Qwen-3.5 PLUS Outperforms MoE 397B-A17B

2026-02-17 · Source: Discover AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Emerging Technologies & Innovation · Depth: Intermediate, medium

Summary

Alibaba has released Qwen 3.5, a new large language model available in both a publicly downloadable version (Qwen 3.5) and a closed, hosted "Plus" version (Qwen 3.5 Plus) via Alibaba Cloud Model Studio. The Plus version features extended context length up to 1 million tokens and tool use capabilities. Initial live testing on a complex causal reasoning task, specifically an elevator puzzle requiring optimal button presses, showed Qwen 3.5 Plus initially produced a 16-step solution, then optimized to an 11-step, and finally a 9-step (8 presses + 1 exit) solution which was validated as correct and optimal. However, an attempt to further optimize this solution resulted in an invalid sequence. The standard Qwen 3.5 model struggled, initially proposing a 10-button press solution that it could not self-validate, and subsequent attempts yielded longer, invalid sequences.

Key takeaway

For AI Engineers evaluating new LLMs for complex logical reasoning, you should prioritize models that can not only generate solutions but also robustly self-validate them. Qwen 3.5 Plus shows promise for its reasoning capabilities, but your workflows must include explicit validation steps, especially when attempting to optimize solutions, as even "optimal" suggestions can be invalid. Consider the hosted "Plus" versions for enhanced features like longer context windows.

Key insights

Qwen 3.5 Plus demonstrates strong causal reasoning but can generate invalid optimizations; the base Qwen 3.5 struggles with complex validation.

Principles

• Model validation is crucial for complex reasoning tasks.
• Optimization attempts can lead to invalid solutions.
• Hosted models may offer enhanced capabilities over public versions.

Method

The testing methodology involved live, interactive problem-solving with an LLM, followed by explicit self-validation requests to confirm solution correctness and optimality, and iterative optimization attempts.

In practice

• Always validate LLM-generated solutions for critical tasks.
• Test both base and "plus" versions of models for performance differences.
• Be wary of LLM-suggested optimizations without re-validation.

Topics

• Qwen 3.5 Plus
• Qwen 3.5
• Mixture of Experts
• Causal Reasoning
• Model Evaluation

Best for: AI Engineer, Machine Learning Engineer, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Discover AI.