[whatmodel.sh](../../README.md) / Muse Spark 1.3 # Muse Spark 1.3 Meta · released 2026-09 · closed weights **score 75.6** ±0.7 · #26 of 732 · from 18 benchmark results across 6 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 1.3 among every model on that task or test, worst to best. 
task                vs the others         rank  counts  tests
agent tasks         ▁▁▃▆█▄▅▅▄●▂▁    #26 of 313     25%      8
coding              ▁▁▁▃▅▇█▇▅▄●▁   #16 of 469*     11%      3
chat & writing      ▁▁▁▃▆▄▆█▆▇▇●   #10 of 402*    4.8%      1
math                ▁▁▃▅█▆▄█▄●▁▁    #24 of 431     43%      5
vision & documents  ▁▁▂▂▁▃▄▂▆▇●▄   #17 of 153*    4.8%      2
web search          █··········●      #1 of 2*    1.0%      1
agent tasks · #26 of 313 · 25% of its score Beats [Hy4 preview (770B-A49B)](hy4-preview.md), [GPT-5.6 Terra](gpt-5-6-terra.md), [Qwen3.8 Max (2.4T-A95B)](qwen3-8-2-4t-a95b.md) here, though they rank higher overall. Trails [Grok 4.7](grok-4-7.md), [Claude Haiku 5.5](claude-haiku-5-5.md), [DeepSeek V4.1 Flash](deepseek-v4-1-flash.md) here, though they rank lower overall. 
test                                    result  vs the others in its run  difficulty            counts  why
Arena Agent (October 2026 aggrega…  0.0259 IPS  ▁▁▁▃▃▂●▁▂▂▁▁ beats 38/59  avg -0.379 IPS           12%  sharp
Terminal-Bench 4.0                       14.5%  █●▅▅▅·▅▃▃▅▅▅ beats 3/20   very hard · avg 0.5%    4.9%  sharp · at its level
E2E-SWE (build a working codebase…       45.2%  ▃··▅··▃●▃▅▃▅ beats 6/12   very hard · avg 3.7%    2.9%  sharp · at its level
Terminal-Bench 2.1 (maker)               88.8%  ▁·▁·▁▁▄▃▆█▅● beats 59/63  hard · avg 26%          2.2%  sharp · small run
JobBench (maker)                         64.9%  █·●····█···█ beats 1/3    hard · avg 25%          1.5%  at its level
DeepSearchQA (maker)                     90.3%  ▄·▄▄···▄▄··● beats 5/6    medium · avg 54%        1.0%  small run
DeepSWE v1.1 (resolved) (maker)          75.4%  ▂····▂▁▁▆██● beats 29/31                             —  not counted yet: no other model in the …
OSWorld 2.0, 2026.08.08 release (…       66.9%  ●·····█·█··█ beats 0/3                               —  not counted yet: no other model in the …
coding · #16 of 469 · 11% of its score Beats [GPT-5.6 Sol](gpt-5-6-sol.md), [Claude Fable 5](claude-fable-5.md), [Muse Wonder](muse-wonder.md) here, though they rank higher overall. Trails [Qwen3.8 Max (0902)](qwen3-8-max-0902.md) here, though they rank lower overall. 
test                                result  vs the others in its run    difficulty            counts  why
Arena WebDev (Code Arena)           1656.2  ▁▁▃▂▅█▃▆▅●▁▂ beats 113/129  avg 981                 8.4%  sharp
E2E-SWE (build a working codebase…   45.2%  ▃··▅··▃●▃▅▃▅ beats 6/12     very hard · avg 3.7%    2.9%  sharp · at its level
DeepSWE v1.1 (resolved) (maker)      75.4%  ▂····▂▁▁▆██● beats 29/31                               —  not counted yet: no other model in the …
chat & writing · #10 of 402 · 4.8% 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 [Claude Opus 4.6](claude-opus-4-6.md), [Claude Opus 4.7](claude-opus-4-7.md), [Gemini 3.8 Flash](gemini-3-8-flash.md) here, though they rank lower overall. 
test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1495.0  ▁▁▁▂▃▃▃▆▆▇█● beats 376/385  avg 1260      4.8%  blunt
math · #24 of 431 · 43% of its score Beats [DeepSeek V4.1 Flash 1017](deepseek-v4-1-flash-1017.md) here, though they rank higher overall. Trails [GPT-5.6 Luna](gpt-5-6-luna.md), [GPT-6 Luna](gpt-6-luna.md), [Claude Haiku 5.5](claude-haiku-5-5.md) here, though they rank lower overall. 
test                                result  vs the others in its run    difficulty            counts  why
FrontierMath tier 4 v2               46.3%  ▁▂▁▂█●▂▃▂▁▁▁ beats 48/67    very hard · avg 0.1%     21%  sharp · at its level
FrontierMath tiers 1–3 v2            74.0%  ▂··▁▁▃▄▄█●▄▃ beats 65/86    very hard · avg 1.2%     19%  sharp · at its level
MathArena ArXivMath August 2026, …   45.6%  █●·▄·······█ beats 2/5      very hard · avg 5.9%    1.9%  sharp · at its level
MathArena BrokenArXiv August 2026…   20.2%  ●▄·····▄·▄·█ beats 0/5      very hard · avg 6.9%    0.8%  at its level · noisy
OTIS mock AIME 2024–2025             99.2%  ▃▃▄▃▃▃▇▇█▃▂● beats 183/197  very hard · avg 7.2%    0.5%  sharp · maxed out · noisy
vision & documents · #17 of 153 · 4.8% 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), [Qwen3.8-Omni-Flash](qwen3-8-omni-flash.md) here, though they rank lower overall. 
test                               result  vs the others in its run    difficulty  counts  why
Arena vision (style controlled)    1289.6  ▁▁▂▂▂▂▃▄▅▅█● beats 142/152  avg 1061      3.2%  blunt
Arena document (style controlled)  1467.9  ▁▁▃▃▄▃▇█●▇▄▅ beats 23/37    avg 1331      1.6%  blunt
web search · #1 of 2 · 1.0% of its score 
test                  result  vs the others in its run  difficulty        counts  why
DeepSearchQA (maker)   90.3%  ▄·▄▄···▄▄··● beats 5/6    medium · avg 54%    1.0%  small run
other tests (3) 
test                                result  vs the others in its run    difficulty            counts  why
Mystery Game Puzzles (Epoch)         25.0%  █··▂▃▇●█▂▄▂▁ beats 43/77    very hard · avg 9.4%    8.4%  sharp · at its level · noisy
Chess Puzzles (Epoch)                38.0%  █▁▁▁▁▂▃▃●▂▁▁ beats 116/137  very hard · avg 5.8%    4.8%  sharp · at its level · noisy
AutomationBench public set (Zapie…   49.6%  █······▄█··● beats 5/6      very hard · avg 13%     0.8%  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                                        5   54% ▇▇▇▇▇▇
LMArena leaderboard dataset                     5   30% ▇▇▇▇
Meta vendor-reported benchmark scores f…        4    5% ▇
Terminal-Bench 4.0 official leaderboard…        1    5% ▇
E2E-SWE: Benchmarking LLMs on Building …        1    3% ▇
MathArena ArXivMath / BrokenArXiv / ArX…        2    3% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 75.6
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/)) · Terminal-Bench 4.0 official leaderboard submissions ([Apache-2.0](../../licenses/terminal-bench-leaderboard-submissions-Apache-2.0.txt)) · E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · MathArena ArXivMath / BrokenArXiv / ArXivLean model-output datasets ([CC BY-SA 4.0](../../licenses/matharena-sharealike-NOTICE.txt))
Not counted (2) 
source                  test                            score  why
Meta vendor-reported …  OSWorld 2.0, 2026.08.08 relea…  66.9%  too few models have it yet
Meta vendor-reported …  DeepSWE v1.1 (resolved)         75.4%  too few models have it yet
## How to run it ### Through an API api (Meta) · $2.00/M blended · $1.25 in / $4.25 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 tier (prompts not used for training) for muse-spark-1.3; cached input $0.15/1M and the cheaper Contributor tier (muse-spark-1.3-contributor, data used for training) excluded; web-search grounding billed separately; page states no long-context premium. Currency: USD. Meta official pricing page, observed 2026-09-25. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/meta/muse-spark-1.3.toml) [interactive version](https://whatmodel.sh/model/muse-spark-1-3) · [JSON](https://whatmodel.sh/api?format=json&all=1)