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GLM 5.2 vs Muse Spark 1.1

Muse Spark 1.1 leads GLM 5.2 on coding, 70.3 to 61.1 on the Vibe Coding Index, at about the same output price ($4.25 vs $4.40 per million). Updated October 22, 2026; 79 benchmarks have a result for both.

Replace GLM 5.2 Replace Muse Spark 1.1
GLM 5.2Z.ai
Coding
61.1
Overall
62.5
Output $/M
$4.40
37
Muse Spark 1.1Meta
Coding
70.3
Overall
67.0
Output $/M
$4.25
40
vs boards won of 79 shared, 2 tied

GLM 5.2 or Muse Spark 1.1: which to pick

Pick Muse Spark 1.1 for coding: it scores 70.3 on the Vibe Coding Index against 61.1 for GLM 5.2, though on 3 benchmarks against 4.

Pick GLM 5.2 for work measured by LMArena WebDev or SWE-bench Verified (Vals AI), where it leads.

Model AGLM 5.2Z.ai61.1/1004 independent benchmarks$0.86 per solved taskModel BMuse Spark 1.1Meta70.3/1003 independent benchmarks$0.43 per solved task
Shared results
79
GLM 5.2 only
19
Muse Spark 1.1 only
17
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
GLM 5.2 61.1 from 4 benchmarks, Muse Spark 1.1 70.3 from 3 benchmarks.
Widest category
Coding, 6.2 points apart on average over 14 shared benchmarks.
Widest single gap
SimpleQA Verified: GLM 5.2 34.2% against Muse Spark 1.1 57.8%, 23.6 points apart on a 0 to 100 scale.
Output price
GLM 5.2 $4.40 against Muse Spark 1.1 $4.25 per million output tokens, 1.0x 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 5.2BarsMuse Spark 1.1Ties
Coding68
Intelligence011 tie
Agentic work941 tie
Reasoning25
Math21
Knowledge27
Web design53
General1111
All shared boards37402 ties
CodingLike-for-likeSix of these boards feed the Vibe Coding Index68
Coding benchmarks for GLM 5.2 and Muse Spark 1.1
BenchmarkGLM 5.2Muse Spark 1.1Leader
LMArena WebDev16041542GLM 5.2
AA Coding Index68.871.3Muse Spark 1.1
LiveCodeBench (Vals AI)69.5%85.9%Muse Spark 1.1
SWE-bench Verified (Vals AI)82.8%82.0%GLM 5.2
Vibe Code Bench (Vals AI)64.0%72.2%Muse Spark 1.1
Code migration (Vals AI)37.9%31.1%GLM 5.2
ProgramBench (Vals AI)0.5%0.0%GLM 5.2
Terminal-Bench 2.1 (Vals AI)67.8%69.3%Muse Spark 1.1
SkillsBench (Vals AI)45.1%59.2%Muse Spark 1.1
SciCode51.2%58.8%Muse Spark 1.1
Terminal-Bench Hard (Artificial Analysis run)50.8%Not measuredOnly GLM 5.2 measured
FrontierCode24.5%Not measuredOnly GLM 5.2 measured
WeirdML70.1%Not measuredOnly GLM 5.2 measured
ALE-Bench1047.0Not measuredOnly GLM 5.2 measured
LMArena Text: coding14871498Muse Spark 1.1
LMArena Text: software and IT14861498Muse Spark 1.1
LMArena WebDev: React16171532GLM 5.2
LMArena WebDev: HTML15651540GLM 5.2
LMArena WebDev: from an imageNot measured1540Only Muse Spark 1.1 measured
Neither model has a result on the other 20 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 5.2 and Muse Spark 1.1
BenchmarkGLM 5.2Muse Spark 1.1Leader
AA Intelligence Index33.733.7Tie
Epoch Capabilities Index151.9154.1Muse Spark 1.1
Agentic workLike-for-likePublic boards, shown as published, kept out of the index94
Agentic work benchmarks for GLM 5.2 and Muse Spark 1.1
BenchmarkGLM 5.2Muse Spark 1.1Leader
AA Agentic Index38.425.8GLM 5.2
Design Arena: agents building web apps12221207GLM 5.2
Design Arena: agents building full-stack apps12411199GLM 5.2
Design Arena: agents building mobile apps11591175Muse Spark 1.1
Design Arena: agents building Android apps11781178Tie
Design Arena: agents building Godot games11421103GLM 5.2
Design Arena: agents building games12061183GLM 5.2
Design Arena: HTML slides11251195Muse Spark 1.1
Design Arena: PowerPoint slides11701155GLM 5.2
Design Arena: spreadsheetsNot measured1042Only Muse Spark 1.1 measured
SREBench (Vals AI)0.0%Not measuredOnly GLM 5.2 measured
Finance Agent (Vals AI)49.7%57.2%Muse Spark 1.1
Legal Research Bench (Vals AI)31.3%38.0%Muse Spark 1.1
Excel Modeling Benchmark (Vals AI)61.5%56.4%GLM 5.2
Time Horizon Index (Vals AI)Not measured1.7%Only Muse Spark 1.1 measured
τ²-Bench Telecom99.1%Not measuredOnly GLM 5.2 measured
APEX-Agents45.2%31.8%GLM 5.2
PostTrainBench31.7%Not measuredOnly GLM 5.2 measured
Vending-Bench 2$8,314$6,520GLM 5.2
Neither model has a result on the other 22 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index25
Reasoning benchmarks for GLM 5.2 and Muse Spark 1.1
BenchmarkGLM 5.2Muse Spark 1.1Leader
GPQA Diamond85.6%91.2%Muse Spark 1.1
MMMU Pro (Vals AI)Not measured86.6%Only Muse Spark 1.1 measured
GPQA Diamond (Artificial Analysis run)89.5%89.8%Muse Spark 1.1
Humanity's Last Exam (Artificial Analysis run)41.1%46.2%Muse Spark 1.1
GPQA Diamond (Epoch AI run)91.9%Not measuredOnly GLM 5.2 measured
ARC-AGI77.0%Not measuredOnly GLM 5.2 measured
ARC-AGI-222.8%Not measuredOnly GLM 5.2 measured
SimpleBench58.8%Not measuredOnly GLM 5.2 measured
Chess puzzles22.0%34.0%Muse Spark 1.1
Mystery game puzzles19.0%Not measuredOnly GLM 5.2 measured
EBR-bench9.5%Not measuredOnly GLM 5.2 measured
LMArena Text: hard prompts14811485Muse Spark 1.1
LMArena Text: expert14871478GLM 5.2
LMArena Text: hard prompts in English14811480GLM 5.2
LMArena Vision: diagramsNot measured1305Only Muse Spark 1.1 measured
LMArena Vision: homeworkNot measured1287Only Muse Spark 1.1 measured
Kagi LLM Benchmark62.6%Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 5 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index21
Math benchmarks for GLM 5.2 and Muse Spark 1.1
BenchmarkGLM 5.2Muse Spark 1.1Leader
OTIS Mock AIME86.4%Not measuredOnly GLM 5.2 measured
FrontierMath, tiers 1 to 359.2%Not measuredOnly GLM 5.2 measured
FrontierMath, tier 429.3%Not measuredOnly GLM 5.2 measured
ProofBench35.0%39.0%Muse Spark 1.1
LMArena Text: math14831482GLM 5.2
LMArena Text: mathematical work14891487GLM 5.2
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index27
Knowledge benchmarks for GLM 5.2 and Muse Spark 1.1
BenchmarkGLM 5.2Muse Spark 1.1Leader
MMLU Pro86.7%88.7%Muse Spark 1.1
LegalBench (Vals AI)84.1%85.0%Muse Spark 1.1
CorpFin (Vals AI)66.1%71.3%Muse Spark 1.1
TaxEval (Vals AI)73.3%79.7%Muse Spark 1.1
MortgageTax (Vals AI)Not measured66.1%Only Muse Spark 1.1 measured
MedCode (Vals AI)40.8%Not measuredOnly GLM 5.2 measured
SimpleQA Verified34.2%57.8%Muse Spark 1.1
LMArena Text: science14891484GLM 5.2
LMArena Text: medicine and healthcare14751479Muse Spark 1.1
LMArena Text: legal and government14861481GLM 5.2
LMArena Text: business and finance14511470Muse Spark 1.1
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index53
Web design benchmarks for GLM 5.2 and Muse Spark 1.1
BenchmarkGLM 5.2Muse Spark 1.1Leader
Design Arena: websites12851270GLM 5.2
Design Arena: UI components12941289GLM 5.2
Design Arena: game dev12681269Muse Spark 1.1
Design Arena: data viz12851268GLM 5.2
Design Arena: 3D12871260GLM 5.2
Design Arena: all code categories12871276GLM 5.2
Design Arena: SVG12131235Muse Spark 1.1
Design Arena: ASCII art11911271Muse Spark 1.1
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index1111
General benchmarks for GLM 5.2 and Muse Spark 1.1
BenchmarkGLM 5.2Muse Spark 1.1Leader
Public Benefits Bench (Vals AI)Not measured61.3%Only Muse Spark 1.1 measured
SAGE (Vals AI)Not measured45.6%Only Muse Spark 1.1 measured
MedScribe (Vals AI)83.5%88.9%Muse Spark 1.1
Vals Multimodal IndexNot measured66.7%Only Muse Spark 1.1 measured
IFBench73.3%Not measuredOnly GLM 5.2 measured
Long context reasoning (AA-LCR)78.3%77.7%GLM 5.2
DTBench93.6%94.4%Muse Spark 1.1
LMCA45.8%49.9%Muse Spark 1.1
Surface Evolver Bench55.6%52.5%GLM 5.2
LMArena Text14701478Muse Spark 1.1
LMArena Text: instruction following14661457GLM 5.2
LMArena Text: creative writing14631435GLM 5.2
LMArena VisionNot measured1293Only Muse Spark 1.1 measured
LMArena Text: English14751478Muse Spark 1.1
LMArena Text: non-English14591471Muse Spark 1.1
LMArena Text: Chinese15221515GLM 5.2
LMArena Text: German14701469GLM 5.2
LMArena Text: Japanese14501450GLM 5.2
LMArena Text: Korean14401459Muse Spark 1.1
LMArena Text: French14791492Muse Spark 1.1
LMArena Text: Spanish14781464GLM 5.2
LMArena Text: Russian14681482Muse Spark 1.1
LMArena Text: Polish14561499Muse Spark 1.1
LMArena Text: multi-turn14691484Muse Spark 1.1
LMArena Text: longer queries14801462GLM 5.2
LMArena Text: writing and language14631448GLM 5.2
LMArena Text: entertainment and media14501438GLM 5.2
LMArena Vision: EnglishNot measured1292Only Muse Spark 1.1 measured
LMArena Vision: ChineseNot measured1348Only Muse Spark 1.1 measured
LMArena Vision: reading textNot measured1301Only Muse Spark 1.1 measured
LMArena Vision: humorNot measured1288Only Muse Spark 1.1 measured
LMArena Vision: creative writingNot measured1253Only Muse Spark 1.1 measured
LMArena DocumentNot measured1465Only Muse Spark 1.1 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 GLM 5.2 and Muse Spark 1.1
BenchmarkGLM 5.2Muse Spark 1.1Leader
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 5.2$0.0036Fits in one request
Muse Spark 1.1$0.0034Fits in one request

Muse Spark 1.1 costs less

Repository review

50K input + 3K output tokens

GLM 5.2$0.083Fits in one request
Muse Spark 1.1$0.075Fits in one request

Muse Spark 1.1 costs less

Agent loop

200K input + 10K output tokens

GLM 5.2$0.324Fits in one request
Muse Spark 1.1$0.292Fits in one request

Muse Spark 1.1 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 5.2 and Muse Spark 1.1
SpecificationZ.ai GLM 5.2Meta Muse Spark 1.1
MakerZ.aiMeta
WeightsOpen weightsNo public weights listed
ReleasedJune 16, 2026July 16, 2026
Context window1.05M1.05M
Input $/Mtok$1.40$1.25
Output $/Mtok$4.40$4.25
Coding index61.170.3
Independent measurements behind it43
Cost per solved task$0.86$0.43
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.2 better than Muse Spark 1.1 for coding?

Pick Muse Spark 1.1 for coding: it scores 70.3 on the Vibe Coding Index against 61.1 for GLM 5.2, though on 3 benchmarks against 4. Pick GLM 5.2 for work measured by LMArena WebDev or SWE-bench Verified (Vals AI), where it leads.

Which is cheaper, GLM 5.2 or Muse Spark 1.1?

GLM 5.2 costs $1.40 per million input tokens and $4.40 per million output tokens; Muse Spark 1.1 costs $1.25 and $4.25. Per solved SWE-bench Verified task, GLM 5.2 costs $0.86 and Muse Spark 1.1 $0.43.

Which has the bigger context window, GLM 5.2 or Muse Spark 1.1?

Neither: both take 1.05M tokens.

How many benchmarks is the GLM 5.2 vs Muse Spark 1.1 comparison based on?

79 benchmarks have a result for both models as of October 22, 2026. GLM 5.2's coding index rests on 4 independent benchmarks and Muse Spark 1.1's on 3. 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-5-2-vs-meta-muse-spark-1-1">LLMBenchmark.ai, GLM 5.2 vs Muse Spark 1.1</a></p>