Meta just declared it’s no longer playing catch-up in the AI arms race. Alexandr Wang, the company’s chief of Superintelligence, told employees during an internal town hall on July 2 that the company’s latest model, codenamed Watermelon, has matched OpenAI’s GPT-5.5 on key benchmarks.

There’s a catch, though. The model is still in training, the benchmarks are internal and unnamed, and nobody outside Meta has verified any of it.

What we actually know about Watermelon

Watermelon reportedly uses approximately ten times more computational resources than its predecessor, the Avocado model. Avocado was part of Meta’s Muse Spark release back in April 2026, which dropped around the same time OpenAI launched GPT-5.5.

The specific benchmarks where Watermelon allegedly matches GPT-5.5 remain unnamed. That’s a meaningful omission. In AI, benchmark selection matters enormously. A model can look world-class on one set of evaluations while falling short on others. Without knowing which metrics Meta is referencing, the claim is essentially: “Trust us, we measured some things, and we did well.”

No release timeline has been announced. No independent validation exists. The model isn’t finished training.

The AI spending race and what it means for markets

Meta has a distribution advantage that OpenAI can only dream about. Billions of users across Facebook, Instagram, WhatsApp, and Threads represent an instant deployment surface for any model Meta produces.

The compute requirements alone tell a story. Ten times more compute than Avocado means Meta is burning through GPU capacity at an extraordinary rate.

Why investors should stay cautious

The absence of independent verification is not a minor detail. AI companies have a well-documented history of cherry-picking benchmarks that make their models look competitive while quietly underperforming on the evaluations that matter most for real-world applications.

Investors should watch for two things. First, whether Meta releases Watermelon benchmarks publicly with enough detail for independent researchers to replicate the results. Second, whether any timeline emerges for a public release or developer access. Until both of those happen, this remains a corporate morale booster that escaped into the wild.