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Alexandr Wang Says Muse Wonder Focused on Real-World Tasks for Real People
- Who
- Alexandr Wang
- What
- explained Muse Wonder’s focus on real-world tasks informed by product adoption and how people use it
What happened
with muse wonder, we aimed to build a model that is useful for real world tasks for real people, informed by our product adoption and how people use muse. we focused on things that might not look as glamorous on twitter (for example, not 3d demo games), which in retrospect we probably could have done more of.
The story so far
Meta released Muse Wonder through the Meta Model API and Muse Code and Muse Wonder Full for Muse Code subscribers, while Alexandr Wang said the team was addressing a bug in how both models counted against subscribers’ usage limits. Wang promised resets once the bug was fixed; his subsequent update said issues remained. TechCrunch reports that Wonder’s API pricing is $4.00 per million input tokens and $20.00 per million output tokens. Artificial Analysis reports that Wonder and Wonder Full scored 52 and 56 on its Intelligence Index v4.3 at maximum reasoning effort, compared with 48 for Muse Spark 1.3 at the same setting. Full improved across all ten evaluations, with its largest gains in agentic evaluations. Both models support a one-million-token context window. Meta says its internal benchmarks show performance competitive with GPT-6 Astra, Claude Opus 5.5 and Claude Fable 5.1, while The Decoder reports weaker factual knowledge than leading competitors despite stronger coding and agent results. Statements about access through Meta’s personal agent, Muse, differ: Meta’s announcement says Wonder will power it soon, starting with subscribers, while Baoyu reports that Zuckerberg said subscribers could use it at launch. In his latest comments, Wang acknowledged there was still much to improve in response to a Wonder Full review that praised its writing and orchestration but criticized pricing and usage limits.
Keep reading
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