Meta’s new ‘Muse Wonder’ model: good for AI agents, bad for design

I put Meta’s new Muse Wonder (and Wonder Full) 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.

What you’ll learn:

1. ​Why Meta’s “personal and professional agent” framing mostly holds up, and where it doesn’t
2. ​What active focus mode actually does on long-running tasks, and whether it’s real
3. ​The three agentic tasks where Muse ranked first, and why those categories matter
4. ​What Muse’s design output actually looks like (it’s not good)
5. ​How it compares to Sonnet on voice assistant tasks
6. ​Where Muse lands on SVG and short-form video versus Opus 5.5, Astra, and Fable
7. ​Whether I’m actually putting Muse into any of my agent workflows

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Brought to you by:

DX—Engineering intelligence for the AI era

Jira Product Discovery—Prioritize with insights, build with confidence

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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

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Tools referenced:

• ​ Meta Muse Wonder / Muse Wonder Full: https://research.meta.ai/blog/muse-wonder
• ​ Muse Code: https://developer.meta.com/ai/lp/muse-code

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Where to find Claire Vo:

ChatPRD: https://www.chatprd.ai/

Website: https://clairevo.com/

LinkedIn: https://www.linkedin.com/in/clairevo/

X: https://x.com/clairevo

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