Quen 2.5 Max: A Contender or Just Another LLM?

The field of large language models (LLMs) is constantly evolving, with new models emerging regularly, each claiming to push the boundaries of what's possible. Quen has recently launched its new model, the Quen 2.5 Max, positioning it as a competitor to DeepSeq V3. The model is described as a large MOE LLM pre-trained on massive datasets and post-trained with curated SFT and RLHF recipes.

Claims and Benchmarks

Quen asserts that the 2.5 Max achieves competitive performance against top-tier models, even outperforming DeepSeq V3 in specific benchmarks like ArenaHard, LiveBench, LiveCodeBench, and GPQA Diamond. However, these claims should be examined critically. It's important to consider which benchmarks are used and whether they accurately reflect real-world performance.

Accessibility and Open Source

A significant drawback of the Quen 2.5 Max is that it's not open source. Access is limited to their API or chat interface. This restriction prevents independent evaluation, modification, and community-driven improvement.

Testing and Performance

The model's performance can be assessed through various tests. Here are some examples and the results:

• General Knowledge: The model correctly identified Israel as a country whose name ends with "Leah" and provided Jerusalem as its capital.
• Riddles: It successfully answered the riddle "What is the number that rhymes with the word we use to describe a tall plant?" with the correct answer, "3".
• Creative Tasks: The model failed to write a haiku where the second letter of each word spelled "simple". It also failed to provide the correct english adjective of latin origin.
• Math Problems: Successfully answered a word problem involving percentage calculation.
• Logic Puzzles: Successfully answered several logic puzzles testing reasoning skills.
• Code Generation: Demonstrated proficiency in generating HTML, CSS, and JavaScript code for tasks such as creating a confetti button, a synth keyboard, a 3D animation, and generating SVG code. Also, a game of life in Python that works on the terminal was created successfully.

Comparison to DeepSeq V3

Despite the claims, the Quen 2.5 Max may not be on par with DeepSeq V3. While it performs well in certain areas, its code quality and overall robustness might fall short. DeepSeq V3's success is attributed to its reinforcement learning, which resulted in high-quality datasets.

Conclusion

The Quen 2.5 Max presents itself as a potentially strong contender in the LLM landscape. However, its closed-source nature and questions about its true performance compared to models like DeepSeq V3 raise concerns. It may find a niche for trivial tasks via the Quen chat interface. Is the lack of open access a fundamental limitation, or can Quen 2.5 Max still offer value through its API?

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