{"article":{"slug":"meta-muse-wonder-launch","kind":"model","source":{"title":"Muse Wonder: A Frontier Model For Personal & Professional AI Agents","url":"https://research.meta.ai/blog/muse-wonder","date":"2026-10-12"},"headline":"Meta's Muse Wonder leads six of 14 benchmark rows and publishes no price","whatChanged":"Meta introduced Muse Wonder and Muse Wonder Full on October 12. Muse Wonder is available through the Meta Model API and Muse Code, and Wonder Full is available to Muse Code subscribers. Meta's announcement lists no prices and says a fuller safety report is coming. The model card lists a 1 million token context and says both models were run at max reasoning effort, except Wonder Full on SWE-Marathon at xhigh.\n\nMeta's table compares them with GPT-6 Astra, Claude Fable 5.1 and Claude Opus 5.5. On Harvey LAB-AA, Artificial Analysis's version of Harvey's legal benchmark, Wonder Full scores 13.1%, Wonder 10.0%, Astra 8.6%, Fable 6.4% and Opus 4.2%. On Vals Finance Agent V2 the models are close, 59.1 for Wonder Full and 58.9 for Fable 5.1. Opus 5.5 leads on HealthBench Pro (65.6 against 62.3), ProgramBench (70.5 against 59.0) and the Artificial Analysis Index (58 against 56).\n\nMeta chose the comparison and ran or selected much of the data. Its model card calls competitor runs best effort, not tuned for rivals, and describes the Personal Superintelligence Index as an internal composite.","whyItMatters":"Without a price, no cost comparison is possible yet. The scores that exist are mixed: by the table's shading Wonder Full leads six rows and Opus 5.5 also leads six, while Astra and Wonder lead one each. The rival models and benchmarks were Meta's picks, so the table shows where Meta wants attention.\n\nMeta says Active Memory carries context across conversations. A team evaluating that feature should test it with invented data first and ask where retained details are stored and how they are deleted.","issuer":"Meta","glance":"Meta released Muse Wonder October 12 through its Model API and Muse Code; the announcement lists no prices.","keyFact":"13.1% on Harvey LAB-AA; no price listed","action":"Test Wonder on one workflow with invented customer data and check what it recalls later.","visuals":[{"url":"https://research.meta.ai/_next/image?url=%2Farticles%2Fintroducing-muse-wonder%2Fimages%2Fharvey-lab-aa-light-v1.webp&w=3840&q=90","alt":"Bar chart titled Harvey LAB-AA: Muse Wonder Full 13.1%, Muse Wonder 10.0%, GPT-6 Astra 8.6%, Claude Fable 5.1 6.4%, Claude Opus 5.5 4.2%, all at max effort.","caption":"Meta's Harvey LAB-AA chart, October 12. Scores are the share of legal tasks whose deliverable meets every criterion and passes hallucination checks, per Meta's model card, with results sourced from Artificial Analysis. All models are labelled max effort. Even the top score leaves most tasks failing a check. The chart is Meta-published and does not state sample size.","credit":"Meta; results attributed to Artificial Analysis","source":{"title":"Muse Wonder: A Frontier Model For Personal & Professional AI Agents","url":"https://research.meta.ai/blog/muse-wonder","date":"2026-10-12"}},{"url":"https://research.meta.ai/_next/image?url=%2Farticles%2Fintroducing-muse-wonder%2Fimages%2Fmuse-evaluation-table-light-v6.webp&w=3840&q=90","alt":"Benchmark table of Muse Wonder Full, Muse Wonder, Claude Fable 5.1, GPT-6 Astra and Claude Opus 5.5 across 14 agent, coding and general rows.","caption":"Meta's full evaluation table, October 12. Wonder Full leads six rows, including Harvey LAB-AA, DeepSWE 1.1 (79.0) and Vals Finance Agent V2 (59.1). Claude Opus 5.5 leads six, including ProgramBench (70.5) and the Artificial Analysis Index (58). Settings are max effort except Wonder Full's SWE-Marathon at xhigh, and Meta's model card says rival runs are best effort.","credit":"Meta","source":{"title":"Muse Wonder: A Frontier Model For Personal & Professional AI Agents","url":"https://research.meta.ai/blog/muse-wonder","date":"2026-10-12"}}],"attachments":[{"title":"Muse Wonder Model Card","url":"https://research.meta.ai/static/muse-wonder-model-card","description":"Per-benchmark methodology and result sources, the max-effort setting, the limitation that rival runs are best effort, and Meta's risk assessment under its scaling framework.","source":{"title":"Muse Wonder: A Frontier Model For Personal & Professional AI Agents","url":"https://research.meta.ai/blog/muse-wonder","date":"2026-10-12"}}],"companyLabel":"Public edition"},"issue":{"id":"2026-10-19","publishedLabel":"Week of October 19, 2026","isLatest":true},"position":{"index":2,"total":5},"next":{"slug":"meta-muse-spark-open-weights","headline":"Muse Spark Open wins some rows, loses others, and needs about 1.6 TB of storage"},"session":null}