Meta's Muse Spark Open is an 816B open-weight model that runs on two Mac Studios. Here's the license, hardware needs, and why it trails Claude Opus 5.5 on coding.
Vamsi Tallapudi
Manager, Architect Technology at Cognizant
Meta dropped the weights for Muse Spark Open on October 15, 2026: an 816-billion-parameter mixture-of-experts model that activates 42 billion parameters per token, reads a 1-million-token context window, and ships under a custom license with a $20 million revenue trip wire. It runs across two Apple Mac Studios with M5 Ultra chips. And based on every coding benchmark Meta and third parties have published so far, it does not beat Claude Opus 5.5.
That's the short version. Here's what's actually in the box, what the license really restricts, what hardware you need to touch this thing, and why I wouldn't pick it as a coding agent's brain just yet.
Muse Spark Open is the open-weight release of Muse Spark, the model line Meta's Superintelligence Labs has been shipping since August 2026 (I covered the earlier Muse Code agent and Muse Spark 1.2 here). It's not a new model trained from scratch. Meta says it "shares the same base model as Muse Spark 1.3," with extra safety post-training layered on so it behaves without someone else's guardrails wrapped around it.
The specs, from Meta's PyTorch blog and its own announcement:
Same family that powers Meta's closed Muse Spark line. Meta just decided this particular checkpoint was safe enough to hand out the raw weights for.
Not what Llama trained you to expect, and definitely not Apache 2.0. Muse Spark Open ships under the Muse Model License, Meta's own custom terms.
The good parts: you can use, copy, fine-tune, and distill it for free, commercially included. Meta claims no rights over what the model generates, and you can ship a product on top of it without asking permission first.
The catch: if your company, plus affiliates, pulls in more than $20 million in revenue over any trailing 12 months and you're offering Muse Spark Open (or a derivative) as a model-as-a-service, you need a separate commercial agreement with Meta before you can keep doing that. You also have to display "Built with Muse Spark Open" on your product or docs, and the license bans military, weapons, surveillance, and a handful of other use cases outright.
That's lighter than some past Llama licenses, which gated on monthly active users instead of revenue. But calling this "open source" is still a stretch. "Open weight with a revenue trip wire" is the honest description, and at this point that's basically the house style for every US lab doing this.
Technically, yes. Comfortably, no.
Raw storage math first: the NVFP4 quantized version needs roughly 700GB, the full BF16 version needs 2TB. A 4-bit quantization gets it down to about 493GB, which still won't fit on a single 512GB machine.
That's where the Apple partnership comes in, and it's honestly the more interesting part of this release. Muse Spark Open runs across two Mac Studios with M5 Ultra chips, each contributing up to 512GB of unified memory for roughly 1TB combined, using Apple's MLX framework over Thunderbolt 5. With tensor parallelism, two Macs pushed about 25 tokens/second on the 4-bit model, and four Macs got that up to around 37 tokens/second.
A single M5 Ultra Mac Studio starts at $5,499. Since one machine can't hold the whole model, the minimum local setup runs past $11,000 before you add the extra Macs most people will want for better throughput. That's dramatically cheaper than the GPU rack you'd otherwise need for an 816B model, and it's genuinely clever engineering from both companies. It's also still a five-figure hobby. Most developers will just rent inference on OpenRouter or Fireworks instead of buying hardware.
No, and it's not close once you line up the benchmarks.
| Benchmark | Muse Spark Open | Claude Opus 5.5 | Kimi K3 | GLM 5.3 |
|---|---|---|---|---|
| Terminal-Bench 2.1 | 76.4 | — (scores 66.4 on 4.0) | 85.0 | 83.9 |
| DeepSWE 1.1 | 60 | — | 68 | 67 |
| SWE-bench Pro | reported 52-61 depending on source | 89.9 (record) | — | — |
Two things jump out. First, Muse Spark Open isn't just behind Claude, it's behind the Chinese open-weight models it's most often measured against. Kimi K3 and GLM 5.3 both beat it on Terminal-Bench 2.1 and DeepSWE 1.1 by real margins (I wrote up how Kimi K3 handles frontend coding against Claude here if that comparison is useful too). Second, Claude Opus 5.5 set the SWE-bench Pro record at 89.9% when Anthropic shipped it in September 2026, a 9.9-point jump over Claude Fable 5 and nearly 11 points over Opus 5 (full pricing and benchmark breakdown here). That's a different tier entirely from where Muse Spark Open lands.
Artificial Analysis's Coding Agent Index tells a similar story once you adjust for versions. The closed Muse Spark 1.1 (not even the open release) scored 69, trailing GPT-5.5 at 71 and only barely ahead of the now-dated Claude Opus 4.8. Nobody's run Opus 5.5 against Muse Spark Open in that specific index yet, but given how much ground Opus 5.5 gained on SWE-bench Pro, the gap almost certainly widened, not closed.
The r/LocalLLaMA thread on the release didn't pull punches either, with commenters pointing out Muse Spark Open was underperforming Qwen3.8 Flash Next and GLM 5.3 Flash, and that Meta has catching up to do. Hacker News landed in roughly the same spot.
Not coding-agent teams chasing the top score, honestly. Where it earns its keep:
If the goal is just "best coding agent, full stop," Claude Opus 5.5 wins that argument today, and the benchmarks aren't especially close.
I like that this exists. A US lab shipping a frontier-scale open model with real multimodal input and a 1M context window, and doing the engineering work to get it running on desk hardware instead of a data center, is good for the ecosystem no matter where it lands on a leaderboard. The Apple partnership is the part of this release that'll stick with me, more than the model itself.
But "most capable US open-weight model" is a title Meta gets almost by default right now, since OpenAI and Anthropic don't ship open weights at this scale and the real pressure is coming from Chinese labs that are currently just better at this specific job. If you're choosing a model for a coding agent on capability alone, this isn't it. If you're choosing on control, auditability, or running it on hardware you actually own, it's one of the only options at this size.
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