Ben Santora recently conducted a detailed performance test of a quantized version of Qwen2.5 (Qwen 2_5-7B-Instruct-IQ4_XS) using his PC, which features an 11th Gen Intel Core i7-1165G7 CPU and 12 GiB of RAM. The test involved solving a complex logic puzzle, 'The Midnight Gathering,' designed to evaluate the reasoning capabilities of both Small Language Models (SLMs) and Large Language Models (LLMs). The puzzle was crafted to test models' deductive and inductive reasoning skills, revealing unique failure modes across different architectures.

For instance, the quantized Qwen SLM experienced a deductive failure due to its limited 'mental bandwidth,' while Gemini "Online" demonstrated an inductive bias failure by smoothing over contradictions to provide a solution. Conversely, Kimi2 excelled by maintaining logical integrity, refusing to produce a solution when a contradiction was detected. This test highlights the critical need for models to prioritize accuracy over helpfulness, especially in tasks requiring strict logical adherence.

The findings underscore the importance of developing models that can distinguish between solving puzzles and validating constraints, a crucial step in advancing AI capabilities in reasoning and problem-solving. The implications are significant for the AI industry, affecting developers and researchers who aim to create more robust and reliable models.