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Llama 4 Scout vs Muse Spark 1.2

Muse Spark 1.2 leads Llama 4 Scout on the Artificial Analysis Intelligence Index, 39.6 to 6.5, and Llama 4 Scout costs 8.5x less per output token ($0.50 vs $4.25 per million). Updated October 22, 2026; 53 benchmarks have a result for both.

Llama 4 ScoutMeta Llama
Coding
No data
Overall
24.9
Output $/M
$0.50
0
Muse Spark 1.2Meta
Coding
55.7
Overall
60.4
Output $/M
$4.25
53
vs boards won of 53 shared

Llama 4 Scout or Muse Spark 1.2: which to pick

Only Muse Spark 1.2 has a coding score, 55.7 on the Vibe Coding Index; Llama 4 Scout has none, so the two cannot be ranked on coding.

Pick Llama 4 Scout for output tokens 8.5x cheaper ($0.50 vs $4.25 per million) and a larger context window (1.31M against 1.05M tokens).

Shared results
53
Llama 4 Scout only
29
Muse Spark 1.2 only
44
Like-for-like categories
8 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
Llama 4 Scout not benchmarked, Muse Spark 1.2 55.7 from 5 benchmarks.
Widest category
Web design, 64.6 points apart on average over 5 shared benchmarks.
Widest single gap
Design Arena: websites: Llama 4 Scout 750 against Muse Spark 1.2 1312, 70.3 points apart on a 0 to 100 scale.
Output price
Llama 4 Scout $0.50 against Muse Spark 1.2 $4.25 per million output tokens, 8.5x apart.
Context window
Llama 4 Scout takes 1.31M tokens, Muse Spark 1.2 1.05M.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryLlama 4 ScoutBarsMuse Spark 1.2Ties
Coding04
Intelligence02
Agentic work01
Reasoning07
Math02
Knowledge010
Web design05
General022
All shared boards053
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for Llama 4 Scout and Muse Spark 1.2
BenchmarkLlama 4 ScoutMuse Spark 1.2Leader
SWE-bench Verified9.1%Not measuredOnly Llama 4 Scout measured
LiveBench codingNot measured78.2%Only Muse Spark 1.2 measured
LMArena WebDevNot measured1534Only Muse Spark 1.2 measured
AA Coding Index8.272.2Muse Spark 1.2
LiveCodeBench (Vals AI)38.5%Not measuredOnly Llama 4 Scout measured
SWE-bench Verified (Vals AI)Not measured86.6%Only Muse Spark 1.2 measured
Terminal-Bench 4 (Vals AI)Not measured6.1%Only Muse Spark 1.2 measured
Vibe Code Bench (Vals AI)Not measured79.1%Only Muse Spark 1.2 measured
Code migration (Vals AI)Not measured29.9%Only Muse Spark 1.2 measured
ProgramBench (Vals AI)Not measured0.5%Only Muse Spark 1.2 measured
IOI (Vals AI)Not measured21.8%Only Muse Spark 1.2 measured
Terminal-Bench 2.1 (Vals AI)Not measured69.7%Only Muse Spark 1.2 measured
SkillsBench (Vals AI)Not measured53.0%Only Muse Spark 1.2 measured
CyberBench (Vals AI)Not measured69.5%Only Muse Spark 1.2 measured
LiveCodeBench (Artificial Analysis run)29.9%Not measuredOnly Llama 4 Scout measured
SciCode21.3%57.4%Muse Spark 1.2
Terminal-Bench Hard (Artificial Analysis run)1.5%Not measuredOnly Llama 4 Scout measured
FrontierSWENot measured12.0%Only Muse Spark 1.2 measured
WeirdMLNot measured60.3%Only Muse Spark 1.2 measured
LMArena Text: coding12861505Muse Spark 1.2
LMArena Text: software and IT12861505Muse Spark 1.2
LMArena WebDev: ReactNot measured1526Only Muse Spark 1.2 measured
LMArena WebDev: HTMLNot measured1543Only Muse Spark 1.2 measured
Neither model has a result on the other 16 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Llama 4 Scout and Muse Spark 1.2
BenchmarkLlama 4 ScoutMuse Spark 1.2Leader
AA Intelligence Index6.539.6Muse Spark 1.2
Epoch Capabilities Index129.6155.1Muse Spark 1.2
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Llama 4 Scout and Muse Spark 1.2
BenchmarkLlama 4 ScoutMuse Spark 1.2Leader
AA Agentic Index0.543.2Muse Spark 1.2
Design Arena: agents building web appsNot measured1221Only Muse Spark 1.2 measured
Design Arena: agents building full-stack appsNot measured1210Only Muse Spark 1.2 measured
Design Arena: agents building mobile appsNot measured1181Only Muse Spark 1.2 measured
Design Arena: agents building Android appsNot measured1222Only Muse Spark 1.2 measured
Design Arena: agents building gamesNot measured1179Only Muse Spark 1.2 measured
Design Arena: HTML slidesNot measured1085Only Muse Spark 1.2 measured
Design Arena: PowerPoint slidesNot measured1172Only Muse Spark 1.2 measured
LiveBench researchNot measured67.7%Only Muse Spark 1.2 measured
LiveBench browserNot measured36.0%Only Muse Spark 1.2 measured
LiveBench long agenticNot measured21.3%Only Muse Spark 1.2 measured
SREBench (Vals AI)Not measured0.0%Only Muse Spark 1.2 measured
Finance Agent (Vals AI)Not measured60.6%Only Muse Spark 1.2 measured
Tax Agent Bench (Vals AI)Not measured32.8%Only Muse Spark 1.2 measured
Legal Research Bench (Vals AI)Not measured43.8%Only Muse Spark 1.2 measured
Excel Modeling Benchmark (Vals AI)Not measured57.0%Only Muse Spark 1.2 measured
BioMysteryBench (Vals AI)Not measured64.8%Only Muse Spark 1.2 measured
τ²-Bench Telecom15.5%Not measuredOnly Llama 4 Scout measured
APEX-AgentsNot measured45.4%Only Muse Spark 1.2 measured
Berkeley Function Calling Leaderboard (BFCL)28.1%Not measuredOnly Llama 4 Scout measured
Neither model has a result on the other 21 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for Llama 4 Scout and Muse Spark 1.2
BenchmarkLlama 4 ScoutMuse Spark 1.2Leader
GPQA Diamond47.0%Not measuredOnly Llama 4 Scout measured
LiveBench reasoningNot measured90.0%Only Muse Spark 1.2 measured
MMMU Pro (Vals AI)58.8%86.1%Muse Spark 1.2
GPQA Diamond (Artificial Analysis run)58.7%90.4%Muse Spark 1.2
Humanity's Last Exam (Artificial Analysis run)3.8%45.5%Muse Spark 1.2
GPQA Diamond (Epoch AI run)51.8%Not measuredOnly Llama 4 Scout measured
ARC-AGI0.5%Not measuredOnly Llama 4 Scout measured
ARC-AGI-20.0%Not measuredOnly Llama 4 Scout measured
SimpleBenchNot measured74.5%Only Muse Spark 1.2 measured
Chess puzzlesNot measured27.0%Only Muse Spark 1.2 measured
LMArena Text: hard prompts12661489Muse Spark 1.2
LMArena Text: expert12351475Muse Spark 1.2
LMArena Text: hard prompts in English12931487Muse Spark 1.2
LMArena Vision: diagrams11251317Muse Spark 1.2
LMArena Vision: homework1148Not measuredOnly Llama 4 Scout measured
Kagi LLM Benchmark36.9%Not measuredOnly Llama 4 Scout measured
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Llama 4 Scout and Muse Spark 1.2
BenchmarkLlama 4 ScoutMuse Spark 1.2Leader
AIME19.0%Not measuredOnly Llama 4 Scout measured
MATH 500 (Vals AI)79.2%Not measuredOnly Llama 4 Scout measured
MGSM (Vals AI)88.0%Not measuredOnly Llama 4 Scout measured
AA Math Index14.0Not measuredOnly Llama 4 Scout measured
AIME 2025 (Artificial Analysis run)14.0%Not measuredOnly Llama 4 Scout measured
OTIS Mock AIME7.8%Not measuredOnly Llama 4 Scout measured
ProofBenchNot measured43.0%Only Muse Spark 1.2 measured
MATH level 562.3%Not measuredOnly Llama 4 Scout measured
LMArena Text: math12871484Muse Spark 1.2
LMArena Text: mathematical work12861480Muse Spark 1.2
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index010
Knowledge benchmarks for Llama 4 Scout and Muse Spark 1.2
BenchmarkLlama 4 ScoutMuse Spark 1.2Leader
MMLU Pro69.6%88.3%Muse Spark 1.2
LegalBench (Vals AI)72.0%85.3%Muse Spark 1.2
CorpFin (Vals AI)46.8%70.9%Muse Spark 1.2
TaxEval (Vals AI)55.2%80.4%Muse Spark 1.2
MortgageTax (Vals AI)57.8%65.4%Muse Spark 1.2
MedQA (Vals AI)50.9%Not measuredOnly Llama 4 Scout measured
MedCode (Vals AI)23.3%49.4%Muse Spark 1.2
MMLU Pro (Artificial Analysis run)75.2%Not measuredOnly Llama 4 Scout measured
SimpleQA VerifiedNot measured60.3%Only Muse Spark 1.2 measured
LMArena Text: science12781500Muse Spark 1.2
LMArena Text: medicine and healthcare12751496Muse Spark 1.2
LMArena Text: legal and government12851517Muse Spark 1.2
LMArena Text: business and finance12571484Muse Spark 1.2
Neither model has a result on the other 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index05
Web design benchmarks for Llama 4 Scout and Muse Spark 1.2
BenchmarkLlama 4 ScoutMuse Spark 1.2Leader
Design Arena: websites7501312Muse Spark 1.2
Design Arena: UI components7651298Muse Spark 1.2
Design Arena: game dev7721301Muse Spark 1.2
Design Arena: data viz8961334Muse Spark 1.2
Design Arena: 3DNot measured1301Only Muse Spark 1.2 measured
Design Arena: all code categories7891312Muse Spark 1.2
Design Arena: SVGNot measured1284Only Muse Spark 1.2 measured
Design Arena: ASCII artNot measured1284Only Muse Spark 1.2 measured
LiveBench designNot measured67.1%Only Muse Spark 1.2 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index022
General benchmarks for Llama 4 Scout and Muse Spark 1.2
BenchmarkLlama 4 ScoutMuse Spark 1.2Leader
LiveBench data analysisNot measured76.5%Only Muse Spark 1.2 measured
Public Benefits Bench (Vals AI)Not measured68.5%Only Muse Spark 1.2 measured
SAGE (Vals AI)34.8%47.7%Muse Spark 1.2
MedScribe (Vals AI)50.6%90.1%Muse Spark 1.2
Vals Multimodal IndexNot measured69.8%Only Muse Spark 1.2 measured
IFBench39.5%Not measuredOnly Llama 4 Scout measured
Long context reasoning (AA-LCR)27.7%79.0%Muse Spark 1.2
DTBench57.9%94.7%Muse Spark 1.2
LMCA12.0%48.4%Muse Spark 1.2
Fiction.LiveBench36.0%Not measuredOnly Llama 4 Scout measured
LMArena Text12791482Muse Spark 1.2
LMArena Text: instruction following12481462Muse Spark 1.2
LMArena Text: creative writing12491436Muse Spark 1.2
LMArena Vision11181304Muse Spark 1.2
LMArena Text: English13041483Muse Spark 1.2
LMArena Text: non-English12521476Muse Spark 1.2
LMArena Text: Chinese12551509Muse Spark 1.2
LMArena Text: German1273Not measuredOnly Llama 4 Scout measured
LMArena Text: Japanese1206Not measuredOnly Llama 4 Scout measured
LMArena Text: Korean12071462Muse Spark 1.2
LMArena Text: French12831495Muse Spark 1.2
LMArena Text: Spanish12781484Muse Spark 1.2
LMArena Text: Russian12631483Muse Spark 1.2
LMArena Text: Polish1265Not measuredOnly Llama 4 Scout measured
LMArena Text: multi-turn12801502Muse Spark 1.2
LMArena Text: longer queries12651473Muse Spark 1.2
LMArena Text: writing and language12691453Muse Spark 1.2
LMArena Text: entertainment and media12421443Muse Spark 1.2
LMArena Vision: English11431291Muse Spark 1.2
LMArena Vision: Chinese1106Not measuredOnly Llama 4 Scout measured
LMArena Vision: reading text11171313Muse Spark 1.2
LMArena Vision: humor1102Not measuredOnly Llama 4 Scout measured
LMArena Vision: creative writing1126Not measuredOnly Llama 4 Scout measured
Neither model has a result on the other 13 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Llama 4 Scout and Muse Spark 1.2
BenchmarkLlama 4 ScoutMuse Spark 1.2Leader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

Llama 4 Scout$0.0004Fits in one request
Muse Spark 1.2$0.0034Fits in one request

Llama 4 Scout costs less

Repository review

50K input + 3K output tokens

Llama 4 Scout$0.010Fits in one request
Muse Spark 1.2$0.075Fits in one request

Llama 4 Scout costs less

Agent loop

200K input + 10K output tokens

Llama 4 Scout$0.040Fits in one request
Muse Spark 1.2$0.292Fits in one request

Llama 4 Scout costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of Llama 4 Scout and Muse Spark 1.2
SpecificationMeta Llama 4 ScoutMeta Muse Spark 1.2
MakerMeta LlamaMeta
WeightsOpen weightsNo public weights listed
ReleasedApril 5, 2025August 5, 2026
Context window1.31M1.05M
Input $/Mtok$0.175$1.25
Output $/Mtok$0.50$4.25
Coding indexNot benchmarked55.7
Independent measurements behind it05
Cost per solved task$1.276$0.64
SourceOpenRouter listingOpenRouter listing

Questions

Is Llama 4 Scout better than Muse Spark 1.2 for coding?

Only Muse Spark 1.2 has a coding score, 55.7 on the Vibe Coding Index; Llama 4 Scout has none, so the two cannot be ranked on coding. Pick Llama 4 Scout for output tokens 8.5x cheaper ($0.50 vs $4.25 per million) and a larger context window (1.31M against 1.05M tokens).

Which is cheaper, Llama 4 Scout or Muse Spark 1.2?

Llama 4 Scout costs $0.175 per million input tokens and $0.50 per million output tokens; Muse Spark 1.2 costs $1.25 and $4.25. Per solved SWE-bench Verified task, Llama 4 Scout costs $1.276 and Muse Spark 1.2 $0.64.

Which has the bigger context window, Llama 4 Scout or Muse Spark 1.2?

Llama 4 Scout, with 1.31M tokens against 1.05M.

How many benchmarks is the Llama 4 Scout vs Muse Spark 1.2 comparison based on?

53 benchmarks have a result for both models as of October 22, 2026. Llama 4 Scout's coding index rests on 0 independent benchmarks and Muse Spark 1.2's on 5. Where only one model has a result, the page says so instead of naming a winner.

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