Meta’s Muse Wonder Full scores higher than GPT-6 Astra on two reported coding benchmarks, but Astra leads on two other evaluations. The results support a test-by-test comparison—not a claim that Wonder Full is categorically better. Here are the scores, what they do and don’t show, and how access differs between the models.
Where Muse Wonder Full leads—and where Astra does
Meta’s 2026 benchmark table shows Wonder Full ahead on DeepSWE 1.1 and SWE-Marathon. GPT-6 Astra scores higher on Terminal-Bench 4.0-AA and HealthBench Pro. Scores are meaningful within each named evaluation; they should not be compared across different benchmarks as though they measured the same capability.
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| Benchmark | Muse Wonder Full | GPT-6 Astra | Higher reported 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 |
These are Meta’s published results, not an independent replication. They suggest that the choice can depend on the task: Wonder Full has the edge in the two listed coding evaluations, while Astra leads on the terminal and health evaluations shown. The benchmark labels alone do not establish how either model will perform on a particular project or in a different tool setup. Meta’s model and benchmark page
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How the broader comparisons change the picture
A single aggregate score offers another perspective, but it does not settle every task-level comparison. VentureBeat reports Artificial Analysis Intelligence Index scores of 56 for Muse Wonder Full and 53 for GPT-6 Astra; Claude Opus 5.5 scores 58 in the same reported comparison. The article also reports Meta’s Harvey LAB-AA legal-agent results: 13.1 for Wonder Full and 8.6 for Astra. These figures describe different evaluations and should not be treated as interchangeable measures of general quality. VentureBeat’s report
#1 Best Overall
An independent hands-on comparison points in a different direction for its chosen tasks. Pat Simmons tested four builds—a website design, an ad campaign, an iPhone app, and a 3D simulation—and placed Wonder Full fourth in blind rankings for all four. The reviewer says each model ran in its own lab’s coding agent, so the result reflects that particular setup as well as the models. It is one author’s task-specific comparison, not a universal benchmark or proof that the model will rank last for other work. Simmons also estimated Wonder Full token costs using regular Muse Wonder API rates because Meta does not publish a per-token API price for Wonder Full. Read the hands-on review
What Muse Wonder Full is and how to access it
Meta describes Muse Wonder Full as its most advanced model in the Muse Wonder family, which is presented for personal and professional agentic tasks. Meta lists a 1-million-token context window for both Muse Wonder Full and Muse Wonder. A large context window is a product specification; it does not, by itself, show that a model will remember every detail accurately or deliver better results for a given task. Meta’s product page
| Model | Listed access | Published price information |
|---|---|---|
| Muse Wonder Full | Muse Code subscriptions: Power Usage and Maximum Usage | $50 and $200 per month, respectively; no per-token API price listed |
| Muse Wonder | Meta Model API and Muse Code | $4 per million input tokens, $0.20 per million cached input tokens, and $20 per million output tokens |
These are the prices and access details Meta listed on October 13, 2026, and may change. The per-token rates belong to Muse Wonder, not Muse Wonder Full; do not use them as an official Full API price. The Full model is listed as subscription-only in Muse Code, on the two plans above. Check Meta’s current availability and pricing
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsWhat Meta’s memory emphasis does—and doesn’t—establish
Meta says both models will also power its Muse agent, and has highlighted “active memory” as a focus. In a launch video reported by TechCrunch on October 12, 2026, Meta research scientist Kavya Kopparapu said: “We put a tremendous amount of effort into making sure your model can remember what’s important to you over days, weeks, months, and beyond.” This describes Meta’s product priority; it is not independent evidence that memory performance has been validated over those periods. TechCrunch’s launch coverage
Quick Recap
How to choose between the models
- Match the test to the work. The reported coding, terminal, health, and legal-agent results point in different directions. Look for evaluations that resemble your task and tool environment.
- Separate benchmark results from agent results. A model score does not guarantee the same ranking when models operate inside different coding agents or workflows.
- Check access before budgeting. Meta lists Muse Wonder Full on two Muse Code subscription plans, while the published per-token API rates apply to Muse Wonder.
- Run a representative trial if the decision matters. Use the same prompts, tools, success criteria, and review process for each model; compare quality and total workflow cost rather than relying on one headline score.
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.
