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Models Are Getting Dumber on Purpose

w4g1.dev

August 16, 2026

6 min read

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63/100

Summary

Reasoning scores for AI models are increasing while per-token compute is decreasing. GLM-5.2 achieves 99.2% on AIME 2026 with 40 billion parameters, Qwen3.5 scores 91.3% with 17 billion parameters, and DeepSeek V4-Flash operates with 13 billion parameters, contrasting with GPT-4's rumored 280 billion parameters which struggled with AIME problems.

Key Takeaways

Models like GLM-5.2 and Qwen3.5 show high reasoning scores, with GLM-5.2 achieving 99.2% on AIME 2026 using 40 billion parameters.
Current models exhibit high hallucination rates, with Qwen3.5 4B and 9B scoring 80 to 82% on factual recall benchmarks, indicating they often generate incorrect information when lacking knowledge.
Labs are intentionally reducing factual knowledge in models to enhance reasoning skills, leading to a trade-off where models become generalists with broad but shallow knowledge.
The knowledge embedded in models has a limited shelf life, as facts can become outdated quickly, while procedural knowledge remains stable over time.

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Community Sentiment

Mixed

Positives

The idea of pluggable knowledge bases for tailoring models to specific tasks could revolutionize how we build AI, making it more versatile and user-friendly.
Commenters are excited about the potential for sparse models and hotloading new skills, which could make AI more adaptable to various tasks without heavy resource requirements.

Concerns

Skepticism looms over the claim that LLMs can explain their reasoning; it seems more like a facade than genuine understanding, raising concerns about trust in AI outputs.
Critics argue that models like Gemini 2.5 Pro, despite being labeled as leaders, still fail to deliver reliable factual recall, highlighting ongoing limitations in AI performance.