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GLM 4.6V vs Muse Spark 1.3

Muse Spark 1.3 leads GLM 4.6V on the Artificial Analysis Intelligence Index, 48.1 to 11.2, and GLM 4.6V costs 4.7x less per output token ($0.90 vs $4.25 per million). Updated October 22, 2026; 31 benchmarks have a result for both.

Replace GLM 4.6V Replace Muse Spark 1.3
GLM 4.6VZ.ai
Coding
No data
Overall
No data
Output $/M
$0.90
0
Muse Spark 1.3Meta
Coding
57.4
Overall
68.6
Output $/M
$4.25
31
vs boards won of 31 shared

GLM 4.6V or Muse Spark 1.3: which to pick

Only Muse Spark 1.3 has a coding score, 57.4 on the Vibe Coding Index; GLM 4.6V has none, so the two cannot be ranked on coding.

Pick GLM 4.6V for output tokens 4.7x cheaper ($0.90 vs $4.25 per million).

Model AGLM 4.6VZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BMuse Spark 1.3Meta57.4/1004 independent benchmarksNo cost per solved task
Shared results
31
GLM 4.6V only
7
Muse Spark 1.3 only
61
Like-for-like categories
5 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
GLM 4.6V not benchmarked, Muse Spark 1.3 57.4 from 4 benchmarks.
Widest category
Intelligence, 36.9 points apart on average over 1 shared benchmark.
Widest single gap
Humanity's Last Exam (Artificial Analysis run): GLM 4.6V 9.6% against Muse Spark 1.3 48.7%, 39.1 points apart on a 0 to 100 scale.
Output price
GLM 4.6V $0.90 against Muse Spark 1.3 $4.25 per million output tokens, 4.7x apart.
Context window
GLM 4.6V takes 131K tokens, Muse Spark 1.3 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
CategoryGLM 4.6VBarsMuse Spark 1.3Ties
Coding02
Intelligence01
Reasoning07
Knowledge04
General017
All shared boards031
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for GLM 4.6V and Muse Spark 1.3
BenchmarkGLM 4.6VMuse Spark 1.3Leader
LiveBench codingNot measured90.2%Only Muse Spark 1.3 measured
LMArena WebDevNot measured1659Only Muse Spark 1.3 measured
AA Coding IndexNot measured75.8Only Muse Spark 1.3 measured
Terminal-Bench 4 (Vals AI)Not measured10.6%Only Muse Spark 1.3 measured
Vibe Code Bench (Vals AI)Not measured82.9%Only Muse Spark 1.3 measured
Code migration (Vals AI)Not measured27.6%Only Muse Spark 1.3 measured
ProgramBench (Vals AI)Not measured0.5%Only Muse Spark 1.3 measured
IOI (Vals AI)Not measured43.9%Only Muse Spark 1.3 measured
Terminal-Bench 2.1 (Vals AI)Not measured72.3%Only Muse Spark 1.3 measured
CyberBench (Vals AI)Not measured69.4%Only Muse Spark 1.3 measured
LiveCodeBench (Artificial Analysis run)41.1%Not measuredOnly GLM 4.6V measured
SciCodeNot measured59.7%Only Muse Spark 1.3 measured
Terminal-Bench Hard (Artificial Analysis run)14.4%Not measuredOnly GLM 4.6V measured
FrontierSWENot measured25.8%Only Muse Spark 1.3 measured
ALE-BenchNot measured1688.3Only Muse Spark 1.3 measured
LMArena Text: coding13891510Muse Spark 1.3
LMArena Text: software and IT13911511Muse Spark 1.3
LMArena WebDev: ReactNot measured1672Only Muse Spark 1.3 measured
LMArena WebDev: HTMLNot measured1653Only Muse Spark 1.3 measured
LMArena WebDev: from an imageNot measured1640Only Muse Spark 1.3 measured
Neither model has a result on the other 19 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.6V and Muse Spark 1.3
BenchmarkGLM 4.6VMuse Spark 1.3Leader
AA Intelligence Index11.248.1Muse Spark 1.3
Epoch Capabilities IndexNot measured157.2Only Muse Spark 1.3 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index17 boards, none shared
Agentic work benchmarks for GLM 4.6V and Muse Spark 1.3
BenchmarkGLM 4.6VMuse Spark 1.3Leader
AA Agentic IndexNot measured55.5Only Muse Spark 1.3 measured
Design Arena: agents building web appsNot measured1297Only Muse Spark 1.3 measured
Design Arena: agents building full-stack appsNot measured1295Only Muse Spark 1.3 measured
Design Arena: agents building mobile appsNot measured1218Only Muse Spark 1.3 measured
Design Arena: agents building Godot gamesNot measured1241Only Muse Spark 1.3 measured
Design Arena: PowerPoint slidesNot measured1219Only Muse Spark 1.3 measured
Design Arena: spreadsheetsNot measured1062Only Muse Spark 1.3 measured
LiveBench researchNot measured68.6%Only Muse Spark 1.3 measured
LiveBench browserNot measured80.0%Only Muse Spark 1.3 measured
LiveBench long agenticNot measured40.4%Only Muse Spark 1.3 measured
Finance Agent (Vals AI)Not measured58.9%Only Muse Spark 1.3 measured
Tax Agent Bench (Vals AI)Not measured36.0%Only Muse Spark 1.3 measured
Legal Research Bench (Vals AI)Not measured40.9%Only Muse Spark 1.3 measured
Excel Modeling Benchmark (Vals AI)Not measured62.7%Only Muse Spark 1.3 measured
Terminal-Bench-Science (Vals AI)Not measured4.3%Only Muse Spark 1.3 measured
τ²-Bench Telecom31.6%Not measuredOnly GLM 4.6V measured
APEX-AgentsNot measured58.6%Only Muse Spark 1.3 measured
Neither model has a result on the other 24 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for GLM 4.6V and Muse Spark 1.3
BenchmarkGLM 4.6VMuse Spark 1.3Leader
LiveBench reasoningNot measured89.7%Only Muse Spark 1.3 measured
GPQA Diamond (Artificial Analysis run)71.9%94.1%Muse Spark 1.3
Humanity's Last Exam (Artificial Analysis run)9.6%48.7%Muse Spark 1.3
Chess puzzlesNot measured37.0%Only Muse Spark 1.3 measured
Mystery game puzzlesNot measured25.0%Only Muse Spark 1.3 measured
LMArena Text: hard prompts13671503Muse Spark 1.3
LMArena Text: expert13711519Muse Spark 1.3
LMArena Text: hard prompts in English13911500Muse Spark 1.3
LMArena Vision: diagrams11821330Muse Spark 1.3
LMArena Vision: homework11551296Muse Spark 1.3
Neither model has a result on the other 12 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Math benchmarks for GLM 4.6V and Muse Spark 1.3
BenchmarkGLM 4.6VMuse Spark 1.3Leader
AA Math Index85.3Not measuredOnly GLM 4.6V measured
AIME 2025 (Artificial Analysis run)85.3%Not measuredOnly GLM 4.6V measured
OTIS Mock AIMENot measured99.2%Only Muse Spark 1.3 measured
FrontierMath, tiers 1 to 3Not measured74.4%Only Muse Spark 1.3 measured
FrontierMath, tier 4Not measured46.3%Only Muse Spark 1.3 measured
ProofBenchNot measured58.0%Only Muse Spark 1.3 measured
LMArena Text: mathNot measured1497Only Muse Spark 1.3 measured
LMArena Text: mathematical workNot measured1491Only Muse Spark 1.3 measured
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GLM 4.6V and Muse Spark 1.3
BenchmarkGLM 4.6VMuse Spark 1.3Leader
MMLU Pro (Artificial Analysis run)79.9%Not measuredOnly GLM 4.6V measured
LMArena Text: science13891507Muse Spark 1.3
LMArena Text: medicine and healthcare13891493Muse Spark 1.3
LMArena Text: legal and government13931499Muse Spark 1.3
LMArena Text: business and finance13591490Muse Spark 1.3
Neither model has a result on the other 13 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for GLM 4.6V and Muse Spark 1.3
BenchmarkGLM 4.6VMuse Spark 1.3Leader
Design Arena: websitesNot measured1356Only Muse Spark 1.3 measured
Design Arena: UI componentsNot measured1349Only Muse Spark 1.3 measured
Design Arena: game devNot measured1328Only Muse Spark 1.3 measured
Design Arena: data vizNot measured1343Only Muse Spark 1.3 measured
Design Arena: 3DNot measured1358Only Muse Spark 1.3 measured
Design Arena: all code categoriesNot measured1352Only Muse Spark 1.3 measured
Design Arena: SVGNot measured1301Only Muse Spark 1.3 measured
Design Arena: ASCII artNot measured1278Only Muse Spark 1.3 measured
LiveBench designNot measured74.8%Only Muse Spark 1.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
General benchmarks for GLM 4.6V and Muse Spark 1.3
BenchmarkGLM 4.6VMuse Spark 1.3Leader
LiveBench data analysisNot measured79.6%Only Muse Spark 1.3 measured
IFBench30.1%Not measuredOnly GLM 4.6V measured
Long context reasoning (AA-LCR)48.7%83.0%Muse Spark 1.3
DTBenchNot measured96.5%Only Muse Spark 1.3 measured
LMCANot measured53.9%Only Muse Spark 1.3 measured
LMArena Text13761490Muse Spark 1.3
LMArena Text: instruction following13501479Muse Spark 1.3
LMArena Text: creative writing13471462Muse Spark 1.3
LMArena Vision11641308Muse Spark 1.3
LMArena Text: English13951495Muse Spark 1.3
LMArena Text: non-English13581481Muse Spark 1.3
LMArena Text: Chinese14251532Muse Spark 1.3
LMArena Text: GermanNot measured1513Only Muse Spark 1.3 measured
LMArena Text: JapaneseNot measured1470Only Muse Spark 1.3 measured
LMArena Text: KoreanNot measured1490Only Muse Spark 1.3 measured
LMArena Text: FrenchNot measured1511Only Muse Spark 1.3 measured
LMArena Text: SpanishNot measured1496Only Muse Spark 1.3 measured
LMArena Text: Russian13391491Muse Spark 1.3
LMArena Text: PolishNot measured1490Only Muse Spark 1.3 measured
LMArena Text: multi-turn13591487Muse Spark 1.3
LMArena Text: longer queries13571488Muse Spark 1.3
LMArena Text: writing and language13471470Muse Spark 1.3
LMArena Text: entertainment and media13431457Muse Spark 1.3
LMArena Vision: English11791298Muse Spark 1.3
LMArena Vision: Chinese12171384Muse Spark 1.3
LMArena Vision: reading text11711319Muse Spark 1.3
LMArena Vision: humorNot measured1293Only Muse Spark 1.3 measured
LMArena Vision: creative writing11131296Muse Spark 1.3
Neither model has a result on the other 18 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.6V and Muse Spark 1.3
BenchmarkGLM 4.6VMuse Spark 1.3Leader
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

GLM 4.6V$0.0008Fits in one request
Muse Spark 1.3$0.0034Fits in one request

GLM 4.6V costs less

Repository review

50K input + 3K output tokens

GLM 4.6V$0.018Fits in one request
Muse Spark 1.3$0.075Fits in one request

GLM 4.6V costs less

Agent loop

200K input + 10K output tokens

GLM 4.6V$0.069Exceeds the context window
Muse Spark 1.3$0.292Fits in one request

GLM 4.6V 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 GLM 4.6V and Muse Spark 1.3
SpecificationZ.ai GLM 4.6VMeta Muse Spark 1.3
MakerZ.aiMeta
WeightsOpen weightsNo public weights listed
ReleasedDecember 8, 2025September 2, 2026
Context window131K1.05M
Input $/Mtok$0.30$1.25
Output $/Mtok$0.90$4.25
Coding indexNot benchmarked57.4
Independent measurements behind it04
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6V better than Muse Spark 1.3 for coding?

Only Muse Spark 1.3 has a coding score, 57.4 on the Vibe Coding Index; GLM 4.6V has none, so the two cannot be ranked on coding. Pick GLM 4.6V for output tokens 4.7x cheaper ($0.90 vs $4.25 per million).

Which is cheaper, GLM 4.6V or Muse Spark 1.3?

GLM 4.6V costs $0.30 per million input tokens and $0.90 per million output tokens; Muse Spark 1.3 costs $1.25 and $4.25. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.6V or Muse Spark 1.3?

Muse Spark 1.3, with 1.05M tokens against 131K.

How many benchmarks is the GLM 4.6V vs Muse Spark 1.3 comparison based on?

31 benchmarks have a result for both models as of October 22, 2026. GLM 4.6V's coding index rests on 0 independent benchmarks and Muse Spark 1.3's on 4. Where only one model has a result, the page says so instead of naming a winner.

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