Meta says its newest AI models have closed the gap with the biggest names in the field. The company has introduced a new family built to handle complex work for everyday users and developers.

It also claims the models can hold their own against frontier AI models from OpenAI and Anthropic.

Meta Debuts the Muse Wonder AI Family

Meta introduced Muse Wonder and Muse Wonder Full on Oct. 12, 2026, according to the company's newsroom post. The company calls them its most capable models to date, and together they form the new Muse Wonder series. They build on Muse Spark, the first model from Meta Superintelligence Labs, which Fortune reported was unveiled on April 8, 2026.

Meta says Muse Wonder is available to developers and builders through the Meta Model API and Muse Code. Muse Wonder Full is available to Muse Code subscribers. Muse Wonder will also power experiences in Muse, the company's personal AI agent, and subscribers will get it first.

The models are built for personal and professional agentic tasks, according to Meta. Its examples include handling an insurance claim, understanding a lease, making sense of finances, and building a working app.

Meta also said it trained, evaluated, and released the models under its Meta Superintelligence Scaling Framework. A full report with more safety evaluations is expected soon. The company says Muse Wonder is "our most aligned model to date, and alignment is central to how we build personal superintelligence."

Meta Caught Up With Rivals Thanks to Wonder

Engadget reports that Meta says it has caught up with the frontier after more than a year and tens of billions of dollars spent rebuilding its AI efforts. The outlet adds that Meta has said it may spend as much as $145 billion on AI this year. In an interview with the Financial Times, Mark Zuckerberg said the work required "a large amount of compute and investment, and a lot of very rigorous scientific work."

VentureBeat says Meta believes the models can outperform leading offerings from OpenAI and Anthropic in complex software engineering, legal work, business automation and long-running agent tasks. Zuckerberg described Wonder on X as a "much larger and more advanced class of models" than the earlier Muse Spark family.

The push follows a rough stretch for the company. Understanding AI reports that Meta released Llama 4 on April 5, 2025, and that independent benchmarks and users were unimpressed. Meta then released no new large language models for the following year, according to the same outlet.

Fortune reports that Meta paid $14.3 billion in June 2025 for a 49 percent nonvoting stake in Scale AI. It also hired Alexandr Wang, the Scale AI chief executive, as chief AI officer to lead Meta Superintelligence Labs.

How Does Wonder Help Meta Compete With Rivals' Frontier Models?

Meta's research blog names three evaluations behind its claims. They are Harvey's legal agent benchmark, FocusBench, and SWE-Marathon, a test of long-running software projects. The page does not include specific scores, and it compares the models with other frontier models without naming them.

VentureBeat published figures from Meta's own table. Muse Wonder Full scored 57.5 on SWE-Marathon, compared with 42.5 for GPT-6 Astra and 46.3 for Claude Opus 5.5. On a software engineering test called DeepSWE 1.1, it scored 79.0, ahead of 74.1 and 74.2. On the Harvey legal benchmark, it scored 13.1 against 8.6 and 4.2.

The same table shows rivals ahead in some areas. GPT-6 Astra and Claude Opus 5.5 both scored higher than Wonder Full on Terminal Bench and HealthBench Pro.

On the Artificial Analysis Intelligence Index, Claude Opus 5.5 scored 58, and Muse Wonder Full scored 56. Claude Sonnet 5.5 also scored 56, while GPT-6 Astra scored 53 and Muse Wonder scored 52. VentureBeat notes that the comparison mixes third-party, vendor, and Meta-run results, and some coding tests used different tools.

Meta says it trained the models at its gigawatt-scale Prometheus data center. It adds that they build on larger and stronger base models than Muse Spark 1.3, and it says Wonder Full beats the Muse Spark 1.3 trend on unfamiliar tasks. Meta also acknowledges that the models can still make mistakes on unfamiliar multistep tasks.

Frequently Asked Questions

What Is an AI Frontier Model?

Built In describes a frontier model as an AI system operating at the current leading edge of capability. The category moves as newer systems appear, so today's frontier model can become ordinary software later.

Training one takes massive computing resources and costs tens to hundreds of millions of dollars, according to Built In. It cites Stanford's AI Index estimates of about $78 million for GPT-4 and about $191 million for Gemini Ultra. These models handle ambiguous and complex tasks such as multistep reasoning and writing functional code.

Only a small number of well-funded labs can afford that cost, the outlet says. Labs release closed models through an API or open weight models with published weights. VentureBeat says both Muse Wonder models are proprietary, with no downloadable weights.

What Is the Meta Wonder AI Family?

The Muse Wonder series includes Muse Wonder and Muse Wonder Full, which Meta calls its most capable models yet. The company has not disclosed model sizes, and its research blog says only that they use larger and stronger base models than Muse Spark 1.3.

VentureBeat says Muse Wonder costs $4 per million input tokens and $20 per million output tokens through the API. Muse Wonder Full is not offered through the API and is sold through Muse Code subscriptions at $50 a month for Power Usage and $200 a month for Maximum Usage.

Data handling differs by product. Meta says the API does not use submitted content for training. Muse Code subscriptions default to a contributor mode that lets Meta use prompts, code and outputs to improve future models, and users can turn it off for future submissions.

What Are the Meta Muse Wonder's Features?

Active Memory carries important context across conversations and retrieves the details relevant to the task at hand, according to Meta. Active Focus keeps the model working toward a hard goal by trying other approaches, working through blockers and adapting to new information. It also asks for clarification when needed and checks in before important actions.

Meta says the models bring expert level reasoning in law, finance and health, along with frontier coding performance. VentureBeat adds that they accept text, image, audio and video inputs and produce text outputs, with a context window of about 1 million tokens.

Meta says it trained the models to avoid sharing private information without permission and to refuse requests to help with harmful acts. The company also says it built its strongest safeguards to date against chemical, biological, radiological, nuclear and cyber misuse.