Meta's Muse Spark Open Tops the US Open Leaderboard but Flops on Agents

Meta's Muse Spark Open Tops the US Open Leaderboard but Flops on Agents

6 min readOctober 17, 2026

Quick verdict

Meta released Muse Spark Open, an 816B-total, 42B-active mixture-of-experts model with open weights, a 1M context window, and text, image, video, and audio inputs. Artificial Analysis scores it 39 on its Intelligence Index, the highest for any US open-weight model and 13 points clear of NVIDIA's Nemotron 3.5 Super VL at 26. That is the good news. The bad news is in the same report card: it ranks last among the top nine open models on the four agentic evals, and one well-known analyst called it "DOA" against the smaller, MIT-licensed DeepSeek V4.1 Flash. So this is a real milestone for US open weights and a weak pick for anyone who wants a model that acts, not just answers. For most readers it changes nothing today, and it is worth watching if you route work to open models through a provider.

What actually shipped

The launch post on r/LocalLLaMA framed it as "the most capable US open-weight model to date," and the raw specs back up the scale. Muse Spark Open runs 62 layers, grouped-query attention with 16 KV heads, and 8 of 256 experts active per token, paired with a 1.8B perception encoder and a 1M context. It takes text, image, video, and audio, and outputs text.

The footprint is the part worth reading twice. BF16 weights come to roughly 1.6TB. Meta's NVFP4 checkpoint quantizes only the experts and cuts that to about 530GB, which the company says fits on four Blackwell Ultra GPUs, eight H200s, or two 512GB M5 Ultra Mac Studios. "Open weights" at this size does not mean your laptop. It means a hosted endpoint or a serious rig. Day-0 support arrived across the stack: mlx-lm shipped vision support with an Apple writeup on distributed inference across Mac Studios, and vLLM, which served RL rollouts during training, supported the release at launch.

Pricing from Together, Fireworks, and Baseten lands at $1.25 per million input tokens and $4.25 per million output, with cached input at $0.15. On Artificial Analysis's own cost-per-task math the picture gets more interesting: $0.84 per index task versus $2.01 for GLM-5.3 and $0.27 for DeepSeek V4.1 Flash. Muse burns about 40k output tokens per task, the fewest among the top nine open models (AA cost breakdown). Vals counts 55.5M output tokens across its whole index, the fewest of any open model it tracks, but notes input tokens drive most of the bill.

The benchmark split nobody should skip

The number Meta wants you to see is 39 on the Artificial Analysis Intelligence Index, a US open-weight record. Meta's own slide also claims 61.8 on JobBench, 89.2 on MCP-Atlas, 76.4 on Terminal-Bench 2.1, and 80.2 on BabyVision, all first-party. The independent reads add the asterisks. On AA's four agentic evals, Muse Spark Open finishes last among the top nine open models, even as it leads open models on AA-Omniscience, a knowledge test. Vals places it #12 among open-weight models and #38 overall. Arena ranks it the top US open model on Code Arena WebDev at 1511 points, which still only lands #13 among open models.

Put plainly: this model knows a lot and answers cheaply, and it struggles the moment a task needs it to plan, call tools, and recover from its own mistakes over many steps. That is close to the opposite of what a coding or agent workflow asks for, and it is the single fact that should decide whether you reach for it.

The license has teeth

The weights are open, but the terms are not Apache 2.0. The custom Muse Model License requires a "Built with Muse Spark Open" attribution, and any model-as-a-service business pulling more than $20M in annual revenue needs a separate agreement with Meta. For a hobbyist that is noise. For a startup that wants to resell inference, it is a clause to read before you build. That gating is exactly why critics keep comparing Muse to the MIT and Apache releases out of China, where there is no revenue tripwire at all.

The reception reflected that. Teortaxes called the release "DOA" against the smaller, MIT-licensed DeepSeek V4.1 Flash, a model that costs a third as much per task on AA's own numbers. Alexandr Wang took the other side, describing Meta's lab as having built "a machine that builds new models," a nod to the training infrastructure more than the single checkpoint. Both can be true: a strong pipeline, a middling result for agents.

Video: Meta's 816B open model

A rundown of what Meta shipped, the 816B MoE architecture, and how the open-weight claim holds up against the Chinese models it is chasing.

What it means if you pay for AI

For day-to-day use the answer is: not much changes yet, and the right use is narrow. You cannot run 816B parameters at home, so "open weights" here means cheaper hosted access through a provider once you find one serving it, not local inference. Where Muse Spark Open earns a slot is high-breadth knowledge work that it can answer in few tokens, which is why its cost-per-task reads well despite the headline price. Where it does not earn a slot is anything agentic, coding loops, tool chains, long-horizon tasks, where it finishes last among open peers and models like DeepSeek V4.1 Flash cost less and behave better.

That split is the whole argument for routing by task instead of marrying one subscription. Keep a frontier closed model for the hard reasoning, send cheap knowledge queries to a model like this, and point coding work at something built for it. Running everything through one aggregator is how you switch between them without paying for a stack of separate plans. Before you move any real workload, wait for a provider you trust to serve it and check the agentic numbers against whatever you use today.

FAQ

Can I run Muse Spark Open on my own machine?

Not realistically. BF16 weights are about 1.6TB, and even Meta's quantized NVFP4 build is roughly 530GB, which needs four Blackwell Ultra GPUs, eight H200s, or two 512GB Mac Studios. In practice you rent it from a provider rather than self-host.

Is it better than DeepSeek or Qwen?

On raw knowledge it leads US open models, but independent evals place it #12 among open-weight models overall, and it is last among the top nine open models on agentic tasks. Cheaper Chinese models like DeepSeek V4.1 Flash beat it on cost per task and behave better in agent loops.

What is the catch with the license?

It is not Apache 2.0. You have to credit "Built with Muse Spark Open," and any service making more than $20M a year from it needs a separate deal with Meta. That revenue gate is the main knock against it versus the MIT and Apache open models.

Sources

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