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GLM 4.5 vs Muse Spark 1.3

Updated October 21, 2026. 40 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

GLM 4.5Z.ai
Coding
64.2
Overall
12.8
Output $/M
$2.20
0
Muse Spark 1.3Meta
Coding
79.7
Overall
48.1
Output $/M
$4.25
40
vs boards won of 40 shared

Decision reading

Pick Muse Spark 1.3 for coding: it scores 79.7 on the Vibe Coding Index against 64.2 for GLM 4.5.

Pick GLM 4.5 when output tokens cost 1.9x less.

Shared results
40
GLM 4.5 only
20
Muse Spark 1.3 only
52
Like-for-like categories
7 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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 4.5 64.2 from 1 benchmark, Muse Spark 1.3 79.7 from 2 benchmarks.
Widest category
Intelligence, 35.3 points apart on average over 1 shared benchmark.
Widest single gap
ALE-Bench: GLM 4.5 344.8 against Muse Spark 1.3 1688.3, 44.8 points apart on a 0 to 100 scale.
Output price
GLM 4.5 $2.20 against Muse Spark 1.3 $4.25 per million output tokens, 1.9x apart.
Context window
GLM 4.5 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 4.5BarsMuse Spark 1.3Ties
Coding03
Intelligence01
Reasoning05
Math02
Knowledge04
Web design07
General018
All shared boards040
CodingLike-for-likeFour of these boards feed the Vibe Coding Index03
Coding benchmarks for GLM 4.5 and Muse Spark 1.3
BenchmarkGLM 4.5Muse Spark 1.3Leader
SWE-bench Verified64.2%Not measuredOnly GLM 4.5 measured
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
LiveCodeBench (Vals AI)67.5%Not measuredOnly GLM 4.5 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)73.8%Not measuredOnly GLM 4.5 measured
SciCodeNot measured59.7%Only Muse Spark 1.3 measured
Terminal-Bench Hard (Artificial Analysis run)22.0%Not measuredOnly GLM 4.5 measured
FrontierSWENot measured25.8%Only Muse Spark 1.3 measured
WeirdML40.6%Not measuredOnly GLM 4.5 measured
ALE-Bench344.81688.3Muse Spark 1.3
LMArena Text: coding14341510Muse Spark 1.3
LMArena Text: software and IT14401511Muse 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 15 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.5 and Muse Spark 1.3
BenchmarkGLM 4.5Muse Spark 1.3Leader
AA Intelligence Index12.848.1Muse 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 4.5 and Muse Spark 1.3
BenchmarkGLM 4.5Muse 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 measured1217Only Muse Spark 1.3 measured
Design Arena: agents building Godot gamesNot measured1241Only Muse Spark 1.3 measured
Design Arena: PowerPoint slidesNot measured1244Only Muse Spark 1.3 measured
Design Arena: spreadsheetsNot measured1048Only 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 Telecom43.0%Not measuredOnly GLM 4.5 measured
APEX-AgentsNot measured58.6%Only Muse Spark 1.3 measured
Neither model has a result on the other 22 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for GLM 4.5 and Muse Spark 1.3
BenchmarkGLM 4.5Muse Spark 1.3Leader
GPQA Diamond72.2%Not measuredOnly GLM 4.5 measured
LiveBench reasoningNot measured89.7%Only Muse Spark 1.3 measured
GPQA Diamond (Artificial Analysis run)78.2%94.1%Muse Spark 1.3
Humanity's Last Exam (Artificial Analysis run)13.0%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 prompts14291503Muse Spark 1.3
LMArena Text: expert14331519Muse Spark 1.3
LMArena Text: hard prompts in English14291500Muse Spark 1.3
LMArena Vision: diagramsNot measured1330Only Muse Spark 1.3 measured
LMArena Vision: homeworkNot measured1296Only Muse Spark 1.3 measured
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GLM 4.5 and Muse Spark 1.3
BenchmarkGLM 4.5Muse Spark 1.3Leader
AIME86.7%Not measuredOnly GLM 4.5 measured
MATH 500 (Vals AI)94.0%Not measuredOnly GLM 4.5 measured
MGSM (Vals AI)90.8%Not measuredOnly GLM 4.5 measured
AA Math Index73.7Not measuredOnly GLM 4.5 measured
AIME 2025 (Artificial Analysis run)73.7%Not measuredOnly GLM 4.5 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: math14271497Muse Spark 1.3
LMArena Text: mathematical work14301491Muse Spark 1.3
Neither model has a result on the other 2 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GLM 4.5 and Muse Spark 1.3
BenchmarkGLM 4.5Muse Spark 1.3Leader
MMLU Pro81.2%Not measuredOnly GLM 4.5 measured
LegalBench (Vals AI)75.6%Not measuredOnly GLM 4.5 measured
CorpFin (Vals AI)61.0%Not measuredOnly GLM 4.5 measured
TaxEval (Vals AI)72.4%Not measuredOnly GLM 4.5 measured
MedQA (Vals AI)90.0%Not measuredOnly GLM 4.5 measured
MMLU Pro (Artificial Analysis run)83.5%Not measuredOnly GLM 4.5 measured
LMArena Text: science14441507Muse Spark 1.3
LMArena Text: medicine and healthcare14551493Muse Spark 1.3
LMArena Text: legal and government14381499Muse Spark 1.3
LMArena Text: business and finance14201490Muse Spark 1.3
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for GLM 4.5 and Muse Spark 1.3
BenchmarkGLM 4.5Muse Spark 1.3Leader
Design Arena: websites11701356Muse Spark 1.3
Design Arena: UI components11441352Muse Spark 1.3
Design Arena: game dev11451329Muse Spark 1.3
Design Arena: data viz11591343Muse Spark 1.3
Design Arena: 3D11701359Muse Spark 1.3
Design Arena: all code categories11651353Muse Spark 1.3
Design Arena: SVG10961300Muse Spark 1.3
Design Arena: ASCII artNot measured1280Only 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 index018
General benchmarks for GLM 4.5 and Muse Spark 1.3
BenchmarkGLM 4.5Muse Spark 1.3Leader
LiveBench data analysisNot measured79.6%Only Muse Spark 1.3 measured
IFBench44.1%Not measuredOnly GLM 4.5 measured
Long context reasoning (AA-LCR)52.7%83.0%Muse Spark 1.3
DTBenchNot measured96.5%Only Muse Spark 1.3 measured
LMCANot measured53.9%Only Muse Spark 1.3 measured
Creative writing (Lech Mazur)73.4%Not measuredOnly GLM 4.5 measured
LMArena Text14301490Muse Spark 1.3
LMArena Text: instruction following14041479Muse Spark 1.3
LMArena Text: creative writing13951462Muse Spark 1.3
LMArena VisionNot measured1308Only Muse Spark 1.3 measured
LMArena Text: English14371495Muse Spark 1.3
LMArena Text: non-English14171481Muse Spark 1.3
LMArena Text: Chinese14651532Muse Spark 1.3
LMArena Text: German14071513Muse Spark 1.3
LMArena Text: Japanese14151470Muse Spark 1.3
LMArena Text: Korean13801490Muse Spark 1.3
LMArena Text: French14181511Muse Spark 1.3
LMArena Text: Spanish14541496Muse Spark 1.3
LMArena Text: Russian14131491Muse Spark 1.3
LMArena Text: Polish14221490Muse Spark 1.3
LMArena Text: multi-turn14151487Muse Spark 1.3
LMArena Text: longer queries14121488Muse Spark 1.3
LMArena Text: writing and language14031470Muse Spark 1.3
LMArena Text: entertainment and media14041457Muse Spark 1.3
LMArena Vision: EnglishNot measured1298Only Muse Spark 1.3 measured
LMArena Vision: ChineseNot measured1384Only Muse Spark 1.3 measured
LMArena Vision: reading textNot measured1319Only Muse Spark 1.3 measured
LMArena Vision: humorNot measured1293Only Muse Spark 1.3 measured
LMArena Vision: creative writingNot measured1296Only Muse Spark 1.3 measured
Neither model has a result on the other 17 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.5 and Muse Spark 1.3
BenchmarkGLM 4.5Muse 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 4.5$0.0017Fits in one request
Muse Spark 1.3$0.0034Fits in one request

GLM 4.5 costs less

Repository review

50K input + 3K output tokens

GLM 4.5$0.037Fits in one request
Muse Spark 1.3$0.075Fits in one request

GLM 4.5 costs less

Agent loop

200K input + 10K output tokens

GLM 4.5$0.142Exceeds the context window
Muse Spark 1.3$0.292Fits in one request

GLM 4.5 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 4.5 and Muse Spark 1.3
SpecificationZ.ai GLM 4.5Meta Muse Spark 1.3
MakerZ.aiMeta
WeightsOpen weightsNo public weights listed
ReleasedJuly 25, 2025September 2, 2026
Context window131K1.05M
Input $/Mtok$0.60$1.25
Output $/Mtok$2.20$4.25
Coding index64.279.7
Independent measurements behind it12
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5 better than Muse Spark 1.3 for coding?

Pick Muse Spark 1.3 for coding: it scores 79.7 on the Vibe Coding Index against 64.2 for GLM 4.5. Pick GLM 4.5 when output tokens cost 1.9x less.

Which is cheaper, GLM 4.5 or Muse Spark 1.3?

GLM 4.5 costs $0.60 per million input tokens and $2.20 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 4.5 or Muse Spark 1.3?

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

How many benchmarks is the GLM 4.5 vs Muse Spark 1.3 comparison based on?

40 benchmarks have a result for both models as of October 21, 2026. GLM 4.5's coding index rests on 1 independent benchmark and Muse Spark 1.3's on 2. Where only one model has a result, the page says so instead of naming a winner.

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