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GLM 4.7 vs Muse Spark 1.3

Muse Spark 1.3 leads GLM 4.7 on coding, 57.4 to 55.1 on the Vibe Coding Index, and GLM 4.7 costs 1.9x less per output token ($2.20 vs $4.25 per million). Updated October 22, 2026; 52 benchmarks have a result for both.

Replace GLM 4.7 Replace Muse Spark 1.3
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
0
Muse Spark 1.3Meta
Coding
57.4
Overall
68.6
Output $/M
$4.25
52
vs boards won of 52 shared

GLM 4.7 or Muse Spark 1.3: which to pick

Pick Muse Spark 1.3 for coding: it scores 57.4 on the Vibe Coding Index against 55.1 for GLM 4.7.

Pick GLM 4.7 for output tokens 1.9x cheaper ($2.20 vs $4.25 per million).

Model AGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved taskModel BMuse Spark 1.3Meta57.4/1004 independent benchmarksNo cost per solved task
Shared results
52
GLM 4.7 only
29
Muse Spark 1.3 only
40
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 4.7 55.1 from 2 benchmarks, Muse Spark 1.3 57.4 from 4 benchmarks.
Widest category
Coding, 25.7 points apart on average over 7 shared benchmarks.
Widest single gap
ProofBench: GLM 4.7 6.0% against Muse Spark 1.3 58.0%, 52.0 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $2.20 against Muse Spark 1.3 $4.25 per million output tokens, 1.9x apart.
Context window
GLM 4.7 takes 205K 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 4.7BarsMuse Spark 1.3Ties
Coding07
Intelligence02
Agentic work03
Reasoning06
Math04
Knowledge04
Web design08
General018
All shared boards052
CodingLike-for-likeSix of these boards feed the Vibe Coding Index07
Coding benchmarks for GLM 4.7 and Muse Spark 1.3
BenchmarkGLM 4.7Muse Spark 1.3Leader
LiveBench codingNot measured90.2%Only Muse Spark 1.3 measured
LMArena WebDev14351659Muse Spark 1.3
AA Coding Index45.375.8Muse Spark 1.3
LiveCodeBench (Vals AI)82.2%Not measuredOnly GLM 4.7 measured
SWE-bench Verified (Vals AI)69.4%Not measuredOnly GLM 4.7 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)89.4%Not measuredOnly GLM 4.7 measured
SciCodeNot measured59.7%Only Muse Spark 1.3 measured
Terminal-Bench Hard (Artificial Analysis run)31.8%Not measuredOnly GLM 4.7 measured
Terminal-Bench33.4%Not measuredOnly GLM 4.7 measured
FrontierSWENot measured25.8%Only Muse Spark 1.3 measured
ALE-Bench399.51688.3Muse Spark 1.3
LMArena Text: coding14541510Muse Spark 1.3
LMArena Text: software and IT14531511Muse Spark 1.3
LMArena WebDev: React14201672Muse Spark 1.3
LMArena WebDev: HTML14471653Muse Spark 1.3
LMArena WebDev: from an imageNot measured1640Only Muse Spark 1.3 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 GLM 4.7 and Muse Spark 1.3
BenchmarkGLM 4.7Muse Spark 1.3Leader
AA Intelligence Index22.248.1Muse Spark 1.3
Epoch Capabilities Index143.6157.2Muse Spark 1.3
Agentic workLike-for-likePublic boards, shown as published, kept out of the index03
Agentic work benchmarks for GLM 4.7 and Muse Spark 1.3
BenchmarkGLM 4.7Muse 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 apps10501295Muse Spark 1.3
Design Arena: agents building mobile apps11051218Muse Spark 1.3
Design Arena: agents building Android apps1112Not measuredOnly GLM 4.7 measured
Design Arena: agents building Godot games10581241Muse Spark 1.3
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 Telecom95.9%Not measuredOnly GLM 4.7 measured
APEX-AgentsNot measured58.6%Only Muse Spark 1.3 measured
Vending-Bench 2$2,377Not measuredOnly GLM 4.7 measured
BrowseComp-Plus64.7%Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 21 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for GLM 4.7 and Muse Spark 1.3
BenchmarkGLM 4.7Muse Spark 1.3Leader
GPQA Diamond80.0%Not measuredOnly GLM 4.7 measured
LiveBench reasoningNot measured89.7%Only Muse Spark 1.3 measured
GPQA Diamond (Artificial Analysis run)85.9%94.1%Muse Spark 1.3
Humanity's Last Exam (Artificial Analysis run)27.4%48.7%Muse Spark 1.3
GPQA Diamond (Epoch AI run)83.3%Not measuredOnly GLM 4.7 measured
SimpleBench47.7%Not measuredOnly GLM 4.7 measured
Chess puzzles21.0%37.0%Muse Spark 1.3
Mystery game puzzlesNot measured25.0%Only Muse Spark 1.3 measured
LMArena Text: hard prompts14431503Muse Spark 1.3
LMArena Text: expert14241519Muse Spark 1.3
LMArena Text: hard prompts in English14521500Muse 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 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index04
Math benchmarks for GLM 4.7 and Muse Spark 1.3
BenchmarkGLM 4.7Muse Spark 1.3Leader
AIME93.3%Not measuredOnly GLM 4.7 measured
MGSM (Vals AI)88.2%Not measuredOnly GLM 4.7 measured
AA Math Index95.0Not measuredOnly GLM 4.7 measured
AIME 2025 (Artificial Analysis run)95.0%Not measuredOnly GLM 4.7 measured
OTIS Mock AIME83.3%99.2%Muse Spark 1.3
FrontierMath, tiers 1 to 3Not measured74.4%Only Muse Spark 1.3 measured
FrontierMath, tier 4Not measured46.3%Only Muse Spark 1.3 measured
ProofBench6.0%58.0%Muse Spark 1.3
LMArena Text: math14241497Muse Spark 1.3
LMArena Text: mathematical work14111491Muse Spark 1.3
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GLM 4.7 and Muse Spark 1.3
BenchmarkGLM 4.7Muse Spark 1.3Leader
MMLU Pro82.7%Not measuredOnly GLM 4.7 measured
LegalBench (Vals AI)83.4%Not measuredOnly GLM 4.7 measured
CaseLaw (Vals AI)54.9%Not measuredOnly GLM 4.7 measured
CorpFin (Vals AI)46.4%Not measuredOnly GLM 4.7 measured
TaxEval (Vals AI)68.8%Not measuredOnly GLM 4.7 measured
MedQA (Vals AI)93.7%Not measuredOnly GLM 4.7 measured
MedCode (Vals AI)32.8%Not measuredOnly GLM 4.7 measured
MMLU Pro (Artificial Analysis run)85.6%Not measuredOnly GLM 4.7 measured
SimpleQA Verified32.2%Not measuredOnly GLM 4.7 measured
LMArena Text: science14571507Muse Spark 1.3
LMArena Text: medicine and healthcare14691493Muse Spark 1.3
LMArena Text: legal and government14441499Muse Spark 1.3
LMArena Text: business and finance14291490Muse Spark 1.3
Neither model has a result on the other 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index08
Web design benchmarks for GLM 4.7 and Muse Spark 1.3
BenchmarkGLM 4.7Muse Spark 1.3Leader
Design Arena: websites12251356Muse Spark 1.3
Design Arena: UI components11961349Muse Spark 1.3
Design Arena: game dev11881328Muse Spark 1.3
Design Arena: data viz11951343Muse Spark 1.3
Design Arena: 3D11901358Muse Spark 1.3
Design Arena: all code categories12161352Muse Spark 1.3
Design Arena: SVG11421301Muse Spark 1.3
Design Arena: ASCII art11651278Muse Spark 1.3
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 4.7 and Muse Spark 1.3
BenchmarkGLM 4.7Muse Spark 1.3Leader
LiveBench data analysisNot measured79.6%Only Muse Spark 1.3 measured
MedScribe (Vals AI)68.6%Not measuredOnly GLM 4.7 measured
IFBench67.9%Not measuredOnly GLM 4.7 measured
Long context reasoning (AA-LCR)71.0%83.0%Muse Spark 1.3
DTBenchNot measured96.5%Only Muse Spark 1.3 measured
LMCANot measured53.9%Only Muse Spark 1.3 measured
CL-bench15.9%Not measuredOnly GLM 4.7 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351490Muse Spark 1.3
LMArena Text: instruction following14121479Muse Spark 1.3
LMArena Text: creative writing14011462Muse Spark 1.3
LMArena VisionNot measured1308Only Muse Spark 1.3 measured
LMArena Text: English14531495Muse Spark 1.3
LMArena Text: non-English14171481Muse Spark 1.3
LMArena Text: Chinese14951532Muse Spark 1.3
LMArena Text: German14261513Muse Spark 1.3
LMArena Text: Japanese14391470Muse Spark 1.3
LMArena Text: Korean13991490Muse Spark 1.3
LMArena Text: French14321511Muse Spark 1.3
LMArena Text: Spanish14341496Muse Spark 1.3
LMArena Text: Russian14231491Muse Spark 1.3
LMArena Text: Polish14351490Muse Spark 1.3
LMArena Text: multi-turn14461487Muse Spark 1.3
LMArena Text: longer queries14321488Muse Spark 1.3
LMArena Text: writing and language14111470Muse Spark 1.3
LMArena Text: entertainment and media14001457Muse 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 15 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.7 and Muse Spark 1.3
BenchmarkGLM 4.7Muse 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 4.7$0.0017Fits in one request
Muse Spark 1.3$0.0034Fits in one request

GLM 4.7 costs less

Repository review

50K input + 3K output tokens

GLM 4.7$0.037Fits in one request
Muse Spark 1.3$0.075Fits in one request

GLM 4.7 costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.142Exceeds the context window
Muse Spark 1.3$0.292Fits in one request

GLM 4.7 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 4.7 and Muse Spark 1.3
SpecificationZ.ai GLM 4.7Meta Muse Spark 1.3
MakerZ.aiMeta
WeightsOpen weightsNo public weights listed
ReleasedDecember 22, 2025September 2, 2026
Context window205K1.05M
Input $/Mtok$0.60$1.25
Output $/Mtok$2.20$4.25
Coding index55.157.4
Independent measurements behind it24
Cost per solved task$0.29Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than Muse Spark 1.3 for coding?

Pick Muse Spark 1.3 for coding: it scores 57.4 on the Vibe Coding Index against 55.1 for GLM 4.7. Pick GLM 4.7 for output tokens 1.9x cheaper ($2.20 vs $4.25 per million).

Which is cheaper, GLM 4.7 or Muse Spark 1.3?

GLM 4.7 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 GLM 4.7 only.

Which has the bigger context window, GLM 4.7 or Muse Spark 1.3?

Muse Spark 1.3, with 1.05M tokens against 205K.

How many benchmarks is the GLM 4.7 vs Muse Spark 1.3 comparison based on?

52 benchmarks have a result for both models as of October 22, 2026. GLM 4.7's coding index rests on 2 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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<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/glm-4-7-vs-meta-muse-spark-1-3">LLMBenchmark.ai, GLM 4.7 vs Muse Spark 1.3</a></p>