The past two days have seen a lot of buzz around Grok-3, and you might have already read about how it has vaulted to the top of model performance benchmarks.

As seen in the comparison below, Grok-3 Reasoning Beta and Grok-3 Mini Reasoning are clearly outperforming models like O3-Mini, O1, DeepSeek-R1, and Gemini-2 Flash Thinking.

👉 But what caught my attention were the additional bars (in lighter blue) stacked on top of the Grok-3 models. What do these represent?

If my interpretation is correct, is Grok-3 showing performance improvements when using Chain of Thought (CoT) reasoning or extended inference time in these additional bars?

We know that CoT prompting allows a model to think step by step, reason through the output, and improve performance. If the additional blue bars indeed represent this improvement, then Grok-3 models seem to benefit significantly from extra computational steps during these benchmarks.

👉 In simple words:
Grok-3 models allocated more compute per query, leading to better reasoning accuracy.

If this interpretation is correct (i.e., the additional blue bars indicate CoT reasoning), then it raises an important question: How would other models perform if they were given the same additional compute time?

👉 That would be the true apples-to-apples comparison.

I have no doubt that Grok-3 is an excellent model and is here to stay, but I can’t help but be cautious about benchmark reports where key details are often buried in fine print.

So, before selecting a model for your use case, here are few points that you should keep in your mind:
📍 Take your time to analyze its real-world performance.
📍 We are entering a phase with multiple strong AI models—explore your options wisely.
📍 Always conduct due diligence before choosing a model for enterprise rollouts.