BenchLeader

Muse Spark 1.3

Reasoning effort

Muse Spark 1.3 is a Meta proprietary reasoning model, released 2 Sept 2026. Its best configuration (max reasoning effort) ranks #26 of 775 on the BenchLeader Index at 67.2 ±4.4, in the upper half. The ± is the point: 86 other configurations score within that range, so they and this one cannot be told apart on quality alone — price and speed are what separate them. It scores highest in composite (84) and lowest in maths (63). At $2.00 per million tokens blended it is pricier than most ranked models. Output speed of 128 tokens per second puts it faster than most, with a first answer in 80.4 s. It has been measured at 7 reasoning-effort settings; this summary describes the best-scoring one, and the tabs above switch between them. Last measured 19 Oct 2026.

Blended price
$2.00/M
$1.25 in · $4.25 out
Output speed
128 tok/s
measured by Artificial Analysis
First answer
80 s
first token 3.04 s
Context
1.0M
Full answer
84 s
median, reasoning included
Cost per run
$1.60
one full Intelligence Index run
Released
2 Sept 2026
How it scores by categoryDashed line = average model (50). One step of 15 = one standard deviation.
  1. Overall index67
  2. Reasoning78
  3. Coding65
  4. Agents & tools66
  5. Maths63
  6. Knowledge73
  7. Human preference69
  8. Multimodal66
  9. Long context67
  10. Composite84

Versions

Meta has shipped 4 models under this name. Each is ranked on its own results; a newer version often has fewer results so far, which holds its index nearer the average until more arrive.

ModelReleasedIndexRank
Muse Spark 1.3maxthis page2 Sept 202667.2#26
Muse Spark 1.2xhigh5 Aug 202663.2#69
Muse Spark 1.19 Jul 202662.9#75
Muse Spark8 Apr 202664.4#50

Reasoning-effort configurations

The same model behaves differently depending on how much it is allowed to think. Each row is one setting, scored only on the benchmarks that were run at that setting. “Not stated” collects results from publishers that did not say which setting they used; for a reasoning model that is usually its thinking mode, but we do not assume it. Pick a setting here or at the top of the page to see its price, speed and category scores.

EffortIndexRankSpeedFirst answerChat reply costCategories
minimal––87 tok/s3.04 s$0.0018
low––87 tok/s3.04 s$0.0018
medium––87 tok/s3.04 s$0.0018
high––87 tok/s3.04 s$0.0018
xhigh64.9#43282 tok/s26 s$0.0018Agents & tools 54 · Coding 60 · Composite 80 · Knowledge 72 · Long context 67 · Maths 62 · Multimodal 65 · Reasoning 84
maxbest67.2#26128 tok/s80 s$0.0018Agents & tools 66 · Coding 65 · Composite 84 · Human preference 69 · Knowledge 73 · Long context 67 · Maths 63 · Multimodal 66 · Reasoning 78
not stated58.3#15887 tok/s3.04 s$0.0018Agents & tools 59 · Composite 58 · Knowledge 80 · Maths 57

How its index has moved

9 Sept 2026 to 21 Oct 2026
546474
  • max
  • xhigh
  • not stated

The index is recomputed from scratch every day, so a line moves when a new benchmark result lands, when a publisher revises a score, or when the models it is normalised against change. Early movement usually means the score is still settling.

Benchmark results

See all LLM benchmarks. One column per reasoning effort. Rank is among every configuration of every model on that benchmark. Hover a score for the run it came from.

Reasoning

Benchmarkxhighmaxnot statedSourceTrend
Arena Hard Prompts–1515#12–Arena (LMArena)
LiveBench 2.0 Reasoningnot in indexeffort not stated––89.7%#14LiveBench
LiveBench 1.0 Reasoningnot in index89.7%#15––LiveBench
GPQA Diamond (AA)not in index94.1%#793.5%#14–Artificial Analysis
Humanity's Last Exam (AA)not in index47.5%#3848.7%#31–Artificial Analysis
CritPt26.0%#3924.9%#44–Artificial Analysis
MysteryMechanismnot in index–36.0%#9–Vals AI
Chess Puzzlesnot in index34.0%#5737.0%#43–Epoch AI Benchmarking Hub
Mystery Game Puzzlesnot in index14.1%#9525.0%#46–Epoch AI Benchmarking Hub
LMCAnot in indexeffort not stated––53.9%#49Epoch AI Benchmarking Hub
DTBenchnot in indexeffort not stated––96.5%#22Epoch AI Benchmarking Hub
BTF-3not in index14.1%#5––Epoch AI Benchmarking Hub

Coding

Benchmarkminimallowmediumhighxhighmaxnot statedSourceTrend
SciCode––––59.7%#1258.8%#17–SciCode
Arena Coding–––––1535#15–Arena (LMArena)
Arena WebDev––––1621#221659#13–Arena (LMArena)
LiveBench 2.0 Codingnot in indexeffort not stated––––––90.2%#12LiveBench
LiveBench 1.0 Codingnot in index––––81.1%#16––LiveBench
SciCode (AA)not in index––––59.7%#1358.8%#18–Artificial Analysis
IOI––––43.9%#3256.6%#19–Vals AI
Code Migrationnot in index––––27.6%#4447.4%#14–Vals AI
ProgramBenchnot in index––––0.5%#182.5%#7–Vals AI
Vibe Code Bench 1-100not in index–––––20.5%#9–Vals AI
Vibe Code Bench v1.1not in index––––82.9%#2085.9%#14–Vals AI
CursorBenchnot in index24.3%#7529.3%#6832.6%#6033.4%#5537.5%#4441.6%#34–Cursor
ALE-Benchnot in index––––1688.3#14––Epoch AI Benchmarking Hub
FrontierSWEnot in index–––––25.8%#13–Epoch AI Benchmarking Hub
Terminal-Bench 4.0 (AA)not in index––––16.7%#6733.3%#38–Artificial Analysis
Terminal-Bench 2.1 (AA)not in index––––85.4%#2584.3%#30–Artificial Analysis
Startrise Frontendnot in indexeffort not stated––––––74.7%#10Startrise
FrontierCode 1.1 (AA)not in index–––––40.6%#24–Artificial Analysis

Agents & tools

Benchmarkxhighmaxnot statedSourceTrend
Terminal-Bench14.6%#78––Terminal-Bench
APEX-Agents–47.6%#3158.6%#14Mercor
Arena Agent–3.3#15–Arena (LMArena)
LiveBench Long Agenticnot in indexeffort not stated––40.4%#23LiveBench
LiveBench Browsernot in indexeffort not stated––80.0%#16LiveBench
LiveBench Agentic Codingnot in index64.1%#8––LiveBench
Terminal-Bench 2.1 (Vals)72.3%#2479.0%#11–Vals AI
GDPval-AA v2.1not in index56.4%#2359.0%#16–Artificial Analysis
τ³-Banking (AA)47.2%#850.5%#3–Artificial Analysis
ITBench SRE (AA)not in index–33.2%#29–Artificial Analysis
Analyst Agent (AA)not in index–37.5%#26–Artificial Analysis
CUA-benchnot in index–5.8%#7–Vals AI
CyberBenchnot in index69.4%#2672.7%#16–Vals AI
Finance Agent v2not in index58.9%#860.0%#5–Vals AI
Legal Research Benchnot in index40.9%#2755.3%#1–Vals AI
SREBenchnot in index–3.8%#19–Vals AI
Tax Agent Benchnot in index36.0%#1242.4%#7–Vals AI
Terminal-Bench 4.0 (Vals)not in index10.6%#3724.8%#23–Vals AI
Terminal-Bench Sciencenot in index4.3%#2510.0%#15–Vals AI
AutomationBenchnot in index56.8%#4957.9%#46–Artificial Analysis
GDP.pdf (AA)not in index24.2%#3526.6%#20–Artificial Analysis
Harvey LAB-AA v1.1not in index–8.9%#4–Harvey
AA-Briefcase v1.1not in index–1581#16–Artificial Analysis
Terminal-Bench-Science 0.1 (AA)not in index–10.9%#24–Artificial Analysis

Maths

Benchmarkxhighmaxnot statedSourceTrend
FrontierMath Tiers 1–374.4%#2274.0%#23–Epoch AI Benchmarking Hub
FrontierMath Tier 441.5%#3146.3%#27–Epoch AI Benchmarking Hub
OTIS Mock AIME99.2%#16––Epoch AI Benchmarking Hub
ProofBench–58.0%#2155.0%#26Vals AI
LiveBench Mathematicsnot in index96.0%#13––LiveBench
Arena Maths–1504#11–Arena (LMArena)

Knowledge

Benchmarkhighxhighmaxnot statedSourceTrend
LiveBench 2.0 Data Analysisnot in indexeffort not stated–––79.6%#11LiveBench
LiveBench Researchnot in indexeffort not stated–––68.6%#33LiveBench
LiveBench 1.0 Data Analysisnot in index–79.6%#12––LiveBench
AA-Omniscience–23.1#5625#52–Artificial Analysis
PRBench Financeeffort not stated–––59.5%#1Scale AI SEAL
PRBench Legaleffort not stated–––61.6%#1Scale AI SEAL
Excel Modeling Benchmarknot in index–62.7%#3167.4%#16–Vals AI
DrugDiscoveryBenchnot in index62.2%#2–––Scale AI SEAL
GDP.pdfnot in index–27.6%#12–27.6%#12Epoch AI Benchmarking Hub
AA-Omniscience: accuracynot in index–41.5%#11043.6%#97–Artificial Analysis
AA-Omniscience: non-hallucinationnot in index–68.5%#5067.1%#56–Artificial Analysis

Instruction following

BenchmarkxhighmaxSourceTrend
LiveBench Languagenot in index82.8%#24–LiveBench
LiveBench Instruction Followingnot in index78.0%#4–LiveBench
Arena Instruction Followingnot in index–1483#20Arena (LMArena)

Human preference

BenchmarkmaxSourceTrend
Arena Text1494#10Arena (LMArena)
Arena Creative Writingnot in index1461#29Arena (LMArena)
Arena Multi-turnnot in index1491#20Arena (LMArena)
Arena Longer Queriesnot in index1496#20Arena (LMArena)

Multimodal

Benchmarkxhighmaxnot statedSourceTrend
Arena Vision1302#191308#12–Arena (LMArena)
LiveBench Designnot in indexeffort not stated––74.8%#29LiveBench
MMMU-Pro82.0%#34––Artificial Analysis
AA-3Dnot in index–1037#8–Artificial Analysis

Long context

BenchmarkxhighmaxSourceTrend
AA-LCR83.0%#2583.0%#25Artificial Analysis
Arena Documentnot in index–1468#19Arena (LMArena)
MLCRnot in index–43.3%#15Artificial Analysis

Composite

Benchmarkxhighmaxnot statedSourceTrend
Epoch Capabilities Indexnot in indexeffort not stated––157.2#23Epoch AI Benchmarking Hub
LiveBench 2.0effort not stated––74.8%#19LiveBench
LiveBench 1.0not in index81.6%#7––LiveBench
AA Intelligence Index v4.3.245.1#3748.1#25–Artificial Analysis

Where it wins

Benchmarks where this configuration ranks in the top five of every configuration measured.

Where to run it

Every provider serving this model through OpenRouter, with throughput and first-token latency measured on live traffic over the last 30 minutes and each provider’s own price. Purple marks the best in each column.

ProviderSpeedFirst tokenInput $/MOutput $/MContextQuantisation
Meta127 tok/s2.34 s$1.25$4.251.0M–

What a task costs

Estimates from list price, output speed and time to first answer for the best configuration. “With caching” assumes three-quarters of the input is served from the prompt cache at $0.150 per 1M. Reasoning tokens are not modelled.

WorkloadTokens in / outCostWith cachingTime
Chat reply400 / 300$0.0018$0.00141.4 min
Summarize a 30-page report12,000 / 600$0.018$0.00771.4 min
Code edit6,000 / 1,500$0.014$0.00891.5 min
Agentic coding session60,000 / 4,000$0.092$0.0431.9 min
Structured extraction2,000 / 200$0.0034$0.00171.4 min

More from Meta

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See also

Data as of 21 Oct 2026. Compare these configurations.

Cite as: BenchLeader, “Muse Spark 1.3: benchmarks, pricing, speed and rank”, https://www.benchleader.com/models/muse-spark-1-3, data as of 21 Oct 2026.