Meta's 816B open-weight model is real. 'Open' is doing a lot of work in that sentence.
Meta put the 816B Muse Spark Open weights online under a licence that wants a 'Built with Muse Spark Open' byline and a deal above $20M in API revenue.
Vlad Makarovreviewed and published
Meta released the weights of Muse Spark Open on October 15, and the company's own headline is a claim about a league rather than a model: "the most capable US open-weight model to date." It is an 816-billion-parameter mixture of experts with 42 billion parameters active per token, a one-million-token context window and native text, image, video and audio input, built for coding and long-horizon agent work. What "open" means here is narrower than the framing suggests: the weights ship under a bespoke Muse Model License, not an open-source one, and the licence asks for something back.
What Meta actually shipped
The headline numbers are Meta's own and are not in dispute: 816B total parameters, 42B active, a 1M-token context at inference, a 202,048-token vocabulary and training data drawn from more than 100 languages.
| Specification | Muse Spark Open |
|---|---|
| Parameters | 816B total / 42B active |
| Type | Mixture-of-Experts transformer, perception encoder |
| Context length | 1M tokens at inference |
| Inputs and outputs | Text, image, video, audio in; text out |
| Vocabulary size | 202,048 |
| Languages | Data from more than 100 languages |
Weights are downloadable through Amazon SageMaker AI, Google Cloud Model Garden, Hugging Face and Microsoft Foundry Models. Hosted inference comes from Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI. Meta says it runs on SGLang, vLLM, TensorRT-LLM, Hugging Face Transformers, Apple MLX, llama.cpp and Unsloth, and that auto-tunable Helion GPU kernels in SGLang deliver 1.03-to-3.95x kernel-level speedups on NVIDIA and AMD silicon plus a 3% end-to-end gain on AMD MI350. That breadth of day-one support drew the least argument.
The scorecard is Meta's, and only Meta's
Every performance number in the launch post is self-reported.
| Benchmark | Meta's reported score |
|---|---|
| JobBench | 61.8 |
| MCP-Atlas | 89.2 |
| HLE-Diamond | 26.1 |
| ZeroBench | 50.0 |
| GPQA Diamond | 76.4 |
| Deep SWE | 89 |
| HLE | 61 |
| Toolathlon | 72.8 |
| Video-MME v2 | 61.5 |
The competitor column is Meta's work too: Inkling, Nemotron 3 Ultra, Beam, Mistral 4 Large, Qwen3.8-2.4T-A95B, GLM 5.3 and Kimi K3, all assembled by the vendor. The first number from outside Meta tells a flatter story. Artificial Analysis scored Muse Spark Open 39 on its Intelligence Index, relayed by Latent.Space's roundup: first among US open-weight models, but behind GLM-5.3 at 45 and Kimi K3 at 44. That roundup framed the release as Meta fulfilling its open-weights promise "but with a model weaker than their Spark 1.3." If that holds, this is not the closed Muse Spark 1.3 that ranks near the frontier, a line the launch post never draws. It also fits the pattern the wider open-versus-closed gap has tracked all month.
"Built with Muse Spark Open" is the licence's core demand
Read the licence on the model card and the constraints are specific. The model is free to use, copy, modify, fine-tune, distill, merge, publish, distribute and sell. Three conditions travel with every copy.
| Condition | What it requires |
|---|---|
| Attribution | Prominently display "Built with Muse Spark Open" in the UI or docs of any product or derivative |
| Model-as-a-Service | At more than $20M aggregate gross revenue in any 12 months, notify Meta and sign a separate written agreement |
| Usage policy | Ship the licence and its attached Usage Policy, including the prohibited-use list, with any redistribution |
"Sell" is therefore not unconditional, and the $20 million trigger is measured against the licensee's and its affiliates' total revenue, not revenue earned from the model. Nathan Lambert, in a post relayed by the roundup, called the licence "trash." The framing circulating in coverage — that open-weight is not the same thing as open-source — is accurate on the terms as written. One commenter on the release's Hacker News thread made the commercial point: "The usual effect of separate licensing is decreased price competition from public providers."
"On your desktop" means two machines, not a laptop
Meta's most striking claim is deployment, not scores: Muse Spark Open is, it says, the first model in its class to run on a desktop, using two Apple M5 Ultra Mac Studios and the latest MLX. Each Mac Studio's unified memory lets its GPU address up to 512 GB, and the chip's 1.2 TB/s of memory bandwidth is fast enough to read the small slice each token needs. Meta's own line is that "two Mac Studios behave like one supercomputer with a terabyte of unified memory."
That sentence describes a five-figure desk, and Meta's setup assumes the 512 GB configuration at the top of the line. A commenter on the release thread priced it from experience: "A single Mac Studio w/ M5 Ultra and half that RAM (256 GB) is around $11k, and the 512 GB option isn't even available yet." Apple's own research, linked in the same thread, reports more than 36 tokens per second only across a cluster of four Mac Studios. Meta's desktop story is a two-to-four-machine, high-memory build — the same shelf as the hosted endpoints, not a laptop download.
What the people who would run it said
The release thread on Hacker News ran to roughly 900 points, and the split was predictable. The scale, the NVFP4 checkpoint and the broad inference-engine support drew genuine approval, with commenters praising Meta for documenting deployments rather than dropping a blob and letting the ecosystem sort it out. The licence drew objections, several of them flat: a model whose terms restrict fields of use is not open-weight at all. The safety work was the other target. Latent.Space's roundup of the r/LocalLLaMA thread reported that one technical commenter argued the heavy safety post-training could make the model unsuitable for cybersecurity workflows, and named GLM 5.3 as similarly capable but less restricted.
Meta's position is that the tuning is a feature. The open-weights build "ships with additional safety post-training" precisely because it is meant to run without the system guardrails a hosted product would wrap around it, and Meta says the model was evaluated under its Meta Superintelligence Scaling Framework and met the thresholds that framework requires.
What would settle it
Two things are still missing. Meta promises a full evaluation report "available soon" — the model card is up, the report is not — and no independent lab has yet reproduced the vendor table task by task. Until then, the safe reading is the narrow one: Muse Spark Open is a genuinely large, genuinely downloadable model with unusually broad day-one support, and its launch numbers, its licence terms and its desktop story all come from the same page. The company's earlier closed family, Muse Wonder, is where the frontier claim actually lives.
