muse-wonder-1.0-max: LiveBench scores and how to run it with BrainOutput
muse-wonder-1.0-max is a model whose vendor is not publicly stated. In the LiveBench release of 2026-06-25 (fetched 2026-10-13) it ranks #8 of 71, with a global score of 81.16.
What is muse-wonder-1.0-max?
muse-wonder-1.0-max is listed on LiveBench, a public, continuously refreshed benchmark that scores models on seven categories: reasoning, coding, agentic coding, mathematics, data analysis, language and instruction following. Its strongest category in this snapshot: data analysis (#4 of 71).
We verify the licence and whether its weights are available during the AI diagnostic, before recommending anything.
The model at a glance
| Vendor | Not publicly stated |
|---|---|
| Weights | Not publicly stated |
| Licence | We verify the licence during the AI diagnostic |
| Release date (per LiveBench) | — |
| LiveBench identifier | muse-wonder-1.0-max |
LiveBench scores, dated
Score and rank out of 71 models, per category, in the LiveBench release of 2026-06-25 fetched 2026-10-13.
LiveBench, release 2026-06-25 · livebench.ai · 71 models · fetched 2026-10-13
Overall rank in this LiveBench snapshot.
Global score: the mean of the seven categories.
Gap to #1, Claude Fable 5.1 Max Effort (83.41).
| Category | Score | Rank |
|---|---|---|
| Reasoning | 90.63 | #12 / 71 |
| Coding | 80.68 | #20 / 71 |
| Agentic coding | 62.73 | #10 / 71 |
| Mathematics | 96.47 | #8 / 71 |
| Data analysis | 81.62 | #4 / 71 |
| Language | 82.93 | #22 / 71 |
| Instruction following | 73.03 | #17 / 71 |
| Global (mean of the 7 categories) | 81.16 | #8 / 71 |
Method note: the global score is the mean of the seven category averages, computed by us from LiveBench's published file; it may differ by a few decimals from the site's own display. Equal scores keep LiveBench's file order. Benchmarks measure benchmark tasks — during an AI diagnostic we re-run the comparison on your own workloads.
Running muse-wonder-1.0-max with BrainOutput
BrainOutput routes each task to a model by confidentiality: sensitive work stays on a local or private model, and everything else goes to a cloud model only when your policy allows it.
Locally, on your hardware
Checked at diagnostic
Possible only if the weights are published: we verify the licence during the AI diagnostic, before any deployment.
In a private cloud
Checked at diagnostic
Possible only if the weights are published — verified during the AI diagnostic.
With your own key (BYOK)
Available
Plug in your own API key: BrainOutput routes non-sensitive work to it when your policy allows; sensitive work never leaves your perimeter.
Frequently asked questions
Is muse-wonder-1.0-max an open-weight model?
It is not publicly stated: we verify the licence during the AI diagnostic, before recommending anything. The highest-ranked open-weight model in this snapshot is DeepSeek V4.1 Flash Max Effort (#10, 81.11).
Can we use muse-wonder-1.0-max with BrainOutput?
Yes, through your own API key (BYOK), for non-sensitive work and only when your policy allows it. Sensitive work stays on an open-weight model running locally or in a private cloud.
Where do these scores come from?
From LiveBench (livebench.ai), release 2026-06-25, file fetched 2026-10-13. The global score is the mean of the seven categories, computed by us. The ranking changes monthly; we re-verify at diagnostic time.
Find out whether muse-wonder-1.0-max fits your work
An AI diagnostic maps your processes, classes what is sensitive, and benchmarks current models against your real tasks — dated numbers, yours this time.
Sources
- LiveBench — leaderboard, release 2026-06-25 — accessed 2026-10-13