Did Xai over gray 3 benchmarks lied?


Debatates about AI Benchmarks – and as they have reported from AI lab – are played in public view.

This week, a Opea Employees to connected Eon musek’s ai company, xai, from the release of the release result results for his last Ai Model, large, e of the employee of xai, igorderhin, insists that the company was to the right.

The truth is situated somewhere in between.

In one Post on Xai’s blogThe company has a chart a graphic programming of performance on AIME 2025, a collection of the failed math of a recent invitation player. Some experts have asked Aime’s validity as ai benchmarkIn the. Nain, anime in 201525 and older versions of the test are generally used to receive a model skillfulness.

Xai’s graphics has two varieties of gray 3, cocke 3 reasoning and grok 3 mini-permanential available O3-mini-highOn Aime 2025. But Openai employees on x were fast for the XAI, don’t the Graphi-Mini-Mini-mini-high-high-high-high-high-high-high. ”

What is the consa 64, can you ask? Where the time, there is a few consenside of 64-billion, “and a memory of a disappointment. As you can imagine? 22nd To be bodels of benchmark scores pretty much a bit, and remove it from a chart can be issued in reality it is not the case.

Groom 1 Breeringsa and Bide 3 mini use of a Treight 2025 and “” Remain the First Score the used by the used by the used by the used by the use. Grok 3 ration of beta will still trust lightly behind Openai’s o1 model Put on “medium” computers. But Xai is Advertising grok 3 Like the “World Smartest Ai.”

Babuskinin argued on x The Openi has similarly understood the benchmark charts chartered in the past – Alibit Charts compare the performance of his own models. A neutral party in the debate to debate a more “correct value” an accurate Grindes, where almost every character of the Co. 64:

But as ai researchers Nathan Lambert indicated in a postMaybe the most important crisis is staying a mystery: The counter audulent (and currencies) cost it to achieve each model. That’s just for showing how much AI Benchmarks about models of models – and their strengths.



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