The advancements achieved through Qwen2.5-Max represent only the beginning of what’s possible with large-scale AI model development. The continuous scaling of data and model size has already demonstrated significant improvements in intelligence, but the Qwen team is committed to further refining these models.
One of the primary areas of future exploration is enhancing the model’s thinking and reasoning capabilities. By leveraging innovative scaled reinforcement learning techniques, researchers aim to develop AI systems that can surpass human intelligence in complex reasoning tasks. This next-generation AI could pave the way for new breakthroughs in scientific research, problem-solving, and knowledge discovery.
Conclusion
Qwen2.5-Max marks a significant step forward in the evolution of large-scale AI models. With its superior performance across key benchmarks, robust fine-tuning methodologies, and OpenAI-compatible API, it is set to become a cornerstone for AI-driven applications in research, development, and enterprise solutions.
As we continue to push the frontiers of AI intelligence, Qwen2.5-Max serves as a testament to the potential of scaling large MoE models to new heights. Stay tuned for further developments as we refine our approach and explore the uncharted territories of AI-driven intelligence.