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Meta’s Muse Wonder Full beats GPT-6 Astra on two coding benchmarks in Meta’s published comparison, while Astra scores higher on a terminal benchmark and a health evaluation. The results support a narrower claim than “better overall”: which model leads depends on the task and evaluation. Muse Wonder Full is also offered differently from Muse Wonder, with Full available through certain Muse Code subscriptions and no per-token API rate listed for it.
Where Muse Wonder Full leads—and where Astra does
Meta’s 2026 comparison shows Muse Wonder Full ahead on DeepSWE 1.1 and SWE-Marathon. GPT-6 Astra leads on Terminal-Bench 4.0-AA and HealthBench Pro. The scores below are from Meta’s table; higher is favorable within each named evaluation, but scores from different benchmarks should not be compared directly. Meta’s official model and benchmark page provides the comparison.
| Benchmark | Muse Wonder Full | GPT-6 Astra | Higher score |
|---|---|---|---|
| DeepSWE 1.1 | 79.0 | 74.1 | Muse Wonder Full |
| SWE-Marathon | 57.5 | 42.5 | Muse Wonder Full |
| Terminal-Bench 4.0-AA | 57.1 | 59.1 | GPT-6 Astra |
| HealthBench Pro | 62.3 | 65.3 | GPT-6 Astra |
That pattern makes “beats Astra in key tests” accurate only when the tests are named. The coding results favor Muse Wonder Full on these two evaluations, but they do not establish that it is the stronger choice for every coding task, agent workflow, or user.
What other comparisons add
VentureBeat reported Artificial Analysis Intelligence Index scores of 56 for Muse Wonder Full and 53 for GPT-6 Astra; Claude Opus 5.5 scored 58. The index offers an aggregate point of comparison, not a replacement for task-specific results. VentureBeat also reported Meta’s Harvey LAB-AA legal-agent scores: 13.1 for Muse Wonder Full and 8.6 for Astra. These figures are reported results, and should be read as evidence about those evaluations rather than a guarantee of performance in legal work. VentureBeat’s October 13, 2026 report covers the index and legal-agent comparison.
A separate hands-on comparison by Pat Simmons tested four builds—a website design, an ad campaign, an iPhone app, and a 3D simulation—and ranked Muse Wonder Full fourth in the blind rankings for all four. The author says the models ran in their respective labs’ coding agents, so the outcome reflects that setup as well as the models. Simmons also estimated Muse Wonder Full’s token costs using regular Muse Wonder API rates because Meta did not publish a per-token Full rate. This is one reviewer’s task-specific result, not a universal benchmark. The October 12, 2026 review describes its method and findings.
What Muse Wonder Full is and how to access it
Meta describes Muse Wonder Full as its most advanced model and positions the Wonder family for personal and professional agentic tasks. Meta lists a 1-million-token context window for both Muse Wonder Full and Muse Wonder. A context window is the amount of text or other supported input a model can consider in a given interaction; it does not by itself show how accurately the model remembers information or completes a task.
- Muse Wonder Full: listed for Muse Code subscribers on the Power Usage and Maximum Usage plans.
- Muse Wonder: listed for the Meta Model API and Muse Code.
The official page listed Power Usage at $50 per month and Maximum Usage at $200 per month on October 13, 2026. These are subscription prices observed on that date and may change. Meta lists Muse Wonder API rates of $4 per million input tokens, $0.20 per million cached input tokens, and $20 per million output tokens. Those rates apply to Muse Wonder; Meta does not list a per-token API price for Muse Wonder Full on the cited page. Check Meta’s current offering and pricing before choosing a plan.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →TechCrunch reported that both models would also power the Muse agent and highlighted Meta’s focus on “active memory.” In the launch video, Meta research scientist Kavya Kopparapu said the team worked to help the model remember what matters to a person over “days, weeks, months, and beyond.” That describes Meta’s stated product priority, not independent validation of long-term memory or its results. TechCrunch’s October 12, 2026 launch report provides that context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to decide which results matter to you
Use the benchmark that resembles your actual work as a starting point, then verify performance in the environment where you intend to use the model. A coding score, terminal score, health score, legal-agent result, and blind build comparison measure different things; none alone establishes a universal winner.
- For the specific coding evaluations listed: Muse Wonder Full leads DeepSWE 1.1 and SWE-Marathon in Meta’s comparison.
- For terminal and health evaluations listed: Astra leads Terminal-Bench 4.0-AA and HealthBench Pro.
- For legal-agent evaluation: VentureBeat reports higher Meta results for Muse Wonder Full on Harvey LAB-AA.
- For hands-on build work: Simmons’s four comparisons ranked Full last, but the sample and coding-agent setup make that result a limited, separate perspective.
- For access and cost: Full is listed under two Muse Code subscription tiers; the published per-token rates are for Muse Wonder, not Full.
Before committing to a model, check the current availability, price, and evaluation setup, then test representative tasks with the tools, prompts, and constraints you expect to use. Benchmark leadership is useful evidence, but it is not a substitute for a workload-specific comparison.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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