[whatmodel.sh](../../README.md) / Muse Spark # Muse Spark Meta · released 2026-04 · closed weights **score 73.9** ±1.8 · comparison only: no current run option verified · from 5 benchmark results across 2 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks Muse Spark among every model on that task or test, worst to best.}

task                vs the others         rank  counts  tests
chat & writing      ▁▁▁▃▅▄▇█▇▇▇●   #15 of 474*     34%      1
math                ▁▁▃▆██▅●▃▁▁▁   #80 of 530*    5.1%      1
vision & documents  ▁▁▂▃▂▃▅▄▆██●   #15 of 189*     35%      2

chat & writing · #15 of 474 · 34% of its score

Beats [GPT-6 Astra](gpt-6-astra.md), [GPT-6.1 Sol](gpt-6-1-sol.md), [Claude Sonnet 5.5](claude-sonnet-5-5.md) here, though they rank higher overall. Trails [Muse Spark 1.2](muse-spark-1-2.md), [Muse Spark 1.1](muse-spark-1-1.md), [Claude Opus 4.6](claude-opus-4-6.md) here, though they rank lower overall.

test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1489.0  ▁▁▁▂▃▃▃▆▆▇█● beats 371/385  avg 1260       34%  blunt

math · #80 of 530 · 5.1% of its score

Trails [Granite 4.2 30B](granite-4-2-30b.md), [INTELLECT-3](intellect-3.md), [Qwen3 235B-A22B Thinking 2507](qwen3-235b-a22b-thinking-2507.md) here, though they rank lower overall.

test                      result  vs the others in its run    difficulty            counts  why
OTIS mock AIME 2024–2025   88.9%  ▃▃▄▃▃▃▇▇●▃▂▅ beats 143/197  very hard · avg 7.2%    5.1%  sharp · maxed out · noisy

vision & documents · #15 of 189 · 35% of its score

Beats [Claude Sonnet 5.5](claude-sonnet-5-5.md), [GPT-6 Sol](gpt-6-sol.md), [GPT-5.5](gpt-5-5.md) here, though they rank higher overall. Trails [Muse Spark 1.2](muse-spark-1-2.md), [Qwen3.8-Flash-Next](qwen3-8-flash-next.md), [Claude Opus 4.7](claude-opus-4-7.md) here, though they rank lower overall.

test                               result  vs the others in its run    difficulty  counts  why
Arena vision (style controlled)    1294.1  ▁▁▂▂▂▂▃▄▅▅█● beats 147/152  avg 1061       23%  blunt
Arena document (style controlled)  1467.3  ▁▁▃▃▄▃▇█●▇▄▅ beats 22/37    avg 1331       12%  blunt

other tests (1)

test          result  vs the others in its run    difficulty      counts  why
GPQA Diamond   89.8%  ▁▁▁▁▂▄▆▄▅▇●▅ beats 172/211  hard · avg 39%     24%  sharp

## 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
LMArena leaderboard dataset          3   69% ▇▇▇▇▇▇▇▇
Epoch AI                             2   29% ▇▇▇▇
prior: pull toward the middle             1% ▇
weighted average                        = 73.9

_{Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))} ## How to run it No verified API or local download yet. [interactive version](https://whatmodel.sh/model/muse-spark) · [JSON](https://whatmodel.sh/api?format=json&all=1)