Meta’s Watermelon, the internal codename for the next frontier large language model after April’s Muse Spark (Avocado), remains in training as of early September 2026 while Meta Superintelligence Labs chief Alexandr Wang reported July parity with OpenAI’s GPT-5.5 on unspecified benchmarks via roughly 10x the compute. August reporting highlighted an October target tied to the consumer Hatch AI agent platform and broader agentic capabilities, following the September 2 Muse Spark 1.3 roadmap update that referenced “bigger models.” Traders are weighing the aggressive scaling investment against historical slippage risks in frontier training runs, OpenAI and Anthropic iteration pace, and the lack of third-party verification or public release confirmation. Key near-term catalysts include any October announcement, benchmark disclosures, or delays signaled in earnings or internal updates.
基於Polymarket數據的AI實驗性摘要。這不是交易建議,也不影響該市場的結算方式。 · 更新於$12,303 交易量
9 月 30 日
5%
10 月 31 日
74%
11 月 30 日
93%
$12,303 交易量
9 月 30 日
5%
10 月 31 日
74%
11 月 30 日
93%
This market will resolve to "Yes" if Meta releases "Watermelon" or a model confirmed to be the model referenced above, and that model is made available to the general public by the listed date (ET). Otherwise, this market will resolve to "No".
A qualifying model must be named "Watermelon" or be identified, by Meta or by a consensus of credible reporting, as the model internally codenamed "Watermelon," regardless of the name under which it is ultimately released.
A qualifying model must be launched and publicly accessible, including via open beta or open rolling waitlist signups. A closed beta or any form of private access will not suffice. The release must either be clearly defined and publicly announced by Meta as accessible to the general public, or otherwise be made publicly accessible and explicitly labeled on the company's official website. Labeling errors, placeholder text, or version names displayed on the website that do not correspond to a model that is actually accessible to the general public will not qualify.
The primary resolution source for this market will be official information from Meta, with additional verification from a consensus of credible reporting.
市場開放時間: Sep 4, 2026, 10:35 AM ET
This market will resolve to "Yes" if Meta releases "Watermelon" or a model confirmed to be the model referenced above, and that model is made available to the general public by the listed date (ET). Otherwise, this market will resolve to "No".
A qualifying model must be named "Watermelon" or be identified, by Meta or by a consensus of credible reporting, as the model internally codenamed "Watermelon," regardless of the name under which it is ultimately released.
A qualifying model must be launched and publicly accessible, including via open beta or open rolling waitlist signups. A closed beta or any form of private access will not suffice. The release must either be clearly defined and publicly announced by Meta as accessible to the general public, or otherwise be made publicly accessible and explicitly labeled on the company's official website. Labeling errors, placeholder text, or version names displayed on the website that do not correspond to a model that is actually accessible to the general public will not qualify.
The primary resolution source for this market will be official information from Meta, with additional verification from a consensus of credible reporting.
Meta’s Watermelon, the internal codename for the next frontier large language model after April’s Muse Spark (Avocado), remains in training as of early September 2026 while Meta Superintelligence Labs chief Alexandr Wang reported July parity with OpenAI’s GPT-5.5 on unspecified benchmarks via roughly 10x the compute. August reporting highlighted an October target tied to the consumer Hatch AI agent platform and broader agentic capabilities, following the September 2 Muse Spark 1.3 roadmap update that referenced “bigger models.” Traders are weighing the aggressive scaling investment against historical slippage risks in frontier training runs, OpenAI and Anthropic iteration pace, and the lack of third-party verification or public release confirmation. Key near-term catalysts include any October announcement, benchmark disclosures, or delays signaled in earnings or internal updates.
基於Polymarket數據的AI實驗性摘要。這不是交易建議,也不影響該市場的結算方式。 · 更新於



警惕外部連結哦。
警惕外部連結哦。
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