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: ​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 — Brought to you by: DX —Engineering intelligence for the AI era Jira Product Discovery —Prioritize with insights, build with confidence — 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 Wonder Full: 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/ X: https://x.com/clairevo — Production and marketing by https://penname.co/ . For inquiries about sponsoring the podcast, email jordan@penname.co.