How to Access Qwen2.5-Max?

Have you been keeping tabs on the latest breakthroughs in Large Language Models (LLMs)? If so, you’ve probably heard of DeepSeek V3—one of the more recent MoE (Mixture-of-Expert) behemoths to hit the stage. Well, guess what? A strong contender has arrived, and it’s called Qwen2.5-Max. Today, we’ll see how this new MoE model has been built, what sets it apart from the competition, and why it just might be the rival that DeepSeek V3 has been waiting for.

Qwen2.5-Max: A New Chapter in Model Scaling

It’s widely recognized that scaling up both data size and model size can unlock higher levels of “intelligence” in LLMs. Yet, the journey of scaling to immense levels—especially with MoE models—remains an ongoing learning process for the broader research and industry community. The field has only recently begun to understand many of the nitty-gritty details behind these gargantuan models, thanks in part to the unveiling of DeepSeek V3.

But the race doesn’t stop there. Qwen2.5-Max is hot on its heels with a huge training dataset—over 20 trillion tokens—and refined post-training steps that include Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). By applying these advanced methods, Qwen2.5-Max aims to push the boundaries of model performance and reliability.

What’s New with Qwen2.5-Max?

1. MoE Architecture:
   Qwen2.5-Max taps into a large-scale Mixture-of-Expert approach. This allows different “expert” submodels within the larger model to handle specific tasks more effectively, potentially leading to more robust and specialized responses.
2. Massive Pretraining:
   With a enormous dataset of 20 trillion tokens, Qwen2.5-Max has seen enough text to develop nuanced language understanding across a wide range of domains.
3. Post-Training Techniques:
   • Supervised Fine-Tuning (SFT): Trains the model on carefully curated examples to prime it for tasks like Q&A, summarization, and more.
   • Reinforcement Learning from Human Feedback (RLHF): Hones the model’s responses by rewarding outputs that users find helpful or relevant, making its answers more aligned with real-world human preferences.

Performance at a Glance

Performance metrics aren’t just vanity numbers—they’re a preview of how a model will behave in actual usage. Qwen2.5-Max was tested on several demanding benchmarks:

• MMLU-Pro: College-level knowledge probing.
• LiveCodeBench: Focuses on coding abilities.
• LiveBench: A comprehensive benchmark of general capabilities.
• Arena-Hard: A challenge designed to approximate real human preferences.

Qwen2.5-Max Outperforming DeepSeek V3

Qwen2.5-Max consistently outperforms DeepSeek V3 on multiple benchmarks:

• Arena-Hard: Demonstrates stronger alignment with human preferences.
• LiveBench: Shows broad general capabilities.
• LiveCodeBench: Impresses with more reliable coding solutions.
• GPQA-Diamond: Exhibits adeptness at general problem-solving.

It also holds its own on MMLU-Pro, a particularly tough test of academic prowess, placing it among the top contenders

Here’s the comparison:

1. Which Models Are Compared?
   • Qwen2.5‐Max
   • DeepSeek‐V3
   • Llama‐3.1‐405B‐Inst
   • GPT‐4o‐0806
   • Claude‐3.5‐Sonnet‐1022
2. What Do the Benchmarks Measure?
   • Arena‐Hard, MMLU‐Pro, GPQA‐Diamond: Mostly broad knowledge or question‐answering tasks—some mix of reasoning, factual knowledge, etc.
   • LiveCodeBench: Measures coding capabilities (e.g., programming tasks).
   • LiveBench: A more general performance test that evaluates diverse tasks.
3. Highlights of Each Benchmark
   • Arena‐Hard: Qwen2.5‐Max tops the chart at around 89%.
   • MMLU‐Pro: Claude‐3.5 leads by a small margin (78%), with everyone else close behind.
   • GPQA‐Diamond: Llama‐3.1 hits the highest (65%), while Qwen2.5‐Max and DeepSeek‐V3 hover around 59–60%.
   • LiveCodeBench: Claude‐3.5 and Qwen2.5‐Max are nearly tied (about 39%), indicating strong coding performance.
   • LiveBench: Qwen2.5‐Max leads again (62%), closely followed by DeepSeek‐V3 and Llama‐3.1 (both ~60%).
4. Main Takeaway
   • No single model wins at everything. Different benchmarks highlight different strengths.
   • Qwen2.5‐Max looks consistently good overall.
   • Claude‐3.5 leads for some knowledge and coding tasks.
   • Llama‐3.1 excels at the GPQA‐Diamond QA challenge.
   • DeepSeek‐V3 and GPT‐4o‐0806 perform decently but sit a bit lower on most tests compared to the others.

In short, if you look at this chart to pick a “best” model, you’ll see it really depends on what type of tasks you care about most (hard knowledge vs. coding vs. QA).

Face-Off: Qwen2.6-Max vs. DeepSeek V3 vs. Llama-3.1-405B vs. Qwen2.5-72B 

When it comes to evaluating base (pre-instruction) models, Qwen2.5-Max goes head-to-head with some big names:

• DeepSeek V3 (leading open-weight MoE).
• Llama-3.1-405B (massive open-weight dense model).
• Qwen2.5-72B (another strong open-weight dense model under the Qwen family).

In these comparisons, Qwen2.5-Max shows significant advantages across most benchmarks, proving that its foundation is solid before any instruct tuning even takes place.

How to Access Qwen2.5-Max?

Curious to try out Qwen2.5-Max for yourself? There are two convenient ways to get hands-on:

Qwen Chat

You can start interacting with Qwen Chat using this link. Experience Qwen2.5-Max interactively—ask questions, play with artifacts, or even brainstorm in real time.

API Access via Alibaba Cloud

Developers can call the Qwen2.5-Max API (model name: qwen-max-2025-01-25) by following these steps:

1. Register for an Alibaba Cloud account.
2. Activate the Alibaba Cloud Model Studio service.
3. Create an API key from the console.

Since Qwen’s APIs are compatible with OpenAI’s API format, you can plug into existing OpenAI-based workflows. Here’s a quick Python snippet to get you started:

!pip install openai
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)

Output

To determine which number is larger between 9.11 and 9.8 , let's compare them
 step by step:

Step 1: Compare the whole number parts

Both numbers have the same whole number part, which is 9 . So we move to the
 decimal parts for further comparison.

Step 2: Compare the decimal parts

The decimal part of 9.11 is 0.11 .

The decimal part of 9.8 is 0.8 (equivalent to 0.80 when written with two
 decimal places for easier comparison).

Now compare 0.11 and 0.80 :

0.80 is clearly larger than 0.11 because 80 > 11 in the hundredths place.

Conclusion

Since the decimal part of 9.8 is larger than that of 9.11 , the number 9.8 is
 larger.

Final Answer:

9.8

Looking Ahead

Scaling data and model size is far more than a race for bigger numbers. Each leap in size brings new levels of sophistication and reasoning power. Moving forward, the Qwen team aims to push the boundaries even further by leveraging scaled reinforcement learning to hone model cognition and reasoning. The dream? To uncover capabilities that could rival—or even surpass—human intelligence in certain domains, paving the way for new frontiers in AI research and practical applications.

Conclusion

Qwen2.5-Max isn’t just another large language model. It’s an ambitious project geared toward outshining incumbents like DeepSeek V3, forging breakthroughs in everything from coding tasks to knowledge queries. With its massive training corpus, novel MoE architecture, and smart post-training methods, Qwen2.5-Max has already shown it can stand toe-to-toe with some of the best.

Ready for a test drive? Head over to Qwen Chat or grab the API from Alibaba Cloud and start exploring what Qwen2.5-Max can do. Who knows—maybe this friendly rival to DeepSeek V3 will end up being your favourite new partner in innovation.

Pankaj Singh

Hi, I am Pankaj Singh Negi - Senior Content Editor | Passionate about storytelling and crafting compelling narratives that transform ideas into impactful content. I love reading about technology revolutionizing our lifestyle.