Meta’s Superintelligence Labs, led by Alexandr Wang, has accelerated its Muse Spark iteration cadence since launching the initial closed-source model in April 2026. Version 1.1 followed in July with gains in agentic tool use, coding, and multimodal reasoning, accompanied by a public Meta Model API preview and paid token pricing that directly competes with OpenAI and Anthropic offerings. The 1.2 update arrived August 5, continuing the focus on performance-efficiency improvements. Traders monitor release velocity, internal scaling signals, and Meta’s earnings commentary for clues on 1.3+ timing, while noting that product timelines in frontier AI often shift due to benchmark results or competitive positioning.
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8月31日
40%
9 月 30 日
53%
$21 交易量
8月31日
40%
9 月 30 日
53%
A qualifying model must have a name or model identifier that includes "Muse Spark" and be designated as version 1.3 or higher, regardless of capitalization, hyphenation, spacing, or surrounding prefixes, suffixes, dates, or descriptors. For example, a version 1.3 or higher named in the same manner as Muse Spark 1.1 or Muse Spark 1.2 would qualify, including a new whole-number generation such as Muse Spark 2, while models whose name does not include "Muse Spark" or which retain a version designation below 1.3, such as Muse Spark 1.2 (including any open-weight re-release of it), Muse Glimmer, or Muse Image, will not qualify.
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: Aug 14, 2026, 5:32 PM ET
Resolver
0x65070BE91...A qualifying model must have a name or model identifier that includes "Muse Spark" and be designated as version 1.3 or higher, regardless of capitalization, hyphenation, spacing, or surrounding prefixes, suffixes, dates, or descriptors. For example, a version 1.3 or higher named in the same manner as Muse Spark 1.1 or Muse Spark 1.2 would qualify, including a new whole-number generation such as Muse Spark 2, while models whose name does not include "Muse Spark" or which retain a version designation below 1.3, such as Muse Spark 1.2 (including any open-weight re-release of it), Muse Glimmer, or Muse Image, will not qualify.
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 Superintelligence Labs, led by Alexandr Wang, has accelerated its Muse Spark iteration cadence since launching the initial closed-source model in April 2026. Version 1.1 followed in July with gains in agentic tool use, coding, and multimodal reasoning, accompanied by a public Meta Model API preview and paid token pricing that directly competes with OpenAI and Anthropic offerings. The 1.2 update arrived August 5, continuing the focus on performance-efficiency improvements. Traders monitor release velocity, internal scaling signals, and Meta’s earnings commentary for clues on 1.3+ timing, while noting that product timelines in frontier AI often shift due to benchmark results or competitive positioning.
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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