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On July 3, 2026, Alexandr Wang posted on X that Meta is preparing to release a new model — codenamed Watermelon — “soon.” The post described Watermelon as significantly stronger at coding and agentic tasks than Muse Spark, its predecessor launched in April. Internal benchmarks, Wang said, show parity with GPT-5.5.
Watermelon is not available yet. There is no API date. Meta has not published an official model card.
Here is what we know and how to think about it as a builder.
What Watermelon Is
Watermelon is the working codename for the next model from Meta Superintelligence Labs — the team Wang built after joining Meta in late 2025. Muse Spark was the lab’s first release; Watermelon is the follow-on.
The defining difference from Muse Spark:
Compute scale. Wang described Watermelon as using “an order of magnitude more compute” than Muse Spark. In the context of foundation model training, that phrase typically means 8–15× more GPU-hours, though the exact multiplier is not confirmed. More compute at constant architecture generally yields better reasoning coherence on long-horizon tasks — the kind that matters for coding agents, multi-step tool use, and sustained context tracking.
Enhanced contemplating mode. Muse Spark shipped with a “contemplating mode” — a multi-agent reasoning layer that has sub-models deliberate over a response before the final output. Wang’s description suggests Watermelon extends this. The implication: Watermelon’s best-quality outputs will likely involve multiple inference steps under the hood, with associated cost and latency.
Coding-first focus. The benchmarks Wang cited and the framing of the announcement both center coding. This is consistent with where the competition is sharpest — SWE-bench Verified and similar coding-agent evals have become the primary battlefield among frontier models in 2026.
The Benchmark Picture
Wang cited internal results comparing Watermelon to GPT-5.5. The numbers reported by third parties covering the announcement:
| Benchmark | Watermelon | GPT-5.5 |
|---|---|---|
| MMLU | 92.4% | 92.4% |
| HumanEval | 96.3% | 96.1% |
| GSM8K | 94.7% | 94.5% |
| SWE-bench Verified | 48.2% | 49.5% |
Three caveats apply before you weight these numbers heavily:
These are internal benchmarks. Meta has not submitted Watermelon to Scale Labs, HuggingFace, or any third-party evaluator. The numbers come from Wang’s X post and subsequent media coverage, not an independently run evaluation.
Muse Spark set a low baseline. Muse Spark scored approximately 48% below GPT-5.5 on SWE-bench Verified at launch. Reaching 48.2% — near GPT-5.5’s 49.5% — is a significant leap from Muse Spark, but it places Watermelon in the same tier as GPT-5.5, not ahead of it. Kimi K2.7 Code and Claude Sonnet 5 both score higher on independent SWE-bench evaluations at the time of writing.
“Soon” has meant months at Meta. Wang announced Muse Spark’s API access as coming “soon after” the April 8 launch. As of July 4, developer API access to Muse Spark has not shipped — nearly three months later. The term “soon” in Meta Superintelligence Labs announcements should be interpreted as an aspiration, not a date.
The API Access Problem
The most relevant fact for builders right now is not Watermelon’s benchmarks. It is Meta’s track record on API access.
Muse Spark launched April 8, 2026. Developer API access was promised “soon after.” It has been delayed twice. The current stated reason is additional safety evaluation. Builders who planned production integrations around Muse Spark in April and May had to route around it.
Watermelon repeats this pattern at a higher compute scale. More compute means longer training, longer red-teaming, longer safety review cycles. The “soon” in Wang’s post likely means weeks minimum, potentially months before any API is available — and there is no guarantee the API ships before a third delay.
Until Meta demonstrates it can ship API access on a predictable schedule, building against a Watermelon release timeline is not a reliable planning assumption.
What Builders Should Use Today
If you need frontier coding performance right now, three options are live:
Kimi K2.7 Code — $0.95/$4.00 per million tokens, 1T parameter MoE, 256K context, 32B active per forward pass. Available via api.moonshot.cn and as of July 1 via GitHub Copilot. Leads public SWE-bench leaderboards among open-weight models. If you need cost-efficient agentic coding at production scale, this is the current price/performance leader.
Claude Sonnet 5 — $2/$10 per million tokens (introductory through August 31), default model on the Anthropic Platform. Strong multi-step tool use, best-in-class at following complex instructions. Preferred for coding agents that require reliable output structure and context tracking across long sessions.
GPT-5.5 — $5/$20 per million tokens. Currently leads several public coding benchmarks. If you are in the OpenAI ecosystem already, this is the current production-ready frontier choice for coding tasks.
Watermelon, when it ships with API access, will add a fourth live option — potentially competitive on price if Meta prices aggressively to gain market share after Muse Spark’s delayed rollout. That is worth tracking. It is not worth waiting for.
The Bigger Picture: Meta’s Proprietary Model Bet
For years, Meta’s AI strategy was open-source. Llama 3, Llama 4, Code Llama — Meta published weights, invited the ecosystem to build on them, and accepted that it wouldn’t capture downstream economic value directly. The logic was brand, recruitment, and research feedback loops.
Muse Spark and Watermelon are a departure. They are closed, proprietary models run by a separate internal lab under Alexandr Wang. The weights are not published. API access is gated. This is a direct competition play against Anthropic, OpenAI, and Google — not a contribution to the open-source ecosystem.
The change has trade-offs for Meta. Its open-weight models (Llama 4, and presumably future Llama releases) still exist and are still published. But the brand association between Meta and open-source AI has weakened. Builders who relied on Meta’s open-weight history to plan long-term infrastructure bets now have to track two separate Meta AI strategies simultaneously.
For builders evaluating model risk, this matters: Watermelon is not open-weight. When — and if — the API ships, the usual considerations apply: vendor lock-in, pricing changes, availability SLAs. You cannot self-host it as a fallback.
What to Watch
- API access timeline. If Meta ships Muse Spark API access before Watermelon’s launch, it signals the team has resolved their safety-review bottleneck. If Muse Spark still has no public API when Watermelon drops, plan for the same delay on Watermelon.
- Independent benchmark validation. Watch for Scale Labs or HuggingFace evaluations of Watermelon once it ships. Internal Meta numbers should be verified against third-party runs before you route production traffic.
- Pricing. Meta has not announced Watermelon pricing. If it comes in below $3/$15 per million tokens, it will pressure the mid-tier market meaningfully. If it’s priced at or above GPT-5.5, the value proposition narrows to the cases where Meta’s specific architecture advantages matter.
- API launch announcement on X from Wang. The July 3 post was the signal; the next Wang post on Watermelon will likely be the actual launch announcement.