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GLM 5.3 vs Muse Spark 1.2

Updated October 21, 2026. 82 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 5.3 Replace Muse Spark 1.2
GLM 5.3Z.ai
Coding
73.1
Overall
44.8
Output $/M
$1.40
50
Muse Spark 1.2Meta
Coding
65.1
Overall
39.6
Output $/M
$4.25
32
vs boards won of 82 shared

Decision reading

Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 65.1 for Muse Spark 1.2.

Pick Muse Spark 1.2 when what matters is Vibe Code Bench (Vals AI) or SkillsBench (Vals AI), where it leads.

Model AGLM 5.3Z.ai73.1/1002 independent benchmarks$0.36 per solved taskModel BMuse Spark 1.2Meta65.1/1002 independent benchmarks$0.64 per solved task
Shared results
82
GLM 5.3 only
18
Muse Spark 1.2 only
15
Like-for-like categories
8 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 5.3 73.1 from 2 benchmarks, Muse Spark 1.2 65.1 from 2 benchmarks.
Widest category
Coding, 10.2 points apart on average over 19 shared benchmarks.
Widest single gap
IOI (Vals AI): GLM 5.3 68.4% against Muse Spark 1.2 21.8%, 46.7 points apart on a 0 to 100 scale.
Output price
GLM 5.3 $1.40 against Muse Spark 1.2 $4.25 per million output tokens, 3.0x apart.
Context window
Both take 1.05M tokens.

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 5.3BarsMuse Spark 1.2Ties
Coding154
Intelligence20
Agentic work103
Reasoning52
Math30
Knowledge09
Web design54
General1010
All shared boards5032
CodingLike-for-likeFour of these boards feed the Vibe Coding Index154
Coding benchmarks for GLM 5.3 and Muse Spark 1.2
BenchmarkGLM 5.3Muse Spark 1.2Leader
LiveBench coding82.8%78.2%GLM 5.3
LMArena WebDev16211534GLM 5.3
AA Coding Index74.872.2GLM 5.3
LiveCodeBench (Vals AI)80.5%Not measuredOnly GLM 5.3 measured
SWE-bench Verified (Vals AI)95.4%86.6%GLM 5.3
Terminal-Bench 4 (Vals AI)38.9%6.1%GLM 5.3
Vibe Code Bench (Vals AI)78.1%79.1%Muse Spark 1.2
Code migration (Vals AI)44.2%29.9%GLM 5.3
ProgramBench (Vals AI)1.5%0.5%GLM 5.3
IOI (Vals AI)68.4%21.8%GLM 5.3
Terminal-Bench 2.1 (Vals AI)71.5%69.7%GLM 5.3
Vibe Code Bench 1-100 (Vals AI)20.0%Not measuredOnly GLM 5.3 measured
SkillsBench (Vals AI)47.5%53.0%Muse Spark 1.2
CyberBench (Vals AI)72.1%69.5%GLM 5.3
SciCode59.0%57.4%GLM 5.3
FrontierCode40.1%Not measuredOnly GLM 5.3 measured
FrontierSWE30.2%12.0%GLM 5.3
WeirdML75.4%60.3%GLM 5.3
ALE-Bench1317.4Not measuredOnly GLM 5.3 measured
LMArena Text: coding14971505Muse Spark 1.2
LMArena Text: software and IT14931505Muse Spark 1.2
LMArena WebDev: React16411526GLM 5.3
LMArena WebDev: HTML15951543GLM 5.3
Neither model has a result on the other 15 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 5.3 and Muse Spark 1.2
BenchmarkGLM 5.3Muse Spark 1.2Leader
AA Intelligence Index44.839.6GLM 5.3
Epoch Capabilities Index156.0155.1GLM 5.3
Agentic workLike-for-likePublic boards, shown as published, kept out of the index103
Agentic work benchmarks for GLM 5.3 and Muse Spark 1.2
BenchmarkGLM 5.3Muse Spark 1.2Leader
AA Agentic Index53.143.2GLM 5.3
Design Arena: agents building web appsNot measured1222Only Muse Spark 1.2 measured
Design Arena: agents building full-stack appsNot measured1210Only Muse Spark 1.2 measured
Design Arena: agents building mobile apps11941182GLM 5.3
Design Arena: agents building Android appsNot measured1228Only Muse Spark 1.2 measured
Design Arena: agents building gamesNot measured1179Only Muse Spark 1.2 measured
Design Arena: HTML slides11891135GLM 5.3
Design Arena: PowerPoint slides12551198GLM 5.3
LiveBench research73.2%67.7%GLM 5.3
LiveBench browser0.0%36.0%Muse Spark 1.2
LiveBench long agentic35.9%21.3%GLM 5.3
SREBench (Vals AI)9.2%0.0%GLM 5.3
Finance Agent (Vals AI)55.8%60.6%Muse Spark 1.2
Tax Agent Bench (Vals AI)39.7%32.8%GLM 5.3
Legal Research Bench (Vals AI)49.0%43.8%GLM 5.3
Harvey's Legal Agent Benchmark (Vals AI)14.6%Not measuredOnly GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)56.3%57.0%Muse Spark 1.2
BioMysteryBench (Vals AI)Not measured64.8%Only Muse Spark 1.2 measured
Terminal-Bench-Science (Vals AI)5.7%Not measuredOnly GLM 5.3 measured
APEX-Agents56.6%45.4%GLM 5.3
Vending-Bench 2$8,164Not measuredOnly GLM 5.3 measured
Neither model has a result on the other 18 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index52
Reasoning benchmarks for GLM 5.3 and Muse Spark 1.2
BenchmarkGLM 5.3Muse Spark 1.2Leader
GPQA Diamond88.1%Not measuredOnly GLM 5.3 measured
LiveBench reasoning85.8%90.0%Muse Spark 1.2
MysteryMechanism (Vals AI)23.0%Not measuredOnly GLM 5.3 measured
MMMU Pro (Vals AI)Not measured86.1%Only Muse Spark 1.2 measured
GPQA Diamond (Artificial Analysis run)91.7%90.4%GLM 5.3
Humanity's Last Exam (Artificial Analysis run)42.3%45.5%Muse Spark 1.2
GPQA Diamond (Epoch AI run)90.9%Not measuredOnly GLM 5.3 measured
SimpleBenchNot measured74.5%Only Muse Spark 1.2 measured
Chess puzzles37.0%27.0%GLM 5.3
Mystery game puzzles33.0%Not measuredOnly GLM 5.3 measured
LMArena Text: hard prompts14901489GLM 5.3
LMArena Text: expert15141475GLM 5.3
LMArena Text: hard prompts in English14961487GLM 5.3
LMArena Vision: diagramsNot measured1317Only Muse Spark 1.2 measured
Neither model has a result on the other 7 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GLM 5.3 and Muse Spark 1.2
BenchmarkGLM 5.3Muse Spark 1.2Leader
OTIS Mock AIME91.1%Not measuredOnly GLM 5.3 measured
FrontierMath, tiers 1 to 368.8%Not measuredOnly GLM 5.3 measured
FrontierMath, tier 429.3%Not measuredOnly GLM 5.3 measured
ProofBench49.0%43.0%GLM 5.3
LMArena Text: math14951484GLM 5.3
LMArena Text: mathematical work14971480GLM 5.3
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index09
Knowledge benchmarks for GLM 5.3 and Muse Spark 1.2
BenchmarkGLM 5.3Muse Spark 1.2Leader
MMLU Pro86.8%88.3%Muse Spark 1.2
LegalBench (Vals AI)84.8%85.3%Muse Spark 1.2
CorpFin (Vals AI)Not measured70.9%Only Muse Spark 1.2 measured
TaxEval (Vals AI)72.4%80.4%Muse Spark 1.2
MortgageTax (Vals AI)Not measured65.4%Only Muse Spark 1.2 measured
MedCode (Vals AI)42.9%49.4%Muse Spark 1.2
SimpleQA Verified41.0%60.3%Muse Spark 1.2
LMArena Text: science14981500Muse Spark 1.2
LMArena Text: medicine and healthcare14811496Muse Spark 1.2
LMArena Text: legal and government14731517Muse Spark 1.2
LMArena Text: business and finance14591484Muse Spark 1.2
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index54
Web design benchmarks for GLM 5.3 and Muse Spark 1.2
BenchmarkGLM 5.3Muse Spark 1.2Leader
Design Arena: websites12951313Muse Spark 1.2
Design Arena: UI components13141300GLM 5.3
Design Arena: game dev13111302GLM 5.3
Design Arena: data viz12601334Muse Spark 1.2
Design Arena: 3D13321301GLM 5.3
Design Arena: all code categories13041313Muse Spark 1.2
Design Arena: SVG12921285GLM 5.3
Design Arena: ASCII art12131284Muse Spark 1.2
LiveBench design77.3%67.1%GLM 5.3
GeneralLike-for-likePublic boards, shown as published, kept out of the index1010
General benchmarks for GLM 5.3 and Muse Spark 1.2
BenchmarkGLM 5.3Muse Spark 1.2Leader
Vals Index59.7%Not measuredOnly GLM 5.3 measured
LiveBench data analysis70.2%76.5%Muse Spark 1.2
Public Benefits Bench (Vals AI)68.5%68.5%GLM 5.3
SAGE (Vals AI)Not measured47.7%Only Muse Spark 1.2 measured
MedScribe (Vals AI)88.8%90.1%Muse Spark 1.2
Vals Multimodal IndexNot measured69.8%Only Muse Spark 1.2 measured
Long context reasoning (AA-LCR)79.7%79.0%GLM 5.3
DTBench87.7%94.7%Muse Spark 1.2
LMCA55.5%48.4%GLM 5.3
LMArena Text14741482Muse Spark 1.2
LMArena Text: instruction following14791462GLM 5.3
LMArena Text: creative writing14581436GLM 5.3
LMArena VisionNot measured1304Only Muse Spark 1.2 measured
LMArena Text: English14821483Muse Spark 1.2
LMArena Text: non-English14591476Muse Spark 1.2
LMArena Text: Chinese15321509GLM 5.3
LMArena Text: German1488Not measuredOnly GLM 5.3 measured
LMArena Text: Japanese1460Not measuredOnly GLM 5.3 measured
LMArena Text: Korean14601462Muse Spark 1.2
LMArena Text: French15031495GLM 5.3
LMArena Text: Spanish14621484Muse Spark 1.2
LMArena Text: Russian14691483Muse Spark 1.2
LMArena Text: Polish1466Not measuredOnly GLM 5.3 measured
LMArena Text: multi-turn14721502Muse Spark 1.2
LMArena Text: longer queries14831473GLM 5.3
LMArena Text: writing and language14691453GLM 5.3
LMArena Text: entertainment and media14471443GLM 5.3
LMArena Vision: EnglishNot measured1291Only Muse Spark 1.2 measured
LMArena Vision: reading textNot measured1313Only Muse Spark 1.2 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 5.3 and Muse Spark 1.2
BenchmarkGLM 5.3Muse 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

GLM 5.3$0.0008Fits in one request
Muse Spark 1.2$0.0034Fits in one request

GLM 5.3 costs less

Repository review

50K input + 3K output tokens

GLM 5.3$0.0081Fits in one request
Muse Spark 1.2$0.075Fits in one request

GLM 5.3 costs less

Agent loop

200K input + 10K output tokens

GLM 5.3$0.030Fits in one request
Muse Spark 1.2$0.292Fits in one request

GLM 5.3 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 5.3 and Muse Spark 1.2
SpecificationZ.ai GLM 5.3Meta Muse Spark 1.2
MakerZ.aiMeta
WeightsOpen weightsNo public weights listed
ReleasedAugust 18, 2026August 5, 2026
Context window1.05M1.05M
Input $/Mtok$0.079$1.25
Output $/Mtok$1.40$4.25
Coding index73.165.1
Independent measurements behind it22
Cost per solved task$0.36$0.64
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.3 better than Muse Spark 1.2 for coding?

Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 65.1 for Muse Spark 1.2. Pick Muse Spark 1.2 when what matters is Vibe Code Bench (Vals AI) or SkillsBench (Vals AI), where it leads.

Which is cheaper, GLM 5.3 or Muse Spark 1.2?

GLM 5.3 costs $0.079 per million input tokens and $1.40 per million output tokens; Muse Spark 1.2 costs $1.25 and $4.25. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and Muse Spark 1.2 $0.64.

Which has the bigger context window, GLM 5.3 or Muse Spark 1.2?

Neither: both take 1.05M tokens.

How many benchmarks is the GLM 5.3 vs Muse Spark 1.2 comparison based on?

82 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 2 independent benchmarks and Muse Spark 1.2's on 2. Where only one model has a result, the page says so instead of naming a winner.

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<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/glm-5-3-vs-meta-muse-spark-1-2">LLMBenchmark.ai, GLM 5.3 vs Muse Spark 1.2</a></p>