Models Are Getting Dumber on Purpose
w4g1.dev
August 16, 2026
6 min read
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59/100
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- The knowledge embedded in models has a limited shelf life, as facts can become outdated quickly, while procedural knowledge remains stable over time.
Community Sentiment
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.
