whatmodel.sh / Muse Spark 1.1

Muse Spark 1.1

+ contribute
Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Muse Spark 1.1 is a closed-weights language model from Meta, released July 2026.

75.0±1.0#42 of 719 LLMsfrom 9 benchmark results across 4 sources

$ how it compares

The LLMs ranked either side of Muse Spark 1.1, out of 828 with a score, then more from Meta.

#modelscorewhereRAMreleased
41Claude Opus 4.7 Anthropic75±1api $10.00/M2026-04
42Grok 4.5 SpaceXAI (xAI)75±1api $3.00/M2026-07
43Muse Spark 1.1 Meta75±1api $2.00/M2026-07
44Muse Spark Meta75±2comparison only2026-04
45Step 5 Preview StepFun75±1comparison only
more from Meta
24Muse Spark 1.3 Meta77±1api $2.00/M2026-09
40Muse Spark 1.2 Meta75±1api $2.00/M2026-08
97Muse Glimmer 30B Meta70±1api $0.64/M +local~19GB2026-08

see all LLMs · find the ones that fit your machine

$ what it's good at

agent tasks▁▁▃▆█▆▅▅▅▄▃▁ #51 of 300 * · 27% of its score · 2 tests

Beats Gemini 3.1 Pro (Preview) here, though they rank higher overall. Trails Claude Sonnet 4.6, Qwen3.8 27B, GLM-5.2 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Arena Agent (October 2026 aggregate IPS)-0.0488 IPS ✎▁▁▁▄▃▃▇█▂▂▁▃ beats 12/49avg -0.312 IPS▇▇▇▇▇▇▇▇ 20%sharp
τ³-bench banking_knowledge (v1.0.1 grading)40.5% ✎▃▃▂▃·▆▃▂█▅▅▂ beats 20/25very hard · avg 4.1%▇▇▇ 7.2%at its level
coding▁▁▁▃▆██▇▆▄▃▁ #38 of 458 * · 17% of its score · 1 test

Beats GPT-5.5, GPT-5.6 Terra, GPT-5.4 here, though they rank higher overall. Trails Qwen3.8 27B, GLM-5.2, GLM-5.3-Flash here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Arena WebDev (Code Arena)1541.7 ✎▁▂▄▃▅█▄▇▆▄▁▁ beats 83/119avg 981▇▇▇▇▇▇▇ 17%sharp
chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #10 of 393 * · 10% of its score · 1 test

Beats GPT-6 Astra, GPT-6.1 Sol, Claude Sonnet 5.5 here, though they rank higher overall. Trails Claude Opus 4.6 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LMArena (text)1491.3 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 370/379avg 1268▇▇▇▇ 10%blunt
math▁▂▃▅▆▅▄█▄▁▁▁ #35 of 398 * · 32% of its score · 2 tests

Beats Claude Opus 4.7, Grok 4.5 here, though they rank higher overall. Trails GPT-5.2, Qwen3.7 Max, Claude Sonnet 5 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
MathArena ArXivMath June 2026 (48 problems)56.9% ✎▂···▄·██▆▆▄▂ beats 5/19very hard · avg 4.6%▇▇▇▇▇▇▇▇▇▇ 26%sharp · at its level
MathArena BrokenArXiv June 2026 (54 problems)50.6% ✎▄··█▂▂▂▆▄··▂ beats 10/14very hard · avg 1.1%▇▇ 6.1%sharp · at its level · noisy
vision & documents▁▁▃▂▁▃▄▂▆▇█▄ #22 of 147 * · 10% of its score · 2 tests

Beats Claude Sonnet 5.5, MiMo-V2.6-Pro, GPT-5.6 Terra here, though they rank higher overall. Trails Gemini 3.6 Flash, Gemini 3.5 Flash, Claude Opus 4.6 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Arena vision (style controlled)1281.1 ✎▁▁▂▂▂▂▄▅▅▆██ beats 127/145avg 1068▇▇▇ 6.7%blunt
Arena document (style controlled)1479.5 ✎▁▁▃▃▄▃▇█▅▇▄▅ beats 29/37avg 1335▇ 3.2%blunt
other tests1 not in a task · 3.9% of its score
testresultvs the others in its rundifficultycountswhy
SimpleQA Verified57.8% ✎▁▁▃▂▃▃▆▆█▃▂▅ beats 62/78very hard · avg 8.0%▇▇ 3.9%at its level

$ where to run it

Through an API

api · $2.00/M · $1.25 in / $4.25 out per 1M tokens · 1M context · dev.meta.ai ↗ · price sourceStandard base token rate from models.dev. Provider-specific tiers, long-context surcharges, caching and promotions may differ.

$ how the score was worked out · LMArena leaderboard dataset 57%, MathArena ArXivMath / BrokenA… 32%, τ²/τ³-bench leaderboard: Sier… 7% and 1 more

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The 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's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
LMArena leaderboard dataset5▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 57%CC BY 4.0
MathArena ArXivMath / BrokenArXiv / ArXivLean model-output datasets2▇▇▇▇▇▇▇▇▇▇ 32%CC BY-SA 4.0
τ²/τ³-bench leaderboard: Sierra's own evaluation runs1▇▇ 7%MIT
Epoch AI — internal evaluations1▇ 4%CC BY 4.0
prior · pull toward the middle▇ 0%
weighted average= 75.0

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json