The world of artificial intelligence is a competitive one, with companies vying for the title of "smartest AI." Recently, Elon Musk's xAI threw its hat in the ring, claiming its latest model, Grok 3, outperforms OpenAI's leading models. But has xAI played fair in showcasing Grok 3's capabilities?
xAI's blog post proudly displayed a graph illustrating Grok 3's performance on AIME 2025, a challenging math benchmark. At first glance, it appeared that Grok 3 had indeed surpassed OpenAI's o3-mini-high. However, OpenAI employees quickly cried foul, pointing out a crucial omission in xAI's analysis: the "cons@64" scoring method.
What is cons@64?
Imagine giving a student 64 attempts to solve a math problem and then selecting their most frequent answer. That's essentially what cons@64 does for AI models. It allows the model to "try" a problem multiple times and takes the most common answer as the final solution. This naturally leads to higher scores, as the model has more chances to arrive at the correct answer.
xAI's graph conveniently omitted o3-mini-high's score using cons@64, making Grok 3 appear superior. When comparing the models based on their first attempt ("@1" score), Grok 3 actually falls behind OpenAI's models.
xAI's Defense and the Bigger Picture
Igor Babushkin, co-founder of xAI, defended the company's actions, pointing out that OpenAI has used similar tactics in the past when comparing its own models. While this might be true, it doesn't excuse xAI's potentially misleading presentation.
An independent researcher stepped in to provide a more complete picture, creating a graph that included cons@64 scores for all models. This graph painted a less clear-cut picture of Grok 3's dominance.
However, as AI expert Nathan Lambert highlights, a critical piece of the puzzle is still missing: the computational cost. We don't know how much processing power and resources each model used to achieve its scores. This information is crucial for understanding the efficiency and scalability of these AI models.
The Benchmark Debate
This controversy highlights a growing concern in the AI community: the limitations of current benchmarks. While tests like AIME 2025 provide some insights into a model's mathematical reasoning abilities, they don't tell the whole story. Factors like computational cost, energy consumption, and even the potential for bias in the benchmark dataset itself need to be considered.
As AI continues to evolve, it's crucial to develop more comprehensive and transparent benchmarks that accurately reflect the capabilities and limitations of these powerful technologies. This will ensure that claims of "world's smartest AI" are based on solid evidence and not just clever marketing tactics.
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