Models Are Getting Dumber on Purpose
AI developers are increasingly prioritizing reasoning capabilities over factual knowledge in large language models to improve benchmark performance. This trade-off results in models that are highly skilled at logic but prone to hallucinations when asked for specific factual information.
Reasoning scores keep climbing while per-token compute keeps dropping. GLM-5.2 scores 99.2% on AIME 2026 with about 40 billion parameters active per token. Qwen3.5 scores 91.3% with 17 billion active. DeepSeek V4-Flash runs 13 billion active. For scale, GPT-4 was rumored to run around 280 billion active parameters in 2023, and it could barely solve an AIME problem. At the small end, Qwen3.5 9B fits in 6GB of VRAM quantized and roughly doubles the score of the next best model under 10B parameters on Artificial Analysis's intelligence index. If you only looked at math and code benchmarks, you'd conclude that models are getting smarter per parameter at an absurd rate.
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