Can A Medium Qwen 3.5 Reason? Flash, 27B, 9B TEST

2026-03-03 · Source: Discover AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, long

Summary

This analysis evaluates the causal reasoning capabilities of smaller Qwen 3.5 models, specifically the Flash, 27B, and 9B versions, using a complex "elevator puzzle" benchmark. The Qwen 3.5 Flash model achieved a solution in 10 actions (9 button presses plus an exit), demonstrating a good but not optimal performance. The Qwen 3.5 27B model struggled more, initially providing an invalid 17-press solution and later an 18-press solution that was also suboptimal and close to the 20-press limit. Both models initially failed validation, requiring recalculation. The Qwen 3.5 9B model consistently crashed during testing and could not produce any results for this complex task. The evaluation highlights a significant degradation in strategic planning and optimization capabilities as model size decreases, suggesting smaller models are less suitable for intricate logical reasoning.

Key takeaway

For AI Engineers evaluating Qwen 3.5 models for deployment, you should carefully consider the complexity of your reasoning tasks. While Flash and 27B models might handle simpler logic, their performance on intricate causal reasoning is significantly diminished compared to larger models. Avoid using the 9B model for complex tasks, as it failed to produce any valid results. Prioritize larger models for applications requiring strategic planning or optimization to ensure reliable and accurate outcomes.

Key insights

Smaller Qwen 3.5 models exhibit reduced causal reasoning and strategic planning capabilities on complex tasks.

Principles

• Model size directly impacts complex reasoning ability.
• Smaller models often resort to trial-and-error.
• Validation is crucial for LLM-generated solutions.

Method

A custom "elevator puzzle" with mathematical functions, energy constraints, and logical dependencies was used to test causal reasoning without code conversion or mathematical optimization.

In practice

• Restrict smaller LLMs to non-complex reasoning tasks.
• Use larger models for scientific, medical, or financial reasoning.
• Implement validation steps for LLM outputs in critical applications.

Topics

• Qwen 3.5 Models
• Causal Reasoning
• LLM Benchmarking
• Model Scaling
• Logical Puzzles

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.