Muse Wonder
Meta's newest frontier model for coding, long-running agents and computer use, released on October 12, 2026 through the Meta Model API and Muse Code. It ties for first on the LMArena text leaderboard, and a stronger Muse Wonder Full version is offered only to Muse Code subscribers.
MetaLanguage modelReleased October 2026Data checked October 18, 2026
Where people rank it
#1
Chat and everyday questions (tied), 3,247 votes
#2
Writing (tied), 710 votes
From blind votes on LMArena, where people pick the better of two anonymous answers. See the full tables on the AI leaderboard.
On paper
Fixed-test rankings from the benchmarks tab: Speed #2, Reasoning #7, Value #12.
Strengths and weaknesses
- Tied for first on the LMArena text leaderboard
- Fast for a frontier model (181 tokens per second on Artificial Analysis)
- Artificial Analysis Intelligence Index of 52.4 and 63.5 on the Coding Agent Index in Muse Code
- Very new, so its LMArena scores rest on few votes and have wide confidence intervals
- The stronger Muse Wonder Full is subscription-only in Muse Code and not sold per token
Best for
- Agent and coding work in Muse Code
- Fast frontier-level answers
- General chat
Every score we have
Each bar compares this model with the best result any model on this page has on the same test.
- LMArena text arena (human votes)1,508.9Arena score 1508.9 (95% CI 1498.3 to 1519.5); 3,247 votes; entry "Muse Wonder (Max)"; position 2 of 416 on the full board
- LMArena creative writing1,485.1Arena score 1485.1 (95% CI 1461.9 to 1508.2); 710 votes; entry "Muse Wonder (Max)"; position 8 of 414 on the full board
- LMArena WebDev (coding)1,650.4Arena score 1650.4 (95% CI 1635.7 to 1665.1); 1,942 votes; entry "Muse Wonder (Max)"; position 15 of 144 on the full board
- LMArena Agent Arena0.049IPS score 0.0494 (95% CI 0.0323 to 0.0664); 7,690 sessions; entry "Muse Wonder (Max)"; position 11 of 52 on the full board
- Artificial Analysis Intelligence Index52.452.4 on the Artificial Analysis Intelligence Index v4.3.2 (AA shows 52), setting "Muse Wonder (max)"
- Humanity's Last Exam (Artificial Analysis)50.1%50.1% on Humanity's Last Exam (AA, 2,158 text-only questions), setting "Muse Wonder (max)"
- Terminal-Bench 4.0 (Artificial Analysis run)52.0%52.0% on Terminal-Bench 4.0 as run by AA (mini-swe-agent harness), setting "Muse Wonder (max)"
- Long-context reasoning (AA-LCR)80.7%80.7% on AA-LCR (long-context reasoning), setting "Muse Wonder (max)"
- AA-Omniscience, accuracy minus hallucination25.525.5 on the AA-Omniscience Index (knowledge accuracy minus hallucination, range -100 to 100), setting "Muse Wonder (max)"
- Output speed, tokens per second181 t/s181 output tokens per second (AA median), row "Muse Wonder (max)"
- Cost per Artificial Analysis test task$4.52$4.52 to run one AA Intelligence Index task, row "Muse Wonder (max)"
- Artificial Analysis Coding Agent Index63.563.5 on the AA Coding Agent Index v1.5 (DeepSWE v1.1, Terminal-Bench 4.0, SWE-Atlas-QnA), best listed setup "Muse Code - Muse Wonder (max)"
Compare with
- Claude Opus 5.5Anthropic's flagship for long-running agentic coding and knowledge work, and the model Anthropic suggests starting with for most jobs.
- Claude Fable 5.1Anthropic's top tier for the hardest reasoning and long-horizon agent work, which Anthropic recommends when Opus 5.5 still falls short.
- Muse Spark 1.3Meta's mid-priced Muse model, trained for agentic workflows and tuned for competitive coding, available through the Meta Model API.
- Claude Sonnet 5.5Anthropic's mid-priced model, which Anthropic describes as its best mix of speed and intelligence.