Meta's Watermelon model, the next frontier large language model after Muse Spark (internally codenamed Avocado), remains in training with roughly 10x the compute of its predecessor. In early July 2026, AI chief Alexandr Wang stated internally that it had reached parity with OpenAI's GPT-5.5 on key benchmarks, though those evaluations remain unverified externally. Credible reporting from August points to an internal October 2026 target for release, alongside the consumer Hatch agent platform. On September 2, Meta shipped Muse Spark 1.3 and signaled a broader roadmap of larger models. Traders are weighing this timeline against typical training-to-release lags, ongoing infrastructure spending, and the need for demonstrated capabilities beyond internal claims.
Experimental AI-generated summary referencing Polymarket data. This is not trading advice and plays no role in how this market resolves. · UpdatedSeptember 30
14%
October 31
63%
November 30
78%
$221 Vol.
September 30
14%
October 31
63%
November 30
78%
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.
Market Opened: Sep 4, 2026, 10:35 AM ET
Resolver
0x65070BE91...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.
Resolver
0x65070BE91...Meta's Watermelon model, the next frontier large language model after Muse Spark (internally codenamed Avocado), remains in training with roughly 10x the compute of its predecessor. In early July 2026, AI chief Alexandr Wang stated internally that it had reached parity with OpenAI's GPT-5.5 on key benchmarks, though those evaluations remain unverified externally. Credible reporting from August points to an internal October 2026 target for release, alongside the consumer Hatch agent platform. On September 2, Meta shipped Muse Spark 1.3 and signaled a broader roadmap of larger models. Traders are weighing this timeline against typical training-to-release lags, ongoing infrastructure spending, and the need for demonstrated capabilities beyond internal claims.
Experimental AI-generated summary referencing Polymarket data. This is not trading advice and plays no role in how this market resolves. · Updated



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