Musk responded to a question from a user on social platform X, stating, "Grok 4.7 should be roughly comparable to Opus 5.0, not 5.1," adding that "it is stronger in some aspects and weaker in others," and mentioned that the team "needs to fix the multimodal performance."

He also announced the roadmap for subsequent versions—"Grok 4.8 will have significant improvements," "Grok 4.9 may reach Astra/Fable level," and "Grok 5 may surpass everything currently available," concluding with "we'll wait and see," indicating a noticeably more cautious tone than before.

This statement sharply contrasts with Musk's claim on September 2, when he publicly stated that "Grok 4.7 will surpass all existing models," and that "except for slightly slower service speed, it outperforms Grok 4.6 in all aspects"; however, by September 11, he admitted that the model "still needs a few more days of refinement," citing that the reinforcement learning training phase "may have overly penalized answer length," causing the model to give up too early on complex tasks and not fully self-validate.

On September 13, Musk skipped the originally planned Grok 4.7 and directly announced the larger parameter Grok 4.8 (approximately 2.5 trillion parameters, using a new C++ software stack), expanding nearly 20% from the initially planned 2.1 trillion parameters for Grok 4.7; as of now, xAI has not yet released the official product page, pricing, benchmark results, or specific release date for Grok 4.7 or 4.8.

According to previously disclosed timelines, the Grok 4.5, 4.6, and 4.7 models were originally planned for intensive release within about two months, with parameter sizes expanding from approximately 1.5 trillion for version 4.6 to 2.1 trillion for version 4.7; in comparison, Anthropic's recently released Claude Fable 5.1 achieved a score of 52.6% in the Terminal-Bench-Science benchmark, regarded as one of the current reference points in this field.

This cycle of "release—delay—retraction—further delay" occurs against the backdrop of xAI's ongoing investment in infrastructure—its Memphis "Colossus 2" supercomputing cluster has deployed approximately 550,000 GB200/GB300 chips, with a total power close to 1 gigawatt, and the GPU procurement cost alone is about $18 billion; for xAI, the gap between the speed of model iteration and actual performance is directly related to whether it can convince investors to continue funding this scale of computing power investment, also affecting the valuation narrative of Musk's enterprises in the AI lab ranking.

Source: Public Information