Meta made a “minor” release to Muse Spark, there’s nothing minor about it. Lots to parse here:
- This model is so fucking cheap I almost don’t believe it. In practice we see it’s 1/10 the cost of both Fable and GPT 5.5. If you thought OS models would compete away margins, just wait till you see this. It’s somehow cheaper to use MS 1.1 than host your own OS model…
- Coding improvements are significant. This was a real shortcoming in 1.0. But 1.1 sees a ~50% improvement in VibeCodeBench and ~10% improvement in SWE Bench. Not quite SOTA, but at this cost/latency it is still incredibly compelling.
- Speaking of latency, wow this model is fast. Across our benchmarks, we find it to be 1/4 the latency of Opus 4.8 and 1/2 the latency of GPT 5.5. I would expect Meta to have incredible web infra, but really don’t know what witchcraft they’re pulling to host the model for such fast inference at high rate limits.
- There is a public API. This is the first time Meta has released a model through a hosted API. I’m expecting lots of AI natives to hot-swap and rapidly test this model as a replacement. We’ll soon see if it's performant enough for those production uses.
- Speaking of AI natives, it’s been a wild week for Harvey’s legal benchmark. Grok 4.5 held the SOTA position for ~24 hours at 12% before MS 1.1 unseated it with a big jump up to ~20%. I suspect many internal evals will see surprising results like this. Glad to collab with @harvey @gabepereyra @nikogrupen @ItsJulioPereyra on this eval.
- Intelligence is more jagged than ever, even within individual domains like legal and coding. Every application and user benefits from staying dynamic. There an edge in picking the right model/system for each task.
