muse-wonder-1.0. Full is the most advanced model in the series and, for now, is available only through a Muse Code subscription. This page is about Wonder.
Meta names two parts of that behavior. Active Memory carries what matters across conversations and brings back the right detail when the task needs it. Active Focus stays on the goal when the task stalls: it tries another path, recovers from an error, asks for clarification, and checks in before an important action.
Artificial Analysis records that multimodal input on the max-effort variant. Meta has not published a parameter count.
When to use Muse Wonder and when to use Opus 5.5
The difference is the kind of action. Meta positions Wonder for repository coding, computer use, and tasks where the agent has to remember the context and recover from an error. Opus 5.5 stays ahead when the work needs open-ended judgment, a terminal, or a program written from scratch.What Meta measured
The numbers below come from Meta’s table at max effort. Muse Wonder Full scores higher on almost every row, and it is not the model on this page.Coding that crosses the repository
On DeepSWE 1.1, long-horizon coding, Wonder scored 78.8%. Claude Opus 5.5 scored 74.2% and GPT-6 Astra scored 74.1%. On SWE-Marathon, ultra-long-horizon engineering, the scores were 53.1%, against 46.3% for Opus 5.5 and 42.5% for Astra. On the terminal and on from-scratch programming, the order flips. On Terminal-Bench 4.0, Wonder scored 52.0%, behind Opus 5.5 (59.6%) and Astra (59.1%). On ProgramBench it scored 50.0%, well behind Opus 5.5 (70.5%).Memory, tools, and the screen
On MemoryBench, Wonder scored 85.6, ahead of Opus 5.5 (81.5) and Astra (81.4). On JobBench, professional tool use, it scored 70.5%, behind Opus 5.5 (75.4%) and ahead of Astra (66.1%). On OSWorld 2.1, computer use on the partial measure, Wonder scored 72.6%. Opus 5.5 scored 81.8% and Astra scored 75.8%. On the binary measure of the same test, Wonder and Astra tied at 34.3%. On domain work, Meta’s table is mixed. On Finance Agent v2, 56.6%, close to Opus 5.5 (58.6%) and above Astra (53.5%). On Harvey LAB, legal work from documents, 10.0%, above Opus 5.5 (4.2%) and Astra (8.6%): the whole band is still low. On HealthBench Pro, 60.5%, behind Opus 5.5 (65.6%) and Astra (65.3%).What the independent benchmarks show
In Artificial Analysis’s own measurement, the max-effort variant scored 52 on the Intelligence Index, ranking 9th among 216 models in the same class. The median for that price range is 27. In that run the model generated text at 104.1 tokens per second, above a median of 83.6, and took 19.73 seconds to the first token, against a median of 5.87 seconds. That time includes reasoning. Average cost was 4.52 dollars per index task, at a list price of 4 dollars for input and 20 dollars for output per million tokens.
On BenchLM, the partial aggregate is 70.44 out of 100, ranking 16th among 891 models. Coverage is still partial (21 benchmarks), so the score is conservative.
The table was published by Meta at max effort. The Intelligence Index, speed, latency, and cost per task are from Artificial Analysis on that same variant. The BenchLM aggregate uses partial coverage. Results depend on the harness, the tools, and the effort. Validate in your own workflow before changing a production setup.
Where it doesn’t lead
- On from-scratch programming, Opus 5.5 opens a wide gap (70.5% versus 50.0% on ProgramBench).
- On terminal coding and computer use, Opus 5.5 and GPT-6 Astra stay ahead.
- Time to first token rises while the model is reasoning. For a short classification, another model answers sooner.
- In Artificial Analysis, the max variant writes more than the median for the range: 130 million output tokens on the index, against 85 million. Ask for short answers when the format matters.
- Muse Wonder Full scores higher on Meta’s table. It is not the API model.
Where it adds the most value
A change that crosses several files
The fix does not fit in one file. Wonder walks the repository, edits in parallel, and checks the result before it stops. Expected result: the change you asked for, verified, with no extra file outside the scope.A task that stalls midway
The plan breaks on a tool, a source, or a step that will not close. The model tries another path, says where it stopped, and continues an important action only after you confirm. Expected result: the task finishes, or it stops with the blocker written down, with no silent action.A process that lives on the screen
The figure is in a system, a PDF, and a spreadsheet, and someone has to work across all three. Wonder sees the screen, joins the sources, and returns what it checked. Expected result: every number points to the screen or the file it came from.How to apply it in Tess
1
Pick a task with a screen, code, or several tools
Prefer a repository change, a process with several tools, and a check against the screen. For short classification at volume, another model answers sooner.
2
Lock what counts as done
State the outcome, the files that may change, and the test that proves it is finished. State what is out of scope.
3
Set tools and approvals
Enable only the tools you need. Require confirmation before sending, publishing, paying, or changing data.
4
Ask for an explicit recovery
Tell the agent to try another path when a step fails, and to stop, with the reason, if the second attempt fails too.
5
Sample before you let it run
Run one full case, check what changed, and only then repeat the flow.
Three playbooks to get started
1. A repository change with a check
Input: the repository, the expected behavior, and the tests that already exist.Tools: reading and editing files, running the project’s tests.
2. A process that can fail midway
Input: the goal, the sources, and the action that needs confirmation.Tools: the process integrations, read-only until confirmation.
3. A check between the screen and a file
Input: the PDF or spreadsheet and the system where the same figure appears.Tools: file reading and the system screen.
Limitations and best practices
- Muse Wonder Full is not this model. It leads Meta’s table, and access today is through a Muse Code subscription.
- A 1M-token context does not replace selecting information. Send only relevant content.
- List price on Meta’s API is 4 dollars for input and 20 dollars for output per million tokens. A cache read costs 0.20 dollars.
- Meta describes strong safeguards against chemical, biological, radiological, nuclear, and cyber misuse, and says defensive security work remains possible. A full safety report was still to come at launch.
- Review a sample before publishing, sending, or automating decisions.
- Keep human approval for financial, legal, clinical, or high-impact decisions.