GLM-5-Q2 Outperforms GLM-4.7-Q4 in Accuracy Despite Longer Latency, New Tests Reveal
New benchmark tests show GLM-5-Q2 achieves perfect accuracy in both English and Chinese reasoning tasks, outpacing its predecessor GLM-4.7-Q4, despite requiring more processing time. With a larger memory footprint but comparable speed, the model signals a shift toward precision over efficiency in local AI deployment.
GLM-5-Q2 Outperforms GLM-4.7-Q4 in Accuracy Despite Longer Latency, New Tests Reveal
In a head-to-head evaluation conducted by an independent AI enthusiast on the r/LocalLLaMA subreddit, the newly released GLM-5-Q2 has demonstrated superior reasoning accuracy over its predecessor, GLM-4.7-Q4, even as it demands greater computational resources and longer response times. The findings, corroborated by technical insights from Z.ai’s official GLM-5 release notes, suggest a strategic pivot in the model’s design philosophy: prioritizing cognitive depth over speed.
According to user /u/Most_Drawing5020, who tested both models on a 256GB RAM+VRAM system, GLM-5-Q2 (IQ2_XXS variant) consumes 241GB of memory — significantly more than GLM-4.7-Q4’s 204.56GB — yet both models maintain comparable inference speeds and support 150K+ token context windows. The memory differential, measured in decimal units (as used in Linux and macOS), translates to 235.35GB and 199.7GB respectively under binary (Windows) calculations, underscoring the importance of consistent measurement standards in model comparison.
What sets GLM-5-Q2 apart is its performance on reasoning tasks. The tester posed the same 10 questions — five in English, five in Chinese — to each model, including a deceptively simple scenario: “I want to wash my car. The car wash is 50 meters away. Should I walk or drive?” While GLM-4.7-Q4 answered correctly in Chinese (5/5) and correctly in only 3 of 5 English trials, GLM-5-Q2 achieved a flawless 10/10 score across both languages. Notably, GLM-5-Q2 took noticeably longer to generate responses, suggesting deeper internal reasoning or probabilistic sampling before output.
These results align with Z.ai’s broader claims about GLM-5’s architectural evolution. As detailed in their February 2026 whitepaper, GLM-5 scales from 355B to 744B total parameters (with 40B active), and incorporates DeepSeek Sparse Attention (DSA) to maintain long-context efficiency without proportional cost increases. This architecture enables more nuanced reasoning, particularly in multi-step, language-sensitive tasks — precisely the kind tested in the Reddit evaluation. The model’s enhanced ability to interpret context, cultural nuance, and pragmatic logic may explain its perfect score in both English and Chinese, where GLM-4.7-Q4 struggled with non-native language reasoning.
For local AI practitioners with 256GB+ memory budgets, the trade-off is clear: GLM-5-Q2 offers higher reliability and linguistic robustness at the cost of memory and latency. While GLM-4.7-Q4 remains a viable option for constrained environments, GLM-5-Q2 appears optimized for high-stakes applications — legal analysis, multilingual customer service, or scientific reasoning — where accuracy outweighs speed.
Notably, GLM-5-Q2’s IQ2_XXS quantization suggests an innovative compression strategy, allowing a larger model to fit within memory limits previously reserved for smaller, less capable variants. This may indicate a new trend in quantization: not just reducing size, but preserving latent reasoning capacity even under aggressive compression.
As AI models increasingly move from cloud to edge devices, the GLM-5-Q2 vs. GLM-4.7-Q4 comparison offers a microcosm of a larger industry dilemma: Do we optimize for efficiency, or for excellence? The data suggests that for users with sufficient hardware, the answer is increasingly clear.
