[whatmodel.sh](../../README.md) / Muse Wonder # Muse Wonder Meta · released 2026-10 · closed weights **score 78.3** ±0.7 · #17 of 732 · from 13 benchmark results across 3 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Muse Wonder among every model on that task or test, worst to best. 
task            vs the others         rank  counts  tests
agent tasks     ▁▁▃▆█▄▅▅▄●▂▁    #21 of 313     15%      3
coding          ▁▁▁▃▅▇█▇▅▄●▁   #17 of 469*    9.0%      1
chat & writing  ▁▁▁▃▆▄▆█▆▇▇●    #4 of 402*    5.1%      1
math            ▁▁▃▅█▆▄█▄▂●▁    #15 of 431     36%      3
agent tasks · #21 of 313 · 15% of its score Trails [DeepSeek V4.1 Flash](deepseek-v4-1-flash.md), [Claude Opus 4.8](claude-opus-4-8.md), [DeepSeek V4.1 Flash 1017](deepseek-v4-1-flash-1017.md) here, though they rank lower overall. 
test                                    result  vs the others in its run  difficulty      counts  why
Arena Agent (October 2026 aggrega…  0.0314 IPS  ▁▁▁▃▃▂●▁▂▂▁▁ beats 44/59  avg -0.379 IPS     13%  sharp
JobBench (maker)                         70.5%  █·█····●···█ beats 2/3    hard · avg 25%    1.5%  at its level
OSWorld 2.1 (partial score) (make…       72.6%  ●█·····█··██ beats 0/4    hard · avg 28%    1.0%  at its level · small run
coding · #17 of 469 · 9.0% of its score Beats [GPT-5.6 Sol](gpt-5-6-sol.md), [Claude Fable 5](claude-fable-5.md) here, though they rank higher overall. Trails [Qwen3.8 Max (0902)](qwen3-8-max-0902.md), [Muse Spark 1.3](muse-spark-1-3.md), [Qwen3.8 Max (2.4T-A95B)](qwen3-8-2-4t-a95b.md) here, though they rank lower overall. 
test                       result  vs the others in its run    difficulty  counts  why
Arena WebDev (Code Arena)  1652.1  ▁▁▃▂▅█▃▆▅●▁▂ beats 112/129  avg 981       9.0%  sharp
chat & writing · #4 of 402 · 5.1% of its score Beats [GPT-6 Astra](gpt-6-astra.md), [GPT-6.1 Sol](gpt-6-1-sol.md), [Claude Fable 5.1](claude-fable-5-1.md) here, though they rank higher overall. 
test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1507.1  ▁▁▁▂▃▃▃▆▆▇█● beats 382/385  avg 1260      5.1%  blunt
math · #15 of 431 · 36% of its score Beats [GPT-5.4 Pro](gpt-5-4-pro.md) here, though they rank higher overall. Trails [GPT-5.6 Terra](gpt-5-6-terra.md), [GPT-5.5](gpt-5-5.md) here, though they rank lower overall. 
test                       result  vs the others in its run    difficulty            counts  why
FrontierMath tier 4 v2      70.7%  ▁▂▁▂█▅▂●▂▁▁▁ beats 54/67    very hard · avg 0.1%     21%  sharp · at its level
FrontierMath tiers 1–3 v2   82.1%  ▂··▁▁▃▄▄█▆●▃ beats 71/86    very hard · avg 1.2%     15%  sharp
OTIS mock AIME 2024–2025    99.4%  ▃▃▄▃▃▃▇▇█▃▂● beats 185/197  very hard · avg 7.2%    0.3%  sharp · maxed out · noisy
other tests (5) 
test                              result  vs the others in its run  difficulty            counts  why
EBR-bench (Epoch)                  31.4%  ▄▂·▆█▂█●▄▆·▆ beats 15/25  very hard · avg 0.3%     12%  sharp · at its level
Mystery Game Puzzles (Epoch)       51.0%  █··▂▃▇▄█▂●▂▁ beats 66/77  very hard · avg 9.4%     11%  sharp · at its level · noisy
Furniture Assembly (Epoch)         54.2%  █▆▆▆▆▂●▂▂▂▂▅ beats 22/32  very hard · avg 1.2%    9.3%  sharp · at its level
ProgramBench (Almost@1) (maker)    50.0%  ██·······●·█ beats 2/3    very hard · avg 10%     0.9%  at its level · small run
HealthBench Professional (maker)   60.5%  ▂·▂··▂▂▂●▄▂▄ beats 6/13   hard · avg 21%          0.8%  blunt · at its level · small run
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
Epoch AI                                        6   69% ▇▇▇▇▇▇▇▇
LMArena leaderboard dataset                     3   27% ▇▇▇
Meta vendor-reported benchmark scores f…        4    4% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 78.3
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) ## How to run it ### Through an API api (Meta) · $8.00/M blended · $4 in / $20 out per 1M tokens · [dev.meta.ai](https://dev.meta.ai/docs/pricing-rate-limits) [price source](https://dev.meta.ai/docs/pricing-rate-limits): USD per 1M tokens, Meta Developer Platform Standard mode for muse-wonder-1.0; cached input $0.20/1M excluded; Contributor mode is not available for Muse Wonder; web-search grounding billed separately ($2.50 per 1,000 searches); page states no long-context premium. Currency: USD. Meta official pricing page, observed 2026-10-14. · [context source](https://github.com/anomalyco/models.dev/blob/b76390e131da169014dab4ca4eff21ca774fe5d3/models/meta/muse-wonder-1.0.toml) [interactive version](https://whatmodel.sh/model/muse-wonder) · [JSON](https://whatmodel.sh/api?format=json&all=1)