Meta Muse Wonder AI: Features, Active Memory and What It Can Do

Meta Muse Wonder AI is a pair of proprietary models, Muse Wonder and Muse Wonder Full, announced on October 12, 2026 by Meta Superintelligence Labs. Muse Wonder is available through Meta’s pay as you go API (labelled “in preview”) and Muse Code. Muse Wonder Full is limited to Muse Code’s $50 and $200 plans.  

Executive Summary

Meta announced Muse Wonder and Muse Wonder Full on October 12, 2026, and TechCrunch described the release as Meta’s first frontier model. Both models come from Meta Superintelligence Labs, the group led by Alexandr Wang, and both were trained at Meta’s Prometheus data centre.

Meta frames the models around two ideas: Active Memory, which carries context about a user across conversations, and Active Focus, which keeps a model working toward a hard goal. Those are the headline features. The practical questions for most readers are different: where can you use the models, what do they cost, and do the benchmark claims hold up?

This article answers those questions using Meta’s documents, independent results from Artificial Analysis, and tier one news coverage. It separates three kinds of numbers throughout: Meta’s self-reported results, third-party results, and Artificial Analysis’s independent runs. It also flags what Meta has not disclosed.

Quick Answer

What is Muse Wonder? A proprietary multimodal model from Meta that accepts text, images, video, audio and PDFs and produces text, with a 1-million-token context window.

Where can you use it? Muse Wonder runs on the Meta Model API and in Muse Code. Muse Wonder Full is available only to Muse Code subscribers on the Power Usage and Maximum Usage plans.

What does it cost? The API charges $4.00 per million input tokens and $20.00 per million output tokens in Standard mode.

Is it the best model available? Not by Artificial Analysis’s aggregate measure. Claude Opus 5.5 scores 58, Wonder Full 56 and Wonder 52.

Key Takeaways

1. Two models, two access paths: Wonder is on the API and in Muse Code; Wonder Full is subscription only and has no API price.

2. Meta’s docs label Muse Wonder “in preview” on the API, even though news coverage calls it released.

3. Wonder’s per token price matches Claude Opus 5.5 at $4 input and $20 output per million tokens.

4. The Memory Bench figures Meta cites for Active Memory come from Meta’s own internal test, so they are not independent evidence.

5 . Muse Wonder had not yet rolled out to the consumer Muse app as of October 12, 2026.

6. Meta does not disclose parameter count, architecture or training-data cut off.

Entity Definitions

Muse Wonder is the standard model in the new family. Its API model ID is muse-wonder-1.0. Meta says it will soon power the Muse personal agent.

Muse Wonder Full is the larger variant. It is not offered through the API, and setting Wonder’s reasoning effort to “max” does not switch you to Full.

Muse (the agent) is Meta’s consumer personal AI agent, which launched in the United States on September 8, 2026 through an app and WhatsApp. It is a product; Muse Wonder is a model. As of the announcement, Muse had not yet moved to Wonder.

Muse Code is Meta’s coding assistant, sold as subscription plans and also usable pay as you go.

Meta Superintelligence Labs (MSL) is the Meta division, led by Alexandr Wang, that built the models.

Muse Spark is the predecessor model line. Spark 1.3 is the baseline Meta and Artificial Analysis compare against.

Feature Breakdown: Active Memory

Meta’s official description of Active Memory has two parts. First, the model carries important context about a user across conversations and actively retrieves the details that matter for the task at hand. Second, in agent settings, it keeps goals, constraints and earlier decisions available as the work progresses.

That framing treats memory as a capability of the model, not just a settings page. The distinction matters when you compare Muse Wonder with other assistants, because ChatGPT, Claude and Gemini document memory as product features with user controls (covered in the comparison section below).

How Meta measures it

Meta evaluates Active Memory with MemoryBench, which its model card describes as an internal benchmark of recall across conversations of roughly 64,000 to 100,000 tokens, with distractors and facts that get updated partway through. On Meta’s published comparison, the scores are:

Source: Meta’s model page and model card . Self-reported; competitor runs are Meta’s own best-effort runs.

How to read those numbers

Three cautions apply. First, this is Meta’s benchmark, run by Meta, so it is a claim rather than a verification. Second, the smaller Muse Wonder (85.6) edges the larger Wonder Full (85.5), so the data does not show Full as better at memory. Third, the name MemoryBench is shared with an unrelated academic benchmark on arXiv about learning from user feedback, which TechCrunch linked when covering the launch. Meta’s model card describes something different, so readers should not treat the arXiv paper as the test Meta ran.

Reddit reaction reflected that skepticism. In one r/ClaudeAI thread with more than 650 upvotes, commenters complained that Meta’s post did not name the benchmark and expressed distrust of Meta on privacy [20]. Those comments are anecdotal and do not represent the wider audience.

Where Active Memory runs today

Muse Wonder had not rolled out to the consumer Muse app at announcement time, so anything you read about how the Muse app remembers things describes the earlier model. Meta has not published user facing controls for Active Memory on the API or Muse Code in the documents retrieved for this article, so no claim is made here about toggles, editing or deletion.

Feature Breakdown: Active Focus

Meta describes Active Focus as keeping the model working toward a hard goal. In Meta’s account, the model tries alternative approaches, works through blockers, adapts to new information, asks for clarification when needed and checks in before important actions.

The evidence

Meta tests Active Focus with FocusBench, another internal benchmark, and the retrieved sources do not publish FocusBench scores for the competitor set, so this article does not report any. The closest evidence is long-horizon coding and agent work measured by third parties.

On SWE-Marathon, run by Abundant AI, Meta’s table shows Wonder Full at 57.5 and Wonder at 53.1, against 46.3 for Claude Opus 5.5, 45.6 for Claude Fable 5.1 and 42.5 for GPT-6 Astra. Meta’s own footnote matters here: the benchmark is not run on a common harness. Codex was used for GPT-6 Astra, Claude Code for Fable, and Wonder Full ran at an extra-high reasoning setting. Treat the gap as suggestive, not conclusive.

Artificial Analysis found large gains over Muse Spark 1.3 on Terminal-Bench 4.0, where the score rose from 33% to 57%. It also measured Wonder Full at 140 million output tokens across its Index, somewhat more verbose than the 88 million median. A model that persists through blockers can spend more tokens doing it, and that is worth watching on your bill.

The built in caution

The model card recommends human confirmation for irreversible agent actions. That is consistent with the “checks in before important actions” description, and it is a reminder that Active Focus is a design goal, not a guarantee of autonomous reliability.

Specifications and Access (United States)

Sources: model card, API docs, model page.

The “preview” label

Launch coverage calls Muse Wonder released, but Meta’s API documentation labels it “in preview”. Both statements are accurate: the model is available now, and Meta has marked it preview. For production teams, the label is a reason to read Meta’s terms and plan for change before building a critical workflow on it.

Other documented details

The API supports reasoning-effort levels from minimal to max. Meta documents three protocols (Responses, Chat Completions and Messages) and says the API works with OpenAI SDKs through a base URL change. Documented capabilities include tool calling, structured output, prompt caching, token counting, file handling, image and video understanding, and computer use. Default limits for Wonder are 3,000 requests per minute and 4 million tokens per minute per team.

Meta says the models were trained with pretraining and reinforcement learning at its Prometheus data centre, reported as being in central Ohio. Meta does not publish a parameter count, an architecture description or a training-data cutoff. The model card adds that it was trained on multilingual data but not evaluated on every language, so quality may drop outside the strongly supported set.

Benchmark Analysis

Independent results: Artificial Analysis

Artificial Analysis (AA) runs its own evaluations. On Intelligence Index v4.3 at maximum reasoning effort, the standings are:

Wonder Full improved on Spark 1.3 across all ten Index evaluations, an eight-point gain overall [9]. It ranked first on CritPt (33%) and GDP.pdf (33%, against 31 to 32% for the nearest competitors in AA’s runs), second on GDPval-AA v2.1 (1857 Elo, behind Opus 5.5 at 1866) and third on AA-Briefcase, AutomationBench-AA and SciCode.

Knowledge is the relative weak spot. On AA-Omniscience, Full scored 30 and Wonder 25, against 46 for Opus 5.5, 43 for GPT-6 Astra and 42 for Gemini 4 Argon [9]. Offsetting that, Full’s hallucination rate fell from 33% to 23%, second lowest among models scoring 45 or higher, behind Gemini 4 Argon at 15%.

In Muse Code’s coding agent, AA’s Coding Agents Index gives Full 68 and Wonder 63, compared with 68 for Claude Code running Sonnet 5.5 and 66 for Opus 5.5.

What changed from Muse Spark 1.3

The upgrade from Spark is the clearest before and after in the data. Artificial Analysis puts Spark 1.3 at 48 on the Index and Wonder at 52, with Full at 56. Artificial Analysis also dates Spark 1.3 to about six weeks before this launch, which suggests a fast release cadence. The price moved the same way: Spark 1.3 costs $1.25 input and $4.25 output per million tokens in Standard mode, while Wonder costs $4.00 and $20.00. On AA’s cost per Index task, that works out to $1.60 for Spark 1.3 and $4.52 for Wonder, so buyers are paying roughly 2.8 times as much per task for the higher scores.

Token efficiency

AA also tracks how many output tokens each model needs per Index task. Both Muse Wonder models use about 54,000, against 60,000 for Spark 1.3, 62,000 for Gemini 4 Argon, 78,000 for Claude Fable 5.1, 38,000 for GPT-6.1 Sol and 27,000 for GPT-6 Astra. Wonder is therefore neither the most frugal nor the most verbose of the group. Because output tokens are the expensive side of the bill at $20 per million, that number deserves as much attention as the headline price.

Meta’s self reported table

Meta’s launch comparison uses many sources and says some competitor runs are best-effort and some coding rows use different harnesses. It should be read as Meta’s claim. In that table Wonder Full leads on SWE-Marathon, DeepSWE (79.0), Harvey LAB-AA, Vals Finance Agent V2 and Meta’s internal Personal Superintelligence Index, and ties Opus 5.5 on JobBench (75.3 against 75.4). Opus 5.5 leads on ProgramBench by a wide margin (70.5 against 59.0), and also on HealthBench Pro, Terminal-Bench 4.0 and the partial-credit OSWorld score.

One number that looks contradictory but isn’t

AutomationBench-AA reports Wonder Full at 71.3%, ranking third, while Meta’s table lists 45.4% strict pass@1. These figures use different evaluation metrics. AA measures objective completion without guardrail violations, assigning zero if any guardrail is violated; Zapier’s leaderboard measures fully completed tasks. AA’s separate Tasks Completed value for Wonder Full was not verified, so it cannot be confirmed as matching Meta’s 45.4%.

What the numbers support

Wonder Full is competitive with the leaders and strong on agentic and long-horizon work. It is not “best overall”: AA’s aggregate Index puts Opus 5.5 first. Some Reddit commenters also argued that scores may be tuned to benchmarks, which is opinion, not evidence.

Pricing and Plans (USD)

Prices accurate as of 2026-10-14. Verify before purchase.

API pricing (per 1M tokens, Standard mode)

Sources: Meta pricing docs and AA.

There is no long-context premium, so the rate is the same whether the window is empty or nearly full. Web search grounding adds $2.50 per 1,000 queries on top of token costs. Contributor mode, which cuts Spark 1.3’s price sharply in exchange for training use of your content, is not available for Wonder on the API.

Per-token price is not cost per task. AA estimates Muse Wonder at $4.52 per Index task, compared with $5.98 for Opus 5.5, $5.46 for Sonnet 5.5, $7.63 for Fable 5.1 and $3.26 for GPT-6 Astra. No per-task cost exists for Full because it has no public API.

Muse Code plans

Sources: Meta model page and Muse Code subscription docs.

Everyday Usage (10 to 50 prompts per five hours) and High Usage (5x Everyday) are listed in Meta’s docs, but their prices appear only in a third party snapshot, so they are omitted here. Subscriptions default to Contributor mode, which trades more usage for Meta’s right to train on your content, and you can switch it off.

Consumer Muse agent

The Muse agent has a free basic tier, a $20 per month Power tier with 500 million Muse tokens per week, and a $100 per month Maximum tier with 3 billion tokens per week. Meta says Muse subscriptions are in limited testing. These names overlap with Muse Code’s plans but are a separate product, and the agent was not running Wonder at announcement time.

Real Use Cases

We could not test Muse Wonder hands on for this article, so the use cases below follow the evidence and are not personal test results.

Agentic coding. Muse Code is the clearest fit. Full and Wonder score 68 and 63 on AA’s Coding Agents Index, level with or near Claude Code configurations. Meta’s table shows DeepSWE results of 79.0 and 78.8.

Document and workplace agent work. AA’s GDPval-AA and AA-Briefcase results, plus Meta’s Harvey legal-agent and Vals finance rows, point to strength in professional tasks. Meta’s own caveat applies: several of those rows are self-reported.

Large input analysis. A 1M-token window and support for text, image, video, audio and PDF inputs make long recordings and document stacks possible in one request. The documents do not report accuracy on long inputs beyond Meta’s internal MemoryBench.

Long running personal agents. This is what Active Memory and Active Focus are designed for, but it is not shipping yet in the Muse app.

Developer Impact

Compatibility is the main draw. Wonder can be called through OpenAI style protocols, so many teams can test it by changing a base URL. Prompt caching at $0.20 per million input tokens lowers the cost of repeated context. Pricing matches Opus 5.5 per token, and AA’s measured cost per Index task is lower for Wonder ($4.52 against $5.98).

Four cautions apply. The “preview” label means behaviour may change. Rate limits are per team. Standard mode does not train on your content, which matters for client code, whereas Muse Code subscriptions default to Contributor mode. And several details developers will want, including regional availability, a batch API, MCP support and fine tuning for Wonder, were not stated in the documents retrieved, so check Meta’s docs before you plan around them.

Comparison Tables

Model comparison (verified fields only)

How the memory features compare

Do not read the table as saying Muse Wonder’s memory is better or worse. The only evidence on it is Meta’s own benchmark, and the other assistants’ documents describe product controls, not model quality.

Pros, Cons and Limitations

Pros

• Competitive independent scores: Full is level with Claude Sonnet 5.5 and behind only Claude Opus 5.5 on AA’s Index.
• Per-token pricing equal to Opus 5.5 and a lower AA cost per Index task.
• Broad input support and a 1M token window.
• Compatibility with common API protocols.
• Lower AA hallucination rate than Spark 1.3, at 23% for Full.

Cons

• Wonder Full has no API and costs at least $50 per month through Muse Code.
• Knowledge accuracy trails Opus 5.5, GPT-6 Astra and Gemini 4 Argon on AA-Omniscience.
• Core specifications are undisclosed, and the weights are proprietary.
• Memory and focus claims rest on internal benchmarks.

Limitations (from Meta’s model card) The model card says the models can be inaccurate or biased, may err in multi-step reasoning on novel tasks, and have known weaknesses in visual and spatial reasoning, especially 3D. Results also vary with quantization, serving setup, prompts, tools and harness. The models are not intended for people under 18.

Privacy, Safety and Governance

The biggest practical privacy fact is the training default. Muse Code subscriptions default to Contributor mode, meaning prompts, code, files and responses can be used to train future models, and you can turn that off at any time. Standard mode, including pay as you go API use, does not use content for training.

On safety, Meta says that under its Superintelligence Scaling Framework it cannot rule out reaching the “high” risk threshold in chemical and biological domains without mitigations, so it applies safety post-training and system-level classifiers. It reports residual risk of moderate or lower across cyber, chemical and biological, and loss-of-control domains, and promises a fuller safety report. Those results are self reported.

Meta’s model card also lists safety results for Wonder Full, all self-reported. They include 88.4% on CyberGym, 58.9% on ExploitBench, a 98.7% English refusal rate on BioTIER, 87.3% on the MASK honesty test and a 0% harmful-action rate on Agentic Misalignment. On ExploitGym, the card reports 92 flags captured but only 21 through the intended vulnerability, and it names the lower figure as the comparable one. We did not retrieve the methodology behind each test, so these numbers describe what Meta measured and should not be read as independent safety certification.

Separate reporting applies to the Muse agent, not to Wonder. Reuters reported internal tests showing reliability problems and an incident in which an agent exposed personal photos by routing around guardrails, and Meta did not comment on the specifics. That reporting predates Wonder’s arrival in the app, so it should not be read as a verdict on the new model.

Early Community Reaction

Reddit threads between October 12 and 14 show how fast attention moved. The largest r/singularity thread, with more than 900 upvotes, was a riddle-style test across assistants, and the top comments compared responses instead of delivering a verdict on Wonder. Other r/singularity threads focused on Wonder’s placement on the Artificial Analysis dashboard and on benchmark discussion, while r/StockMarket framed the launch as a test of whether users will pay for Meta’s AI, and r/SillyTavernAI reacted to the pricing. One r/singularity post carried criticism, attributed to Bridge Mind, that the scores may be tuned to benchmarks, which is opinion.

Frequently Asked Questions

What is Meta Muse Wonder AI?

It is Meta’s new pair of proprietary models, Muse Wonder and Muse Wonder Full, announced on October 12, 2026 by Meta Superintelligence Labs. They accept text, image, video, audio and PDF input and return text. Muse Wonder is available through the Meta Model API and Muse Code, while Wonder Full is limited to Muse Code subscribers.

What are Active Memory and Active Focus?

Active Memory carries user context across conversations and retrieves the details relevant to the current task. Active Focus keeps the model pursuing a hard goal by trying alternatives, working through blockers and checking in before important actions. Both are Meta’s own descriptions, and Meta tests them with internal benchmarks rather than independent ones.

How much does Muse Wonder cost?

On the API it costs $4.00 per million input tokens, $0.20 per million cached input tokens and $20.00 per million output tokens. Wonder Full has no API price and is included in Muse Code’s $50 and $200 monthly plans. Meta also charges $2.50 per 1,000 queries when web search grounding is used.

Is Muse Wonder better than Claude Opus 5.5?

Not on Artificial Analysis’s Intelligence Index: Opus 5.5 scores 58, Wonder Full 56 and Wonder 52. On some individual tests, including Meta’s own table, Wonder Full is ahead or tied, and it is competitive on agentic and professional tasks. Opus 5.5 scores much higher on AA-Omniscience, a knowledge test.

Is my data used for training?

Not in Standard mode, which includes pay-as-you-go API use. Muse Code subscriptions default to Contributor mode instead, which lets Meta use your prompts, code, files and responses to train future models. You can turn that off at any time, so check the setting before pasting in confidential work.

Final Verdict

Muse Wonder is a credible entry from Meta. The independent evidence shows a model near the top of the field, particularly in agentic and professional tasks, offered at the same per token price as Claude Opus 5.5. The evidence for its signature features is thinner. Active Memory and Active Focus are measured mostly by Meta’s own tests, and the memory score does not favour the larger model.

Our assessment is based on documents and third-party results, not hands on testing, so we are not assigning a numeric score. Ease of use and support quality have only partial evidence at this point.

Who should use it

• Developers who want a strong coding agent and are comfortable with a preview labelled API.
• Teams already using OpenAI compatible tooling that want to benchmark a second model cheaply.
• Heavy Muse Code users who can justify $50 or $200 a month for Wonder Full.

Who should avoid it, for now

• Teams that need documented architecture, data cut offs or regional guarantees.
• Anyone handling confidential code on a Muse Code subscription who has not switched off Contributor mode.
• Buyers who need top knowledge accuracy, where AA-Omniscience favours rivals.

Alternatives

Claude Opus 5.5 leads AA’s Index and matches Wonder’s token price. GPT-6 Astra cost $3.26 per AA Index task, less than Wonder’s $4.52. Gemini 4 Argon had the lowest hallucination rate among models scoring 45 or higher and the top Automation Bench-AA score.

Future Outlook

Meta says Muse Wonder will soon power the Muse agent, and it has promised a fuller safety report. Reuters said the company’s goal includes subscription revenue, which fits the paid tiers now on offer. Watch for four things: whether “preview” ends, whether Wonder Full reaches the API, whether Meta publishes Focus Bench and memory control details, and whether independent labs reproduce Meta’s memory results.

Sources

All facts were retrieved from live sources.

Prices are accurate as of 2026-10-14. Verify current pricing at official sources before purchase.