BlogLLM Benchmark Scores as Published, Side by Side

Muse Wonder Full: Benchmark Scores as Published

Every result the catalog holds for Muse Wonder Full: 17 tests in 8 areas, from 2 pages. All were printed by Meta itself. Each number is as printed; nothing is ranked or rescored.

Developer MetaReleased 2026-10-12Data version 2026103001, as of 2026-10-30

Knowledge Work

TestValuePrinted bySetup as printed
AutomationBench45.4%Meta, Meta Muse Wonder model cardchecked 2026-10-19Effort: max
JobBench75.3Meta, Meta Muse Wonder model cardchecked 2026-10-19Effort: max
Vals Finance Agent v259.1%Meta, Meta Muse Wonder model cardchecked 2026-10-19Effort: max
Harvey LAB-AA v1.1 (tasks meeting every criterion)13.1%Meta, Meta Muse Wonder model cardchecked 2026-10-19Effort: max

Agentic Coding

TestValuePrinted bySetup as printed
DeepSWE v1.179%Meta, Meta Muse Wonder model cardchecked 2026-10-19Effort: max
Terminal-Bench 4.057.1%Meta, Meta Muse Wonder model cardchecked 2026-10-19Effort: max
ProgramBench Almost@159%Meta, Meta Muse Wonder model cardchecked 2026-10-19Effort: max; Harness: mini-swe-agent
SWE-Marathon57.5%Meta, Meta Muse Wonder model cardchecked 2026-10-19Effort: xhigh

Long Context

TestValuePrinted bySetup as printed
MemoryBench (Meta internal)85.5%Meta, Meta Muse Wonder model cardchecked 2026-10-19Effort: max

Computer Use

TestValuePrinted bySetup as printed
OSWorld 2.1 partial78.4%Meta, Meta Muse Wonder model cardchecked 2026-10-19Effort: max
OSWorld 2.1 binary39.4%Meta, Meta Muse Wonder model cardchecked 2026-10-19Effort: max

Multimodal Understanding

TestValuePrinted bySetup as printed
GDP.pdf33.2%Meta, Meta Muse Wonder model cardchecked 2026-10-19Effort: max

Cybersecurity

TestValuePrinted bySetup as printed
CyberGym88.4%Meta, Meta Muse Wonder model cardchecked 2026-10-19-
ExploitBench58.9%Meta, Meta Muse Wonder model cardchecked 2026-10-19-

Healthcare

TestValuePrinted bySetup as printed
HealthBench Professional62.3%Meta, Meta Muse Wonder model cardchecked 2026-10-19Effort: max; Tools: none

Preference and Composite Rankings

Price

API list prices in US dollars per one million tokens, as printed on the developer's pricing page on the day checked. Cost per task is measured by Vals AI.

PriceValuePrinted bySetup as printed
Cost per taskVals AI, USD, lower is better$2.29Vals AI, Vibe Code Bench v1.1 leaderboardchecked 2026-10-27Harness: OpenHands

More from Meta

Every number here is read from the same catalog file as the benchmark table, data version 2026103001. Where one page printed the model under several setups, each value is listed. The catalog is public in armanobosyan/sugra-llm-benchmarks; a wrong number can be reported from the table, with Report an error under the value, or as an issue in that repository.