Meta’s Muse Wonder Full Just BEAT Opus 5.5 and GPT-6 Astra!

Summary
In this video, the host of the channel Universe of AI analyzes Meta Superintelligence Labs’ surprise release of their new frontier models, Muse Wonder and Muse Wonder Full. The presenter breaks down Meta’s official benchmark tables against Claude Opus 5.5, GPT-6 Astra, and Claude Fable 5.1, followed by a review of early community demos and tests across coding, voxel generation, and agentic workflows.

What is shown

• [00:00] Intro comparing split-screen simulation demos (koi pond, voxel pagoda, pelican animation) generated by Muse Wonder models versus competitors.
• [00:23] Mark Zuckerberg’s X post announcing the Muse Wonder model family and accompanying official benchmark comparison table.
• [01:08] Detailed breakdown of agentic and knowledge benchmarks, highlighting personal agent performance and professional tasks.
• [03:07] Sponsored segment for freebeat AI music video creation.
• [04:50] Benchmark analysis across legal work (Harvey Lab AA), financial analysis, AutomationBench AA, agentic computer use, context memory, and coding benchmarks (SWE Marathon and from-scratch coding).
• [07:26] Pricing slide from an X post by @chetaslua: Muse Wonder API pricing ($4.00/M input, $0.20 cached, $20.00/M output) and Muse Wonder Full availability under a MuseCode subscription.
• [07:51] Community tests on X: Ethan Mollick evaluating Muse Wonder’s progress on shader generation tests.
• [08:58] Comparative test by @notjazii evaluating Muse Wonder Max versus Cognition’s SWE 2 Max on a living koi pond simulation prompt.
• [09:37] Animations and 3D voxel world generation tests posted by @chetaslua, @AiBattle, and @cherry_reds.
• [10:57] Pelican riding a bicycle animation benchmark comparison.
• [11:16] Artificial Analysis Intelligence Index v4.3 leaderboard comparison showing Muse Wonder Full and Muse Wonder against Gemini 4 Argon and GPT-6.1 Sol.

Claims & numbers

• The presenter states that Meta released two frontier models: Muse Wonder and Muse Wonder Full.
• On Meta’s internal Personal Superintelligence Index benchmark: Muse Wonder Full scores 59.1%, Claude Opus 5.5 scores 55.2%, GPT-6 Astra scores 54.5%, Muse Wonder scores 54.3%, and Claude Fable 5.1 scores 49.9%.
• In legal agent work (Harvey Lab AA): Muse Wonder Full scores 13.1%, GPT-6 Astra scores 8.6%, Claude Fable 5.1 scores 6.4%, and Claude Opus 5.5 scores 4.2%.
• In financial analyst research: Muse Wonder Full scores 59.1%, Opus 5.5 scores 58.6%, Wonder Max scores 56.6%, and GPT-6 Astra scores 53.5%.
• In AutomationBench AA: Muse Wonder Full scores 45.4%.
• In Agentic Computer Use: Claude Opus 5.5 scores 81.8%, Fable 5.1 scores 80.7%, and Muse Wonder Full scores 78.4%.
• In context memory retention: Muse Wonder (smaller model) scores 85.6%.
• In Long Horizon Coding: Muse Wonder Full scores 79.0% vs. Opus 5.5 at 74.2%; in Software Engineering Marathon, Muse Wonder Full scores 57.5% vs. Opus 5.5 at 46.3%; but in from-scratch programming, Opus 5.5 leads at 70.5% vs. Wonder Full at 59.0%.
• API pricing for Muse Wonder: $4.00 per million input tokens, $0.20 cached input, $20.00 per million output tokens; Muse Wonder Full is gated behind a MuseCode subscription.
• On the Artificial Analysis Intelligence Index v4.3: Muse Wonder Full scores 56, Gemini 4 Argon scores 53, and Muse Wonder scores 52.
• In @notjazii‘s koi pond test: Muse Wonder took 24 minutes and cost ~$18, whereas SWE 2 Max took 15 minutes, cost $0, and generated higher visual fidelity.

Notable quotes

• “Out of the blue today, we got a new model from the Meta team actually, which is their new model Muse Wonder Full and Muse Wonder...” [00:00]
• “This model is really good at knowledge work... I wouldn’t expect this model to be the top coding model.” [01:10]
• “Overall today is a good day for Meta because they have improved their models quite significantly.” [11:44]

Assessment
This is a third-party commentary and review video evaluating Meta’s launch announcement, posted benchmark cards, and third-party tester impressions on X. The presenter does not run original evaluations live, instead curating posted benchmarks and user demos to point out that despite strong agent/knowledge benchmark scores, the model’s practical coding output appears noticeably weaker than specialized coding models.

Described by gemini-3.8-flash on 2026-10-13 from the video’s audio and frames.