Meta's 'Watermelon' Model Has Caught Up to GPT-5.5: What It Means for the AI Tools You Choose in 2026

The News: Watermelon Catches GPT-5.5

Meta is finally closing the gap with OpenAI, its top AI leader says. In an internal town hall in July 2026, Meta superintelligence chief Alexandr Wang told employees that Meta's upcoming AI model — internally codenamed "Watermelon" — has caught up with OpenAI's flagship GPT-5.5 model, according to two sources familiar with the matter, as first reported by Business Insider's Charles Rollet and Pranav Dixit.

Wang reportedly cited "closely followed AI model benchmarks" to back the claim, though it is not clear exactly which benchmarks he referenced. In a town-hall quote relayed by a person familiar with the meeting, Wang said: "Watermelon, our next model after Avocado, is currently in training." He added that "Watermelon uses an order of magnitude more compute than Avocado."

If accurate, it would mark the clearest sign yet that Meta's enormous investment in chips, data centers, and talent — plus CEO Mark Zuckerberg's aggressive recruiting blitz — are starting to pay off, even as the broader AI race keeps accelerating.

The Backstory: Avocado, Muse Spark, and Meta's Catch-Up Quest

To understand why "Watermelon catching GPT-5.5" is a big deal, you have to know where Meta just came from. "Avocado" is Meta's internal codename for Muse Spark, the first model in a new family that the company released in April 2026. By Wang's own framing, Watermelon is simply the next release after Avocado in that same family.

The problem with Avocado, however, was results. According to the report, Muse Spark performed well on benchmarks but did not match or exceed OpenAI or other labs such as Anthropic. For a company that has bet its future on AI, that is a painful place to be: solid, but not the model developers reach for first.

Meta's ambitions have long hinged on a single goal: closing the gap with OpenAI, Google, and Anthropic. Despite massive spending, the company has struggled to convince developers and customers that its models belong at the industry's leading edge. Zuckerberg appointed Wang last year to lead the charge, renaming Meta's AI division to Meta Superintelligence Labs. The Watermelon claim is the first internal milestone that suggests that bet may be working.

"Watermelon, our next model after Avocado, is currently in training. Watermelon uses an order of magnitude more compute than Avocado." — Alexandr Wang, Meta Superintelligence Labs, per a person familiar with an internal town hall.

Why "An Order of Magnitude More Compute" Matters

The most concrete technical detail in Wang's remarks is the compute claim. An "order of magnitude" means roughly 10x more training compute than Muse Spark. In modern AI, training compute is one of the strongest predictors of capability — so a 10x jump is exactly the kind of step-change that could move a model from "competitive" to "frontier."

It also explains the economics of Meta's strategy. Unlike OpenAI and Anthropic, which keep their frontier models proprietary and gated behind APIs, Meta's lineage (from Llama onward) has favored open-weight releases that developers can run, fine-tune, and self-host. If Watermelon ships at GPT-5.5-class quality as an open-weight model, it would dramatically reshape the value equation for anyone building AI tools: a top-tier model you can control yourself, not just rent.

Wang also hinted publicly at what is coming. In a post on X, he said an update to the current Muse Spark model is coming soon, with major gains in coding and agentic capabilities aimed at closing the gap with rival models. When a user asked when Meta would have a coding model on par with Anthropic's Claude Opus, Wang replied that it would be "pretty soon," adding that users would like what the company has "cooking."

The Catch: Benchmarks Are Not a Shipped Product

Before you rip up your AI tool stack, there are three big caveats to keep in mind.

First, Watermelon is still in training and has not been released. An internal benchmark win, sourced anonymously, is a signal — not a product you can call today. Meta declined to comment on the report, and OpenAI did not respond to a request for comment.

Second, the goalpost has already moved. GPT-5.5 is the model OpenAI released in April 2026. OpenAI then debuted its most powerful model yet, GPT-5.6, late last month — but has not released it generally, reportedly based on the U.S. government's requests. In other words, even if Watermelon matches GPT-5.5, it is tying with a model that is no longer OpenAI's stated frontier. Catching up is not the same as leading.

Third, benchmarks and real-world tool performance are not the same thing. The model that scores highest on a leaderboard is not automatically the best engine for your coding agent, chatbot, or workflow. Ecosystem, latency, tool-calling reliability, context length, and pricing all decide which model actually belongs in your product.

What It Means for the AI Tools You Use

If you are picking AI tools in 2026, Meta's catch-up changes the landscape in practical ways:

• Watch for a credible fourth frontier option. For most of the past year, the practical top tier has meant GPT, Claude, and Gemini. A GPT-5.5-class model from Meta — especially as a potential open-weight release — would give builders a serious alternative with different licensing and self-hosting economics.
• Coding tools are the front line. Wang specifically called out "major gains in coding and agentic capabilities." If Meta delivers a Claude-Opus-class coder, the AI coding assistants and agentic IDEs that let you swap models will benefit first. Tools that lock you to a single vendor could look expensive by comparison.
• Agentic capabilities are now table stakes. Every frontier lab is now benchmarking on agents, not just chat. Expect the AI agents and automation tools you evaluate in 2026 to advertise benchmark jumps like Watermelon's as proof they can handle multi-step, real-world work.
• The release date matters more than the leak. A leaked internal benchmark will not ship features to your product. Track when Meta actually releases the model — and whether it arrives as open weights, an API, or both — before rewriting roadmaps.

The throughline: the model layer is getting more crowded and more competitive, which is good news for anyone choosing AI tools. More capable, cheaper, and more open options mean the tools built on top of them get better too. Compare the latest AI Coding & Agent tools and AI Chatbot & Q&A tools on aitrove.ai to see which models power the platforms worth your time.

Frequently Asked Questions