Meta may have another major AI model coming, and this one already has a strange codename: Watermelon.
According to reports, Meta’s upcoming Watermelon AI model has reached performance parity with OpenAI’s GPT-5.5 on internal benchmarks. The claim reportedly came from Meta’s Superintelligence Chief Alexandr Wang during an internal town hall, where he told employees that the model is now drawing level with one of OpenAI’s most advanced systems.
That sounds big. Maybe even huge. But there is one important problem.
Meta has not released the model. It has not shared the benchmark data. It has not explained which tests were used. So for now, Watermelon is less of a public AI breakthrough and more of a very loud signal from inside Meta: the company believes it is finally closing the gap.
Watermelon AI Is Meta’s Next Big Foundation Model Push
Watermelon is reportedly still in training, and it follows Meta’s previous internal model codenamed Avocado, also linked to Muse Spark. Wang reportedly said Watermelon uses around an order of magnitude more compute than its predecessor, which points to a much larger scaling effort by Meta.
That detail matters because this is not just about one model name beating another model name.
The frontier AI race is now heavily tied to compute. More chips. Bigger training runs. More expensive infrastructure. More researchers. More pressure to show that all of that spending is turning into something useful.
Meta has been spending aggressively on AI infrastructure, data centers, chips, and specialized hardware, while also trying to pull top AI talent away from rivals. Watermelon appears to be part of that larger bet: build models strong enough to compete directly with OpenAI, Google DeepMind, Anthropic, and other frontier AI labs.
The GPT-5.5 Comparison Needs Caution
The headline comparison is simple: Watermelon reportedly matches GPT-5.5.
The reality is messier.
No public benchmark results are available yet. Meta has not confirmed the test categories, scores, model size, training details, context window, architecture, or release timeline. Without those details, it is impossible to know whether Watermelon is broadly competitive or only strong on a narrow set of internal evaluations.
That is the part worth watching.
Internal benchmarks can be useful, but they are not the same as public testing. Once a model is released, developers test it in unpredictable ways. They throw code at it. Long documents. Broken prompts. Weird reasoning tasks. Enterprise workflows. Real user behavior. That is usually where the marketing claim either survives or starts to bend.
Coding and Agentic AI Could Be the Main Battleground
Watermelon is expected to focus on advanced reasoning, coding, instruction-following, and agentic AI tasks. In plain terms, Meta wants a model that can do more than answer a prompt. It wants one that can plan, write code, debug, follow multi-step instructions, and carry out more complicated work with less human steering.
That is where the AI race has moved.
Chatbots are no longer the only benchmark people care about. Companies want AI agents that can operate inside software tools. Developers want assistants that can actually handle real coding work. Businesses want automation that does not collapse after three steps.
Meta knows this. OpenAI knows it. Anthropic knows it. Google knows it.
The next wave of competition is not just about sounding smart in a demo. It is about whether a model can reliably perform work.
Meta Still Has A Moving Target Problem
Even if Watermelon does match GPT-5.5 internally, Meta may still be chasing a moving target. Techstrong reported that OpenAI has already debuted a more powerful GPT-5.6 iteration, though it is not publicly available.
That makes the race harder to judge from the outside.
AI labs are no longer competing only with public models. They are also competing with unreleased models, internal systems, private enterprise versions, and research models that may never appear in consumer products. So when one company says it has caught up, the next question is obvious: caught up to what, exactly?
Still, Watermelon could be important for Meta if the claims hold up. The company has the distribution advantage through Facebook, Instagram, WhatsApp, Messenger, Ray-Ban smart glasses, and its wider AI product ecosystem. A stronger foundation model would give Meta more room to improve consumer AI tools, developer products, enterprise features, and agentic systems.
Why Watermelon AI Matters
Meta has spent years trying to prove that it can compete at the frontier of artificial intelligence, not just release open models or add AI features into social apps.
Watermelon looks like another attempt to change that perception.
The reported GPT-5.5-level performance claim gives Meta a strong talking point, but the real test has not happened yet. Public benchmarks, third-party evaluations, developer feedback, and real-world usage will matter more than internal confidence.
For now, Watermelon is a serious claim without public proof.
And in AI, that gap matters.
