Big News: The AI landscape has just gotten a whole lot more interesting. A new site, AI IQ, is scoring frontier AI models on the human IQ scale. The results are already dividing the tech world. Honestly, I'm not surprised - this is where most AI benchmarking efforts fail. They try to reduce complex capabilities to a single number, which can be misleading.
The site's methodology is detailed, but it's not without its flaws. It groups 12 benchmarks into four reasoning dimensions: abstract, mathematical, programmatic, and academic. The composite IQ is a straight average of those four dimension scores. In my experience, this approach can work, but it's not perfect. The math doesn't add up, and the results can be skewed.
One of the most useful features of AI IQ is its inclusion of an emotional intelligence score. This is a game-changer, as it allows for a more nuanced understanding of each model's capabilities. The site maps each model's EQ-Bench 3 Elo score and Arena Elo score to an estimated EQ using calibrated piecewise-linear scales. This is a step in the right direction, but it's not without its limitations. The EQ scores produce a meaningfully different ranking than IQ alone, which is interesting. Read also: AI Revolution in Law: Exposing Neglect in Lawyer Judgment Development.
The IQ vs. Effective Cost scatter plot is another valuable tool. It maps each model's estimated IQ against an effective cost metric, which is defined as the token cost for a task using 2 million input tokens and 1 million output tokens, multiplied by a usage efficiency factor. This chart reveals a familiar pattern in enterprise technology: the best models are not always the best value. Models like GPT-5.4-mini, DeepSeek-V3.2, and MiniMax-M2.7 occupy a sweet spot in the middle, with respectable IQ scores between 112 and 120, at effective costs ranging from roughly $1 to $5 per task.
The real race isn't for the highest score - it's for the smartest model stack. This is where AI IQ shines, as it provides a single framework for comparing models across providers, dimensions, and price points. It's not perfect, but it's a step in the right direction. The site offers a 3D visualization mapping IQ, EQ, and effective cost simultaneously, which is a powerful tool for enterprise buyers. Read also: Big News: Notion Revolutionizes Workspace with AI Agents - Enterprise AI & Cloud.
Critics say AI's jagged capabilities make a single IQ score dangerously misleading. I agree - this is a limitation of the current approach. However, AI IQ is aware of this issue and is working to address it. The site compresses ceilings for saturated benchmarks, which helps to mitigate the problem. In my opinion, this is a necessary step towards creating a more comprehensive benchmarking system.
The NextCore Edge: What others are missing is the importance of model stacking. This is where the real value lies, as it allows for the creation of complex systems that can tackle a wide range of tasks. AI IQ is a step in the right direction, but it's only the beginning. We need to think about how to combine these models in a way that creates a synergistic effect, where the whole is greater than the sum of its parts.
Realistic Critique: One of the main limitations of AI IQ is its reliance on a single scoring system. This can be misleading, as it doesn't account for the complexities of each model's capabilities. Additionally, the site's methodology is partially opaque, which can make it difficult to understand how the scores are calculated. However, the site is working to address these issues, and it's a step in the right direction.
As the AI landscape continues to evolve, we need to think about how to create benchmarking systems that can keep up. This is a challenging task, but it's necessary if we want to unlock the full potential of these models. Read also: Semrush & Lovable Partnership: Revolutionizing Search Intelligence in Building Experience.
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