Story
Models Are Getting Dumber on Purpose
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Overview
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.
Summary from w4g1.dev.
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Hacker News · w4g1.dev
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Based on 5 sampled comments from this thread. Analyzed 2026-08-17.
- Praise: 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.
- Praise: 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.
- Concern: 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.
- Concern: 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.