muse wonder vs claude opus 5.5: is meta's new model better?

as of october 2026, our answer to muse wonder vs claude opus 5.5 is opus, as a general default. meta launched muse wonder and muse wonder full on october 12, 2026, and gave muse wonder the same per token list price anthropic charges for opus 5.5.

but in meta's own 14 row benchmark table, the api model beats opus 5.5 on only five rows, led by two long horizon coding tests. the stronger muse wonder full beats opus on eight, and you can only get it inside meta's muse code subscription, which lets meta use your work for training by default.

our verdict: keep opus 5.5 as your default model. trial muse wonder on long repo tasks, where its deepswe lead holds up. try wonder full only if you turn contributor mode off first.

what did meta launch on october 12?

meta released two models at once. muse wonder is available through meta model api and muse code, and muse wonder full is available only to muse code subscribers. meta calls them its most capable models yet, trained at its prometheus data center.

the pitch rests on two ideas. active memory carries important context about you across conversations and retrieves the details a task needs. active focus keeps the model working through blockers, while asking for clarification and checking in before important actions.

meta's models page lists muse wonder as a preview with a 1,048,576 token context window. it also states the catch plainly: wonder full is not on the pay as you go api, and setting reasoning effort to "max" on muse wonder does not select it.

how much does muse wonder cost?

muse wonder's input, output and cached input prices match claude opus 5.5's exactly. meta's pricing page adds that there is no long context premium. anthropic's pricing docs say the same for opus 5.5, which it released on september 22, 2026.

the token prices are standard rates, before batch discounts or fast modes. the openai figures come from openai's pricing page, which charges more for astra's long context tier. the muse code plan prices are from meta's help center.

the same sticker price does not mean the same bill. when artificial analysis ran its intelligence index, the run cost $5,733 on muse wonder and $8,708 on opus 5.5, and opus generated more output tokens along the way. our ai model pricing comparison covers the rest of the market.

where does muse wonder beat claude opus 5.5?

muse wonder's clearest lead is long horizon coding. the numbers below are from meta's developer post, at max reasoning effort unless marked. we picked 11 of meta's 14 rows; the five rows the api model wins are all here.

the deepswe row is the strongest evidence, because meta's model card says the muse and opus scores are third party runs by datacurve. the swe-marathon row is weaker. the card says it is not a controlled common harness comparison, and wonder full's score was reported at xhigh effort rather than max.

the legal row needs context too. harvey lab-aa counts only deliverables that meet every criterion and pass hallucination checks, so even the leader clears a small share of tasks.

where does claude opus 5.5 still lead?

opus 5.5 still wins most general work in meta's own chart. across all 14 rows, it beats the api version of muse wonder on nine, including meta's own personal superintelligence index, computer use, health questions and from scratch programming.

the independent number agrees. on the artificial analysis intelligence index, opus 5.5 is the top model in meta's table, and artificial analysis in a post dated october 12, 2026, calls both muse models a material jump from muse spark 1.3, which scored 48 and came out about six weeks earlier.

one row needs a footnote, because it disagrees with numbers on this blog. the terminal-bench 4.0 figures above are artificial analysis's runs. anthropic's own table puts opus 5.5 at 66.4%, the figure our best ai model for coding guide uses. either opus figure is higher than meta's table gives either muse model.

meta also warns that its own runs of rival models are best effort and may not be tuned for them. in our reading, that caveat can only flatter the muse models on the rows meta ran itself.

should you trust meta's memory claims?

treat them as promising but unproven. memorybench is meta's own test: long conversations of 64k to 100k tokens, with distractors and later updates to the facts, scored by an llm judge. muse wonder edges wonder full on it, the only row in meta's table where it outscores the full model.

in our view, memory is also where vendor lock in starts. active memory lives inside meta's products, the same way chatgpt and claude memory live inside theirs. our ai memory comparison explains why that matters when you switch models.

what does muse code do with your code?

by default, muse code subscriptions let meta use your work for training. meta's help center says plans start in contributor mode, which trades more usage for letting meta train future models on your prompts, code, files and the responses. you can turn it off in settings.

the api is stricter. meta's docs say muse wonder on pay as you go runs only in standard mode, where your content is not used to train meta models. contributor mode is also not available to zero data retention accounts.

in our view, that default matters more than any benchmark row for client or employer code. if you would not paste a repo into a training set, flip the setting before your first prompt. for why provider chat history is a poor home for that work anyway, see where your ai conversations go.

should you switch to muse wonder?

for most people, not yet, and here is how we would split the decision as of october 13, 2026:

• keep claude opus 5.5 as your default for general agent work, computer use and anything scored by an outside lab. same price, more wins.
• trial muse wonder on long, multi file repo tasks, where the datacurve deepswe runs give it a real edge. moving an openai sdk project is a base url change, according to meta.
• try muse wonder full if you already pay $50 a month for a coding agent and want the strongest swe-marathon score, but turn contributor mode off first.
• skip gpt-6 astra for this comparison. it costs more per token than muse wonder or opus 5.5 and trails both on deepswe.
• want it on your own mac? meta's open weight muse model is muse glimmer, covered in our best local llm for mac guide.

for the wider picture, see claude vs chatgpt vs gemini. in our view, expect the ranking to move again within weeks.

keep your context when the model changes

our take: the model you use this month will not be the one you use next quarter, and your working context should not live inside any one of them.

remynd is a mac app that keeps that record for you. it captures the focused window, runs ocr locally with apple vision, and keeps the searchable index on your mac. you can exclude apps and sites, and recordings are kept for 30 days by default.

its agent window runs your own claude code or codex cli against that history.

download remynd for mac and keep your memory when you change models.