Meta’s new ‘Muse Wonder’ model: good for AI agents, bad for design
I put Meta’s new Muse Wonder (and Wonderful) frontier models through the How I AI bench, which covers nearly 20 tasks across prototyping, agentic workflows, coding, writing, and creative work. I scored everything with an LLM-as-a-judge pass and my own voice notes, then pulled together the first-look results you see here.
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What you’ll learn:
- Why Meta’s “personal and professional agent” framing mostly holds up, and where it doesn’t
- What active focus mode actually does on long-running tasks, and whether it’s real
- The three agentic tasks where Muse ranked first, and why those categories matter
- What Muse’s design output actually looks like (it’s not good)
- How it compares to Sonnet on voice assistant tasks
- Where Muse lands on SVG and short-form video versus Opus 5.5, Astra, and Fable
- Whether I’m actually putting Muse into any of my agent workflows
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In this episode, we cover:
(00:00) Intro and what Muse Wonder is
(01:19) Meta’s frontier capability claims
(04:36) Muse code
(06:06) Benchmark overview and how the How I AI bench works
(07:51) Overall results and TL;DR verdict
(11:53) Agentic tasks
(13:36) Email writing and voice assistant performance
(14:35) Where Muse falls short
(18:28) SVG illustration results
(19:33) Short-form video editing
(21:18) Fast on one-shot work, slow on agent tasks
(22:15) Final verdict and where I’ll use Muse Wonder next
Tools referenced:
• Meta Muse Wonder / Muse Wonderful: https://research.meta.ai/blog/muse-wonder
• Muse Code: https://developer.meta.com/ai/lp/muse-code
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
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