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GLM 5.3 vs Muse Wonder

Muse Wonder leads GLM 5.3 on coding, 71.9 to 70.6 on the Vibe Coding Index, and GLM 5.3 costs 4.5x less per output token ($4.40 vs $20.00 per million). Updated October 22, 2026; 55 benchmarks have a result for both.

Replace GLM 5.3 Replace Muse Wonder
GLM 5.3Z.ai
Coding
70.6
Overall
67.9
Output $/M
$4.40
4
Muse WonderMeta
Coding
71.9
Overall
No data
Output $/M
$20.00
51
vs boards won of 55 shared

GLM 5.3 or Muse Wonder: which to pick

GLM 5.3 and Muse Wonder are level for coding, 70.6 against 71.9 on the Vibe Coding Index, so price and context decide.

Pick GLM 5.3 for output tokens 4.5x cheaper ($4.40 vs $20.00 per million) and work measured by IOI (Vals AI) or Tax Agent Bench (Vals AI), where it leads. Pick Muse Wonder for work measured by LMArena WebDev or Terminal-Bench 4 (Vals AI), where it leads.

Model AGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved taskModel BMuse WonderMeta71.9/1003 independent benchmarksNo cost per solved task
Shared results
55
GLM 5.3 only
45
Muse Wonder only
5
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 5.3 70.6 from 6 benchmarks, Muse Wonder 71.9 from 3 benchmarks.
Widest category
Math, 13.1 points apart on average over 3 shared benchmarks.
Widest single gap
ProofBench: GLM 5.3 49.0% against Muse Wonder 84.0%, 35.0 points apart on a 0 to 100 scale.
Output price
GLM 5.3 $4.40 against Muse Wonder $20.00 per million output tokens, 4.5x 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 WonderTies
Coding111
Intelligence01
Agentic work16
Reasoning14
Math12
Knowledge04
Web design06
General017
All shared boards451
CodingLike-for-likeSix of these boards feed the Vibe Coding Index111
Coding benchmarks for GLM 5.3 and Muse Wonder
BenchmarkGLM 5.3Muse WonderLeader
LiveBench coding82.8%Not measuredOnly GLM 5.3 measured
LMArena WebDev16211652Muse Wonder
AA Coding Index74.8Not measuredOnly GLM 5.3 measured
LiveCodeBench (Vals AI)80.5%Not measuredOnly GLM 5.3 measured
SWE-bench Verified (Vals AI)95.4%Not measuredOnly GLM 5.3 measured
Terminal-Bench 4 (Vals AI)38.9%49.5%Muse Wonder
Vibe Code Bench (Vals AI)78.1%83.1%Muse Wonder
Code migration (Vals AI)44.2%62.5%Muse Wonder
ProgramBench (Vals AI)1.5%Not measuredOnly GLM 5.3 measured
IOI (Vals AI)68.4%66.9%GLM 5.3
Terminal-Bench 2.1 (Vals AI)71.5%Not measuredOnly GLM 5.3 measured
SWE-bench Multimodal (Vals AI)Not measured45.6%Only Muse Wonder measured
Vibe Code Bench 1-100 (Vals AI)20.0%26.6%Muse Wonder
SkillsBench (Vals AI)47.5%Not measuredOnly GLM 5.3 measured
CyberBench (Vals AI)72.1%80.4%Muse Wonder
SciCode59.0%60.3%Muse Wonder
FrontierCode40.1%Not measuredOnly GLM 5.3 measured
FrontierSWE30.2%Not measuredOnly GLM 5.3 measured
WeirdML75.4%Not measuredOnly GLM 5.3 measured
ALE-Bench1317.4Not measuredOnly GLM 5.3 measured
LMArena Text: coding14971522Muse Wonder
LMArena Text: software and IT14931515Muse Wonder
LMArena WebDev: React16411641Muse Wonder
LMArena WebDev: HTML15951694Muse Wonder
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 5.3 and Muse Wonder
BenchmarkGLM 5.3Muse WonderLeader
AA Intelligence Index44.8Not measuredOnly GLM 5.3 measured
Epoch Capabilities Index156.0161.8Muse Wonder
Agentic workLike-for-likePublic boards, shown as published, kept out of the index16
Agentic work benchmarks for GLM 5.3 and Muse Wonder
BenchmarkGLM 5.3Muse WonderLeader
AA Agentic Index53.1Not measuredOnly GLM 5.3 measured
Design Arena: agents building mobile apps1194Not measuredOnly GLM 5.3 measured
Design Arena: HTML slides1189Not measuredOnly GLM 5.3 measured
Design Arena: PowerPoint slides1230Not measuredOnly GLM 5.3 measured
Design Arena: spreadsheetsNot measured1243Only Muse Wonder measured
LiveBench research73.2%Not measuredOnly GLM 5.3 measured
LiveBench browser0.0%Not measuredOnly GLM 5.3 measured
LiveBench long agentic35.9%Not measuredOnly GLM 5.3 measured
SREBench (Vals AI)9.2%Not measuredOnly GLM 5.3 measured
Finance Agent (Vals AI)55.8%56.6%Muse Wonder
Tax Agent Bench (Vals AI)39.7%36.3%GLM 5.3
Legal Research Bench (Vals AI)49.0%50.5%Muse Wonder
Harvey's Legal Agent Benchmark (Vals AI)14.6%23.8%Muse Wonder
Excel Modeling Benchmark (Vals AI)56.3%67.1%Muse Wonder
BioMysteryBench (Vals AI)Not measured67.0%Only Muse Wonder measured
Terminal-Bench-Science (Vals AI)5.7%22.9%Muse Wonder
Vals Science IndexNot measured39.1%Only Muse Wonder measured
CUA-bench (Vals AI)Not measured7.2%Only Muse Wonder measured
APEX-Agents56.6%Not measuredOnly GLM 5.3 measured
Vending-Bench 2$8,164$9,432Muse Wonder
Neither model has a result on the other 21 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index14
Reasoning benchmarks for GLM 5.3 and Muse Wonder
BenchmarkGLM 5.3Muse WonderLeader
GPQA Diamond88.1%Not measuredOnly GLM 5.3 measured
LiveBench reasoning85.8%Not measuredOnly GLM 5.3 measured
MysteryMechanism (Vals AI)23.0%27.5%Muse Wonder
GPQA Diamond (Artificial Analysis run)91.7%Not measuredOnly GLM 5.3 measured
Humanity's Last Exam (Artificial Analysis run)42.3%50.1%Muse Wonder
GPQA Diamond (Epoch AI run)90.9%Not measuredOnly GLM 5.3 measured
Chess puzzles37.0%Not measuredOnly GLM 5.3 measured
Mystery game puzzles33.0%Not measuredOnly GLM 5.3 measured
LMArena Text: hard prompts14901506Muse Wonder
LMArena Text: expert15141513GLM 5.3
LMArena Text: hard prompts in English14961506Muse Wonder
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index12
Math benchmarks for GLM 5.3 and Muse Wonder
BenchmarkGLM 5.3Muse WonderLeader
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%84.0%Muse Wonder
LMArena Text: math14951486GLM 5.3
LMArena Text: mathematical work14971514Muse Wonder
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GLM 5.3 and Muse Wonder
BenchmarkGLM 5.3Muse WonderLeader
MMLU Pro86.8%Not measuredOnly GLM 5.3 measured
LegalBench (Vals AI)84.8%Not measuredOnly GLM 5.3 measured
TaxEval (Vals AI)72.4%Not measuredOnly GLM 5.3 measured
MedCode (Vals AI)42.9%Not measuredOnly GLM 5.3 measured
SimpleQA Verified41.0%Not measuredOnly GLM 5.3 measured
LMArena Text: science14981506Muse Wonder
LMArena Text: medicine and healthcare14811512Muse Wonder
LMArena Text: legal and government14731505Muse Wonder
LMArena Text: business and finance14591483Muse Wonder
Neither model has a result on the other 9 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index06
Web design benchmarks for GLM 5.3 and Muse Wonder
BenchmarkGLM 5.3Muse WonderLeader
Design Arena: websites12951362Muse Wonder
Design Arena: UI components13141379Muse Wonder
Design Arena: game dev13111376Muse Wonder
Design Arena: data viz12601317Muse Wonder
Design Arena: 3D13321427Muse Wonder
Design Arena: all code categories13041371Muse Wonder
Design Arena: SVG1294Not measuredOnly GLM 5.3 measured
Design Arena: ASCII art1208Not measuredOnly GLM 5.3 measured
LiveBench design77.3%Not measuredOnly GLM 5.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
General benchmarks for GLM 5.3 and Muse Wonder
BenchmarkGLM 5.3Muse WonderLeader
Vals Index59.7%64.9%Muse Wonder
LiveBench data analysis70.2%Not measuredOnly GLM 5.3 measured
Public Benefits Bench (Vals AI)68.5%70.2%Muse Wonder
MedScribe (Vals AI)88.8%Not measuredOnly GLM 5.3 measured
Long context reasoning (AA-LCR)79.7%80.7%Muse Wonder
DTBench87.7%95.2%Muse Wonder
LMCA55.5%56.8%Muse Wonder
LMArena Text14741502Muse Wonder
LMArena Text: instruction following14791491Muse Wonder
LMArena Text: creative writing14581491Muse Wonder
LMArena Text: English14821507Muse Wonder
LMArena Text: non-English14591492Muse Wonder
LMArena Text: Chinese15321537Muse Wonder
LMArena Text: German1488Not measuredOnly GLM 5.3 measured
LMArena Text: Japanese1460Not measuredOnly GLM 5.3 measured
LMArena Text: Korean1460Not measuredOnly GLM 5.3 measured
LMArena Text: French15031523Muse Wonder
LMArena Text: Spanish1462Not measuredOnly GLM 5.3 measured
LMArena Text: Russian14691522Muse Wonder
LMArena Text: Polish1466Not measuredOnly GLM 5.3 measured
LMArena Text: multi-turn14721510Muse Wonder
LMArena Text: longer queries14831493Muse Wonder
LMArena Text: writing and language14691496Muse Wonder
LMArena Text: entertainment and media14471482Muse Wonder
Neither model has a result on the other 22 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 Wonder
BenchmarkGLM 5.3Muse WonderLeader
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$0.0036Fits in one request
Muse Wonder$0.014Fits in one request

GLM 5.3 costs less

Repository review

50K input + 3K output tokens

GLM 5.3$0.083Fits in one request
Muse Wonder$0.260Fits in one request

GLM 5.3 costs less

Agent loop

200K input + 10K output tokens

GLM 5.3$0.324Fits in one request
Muse Wonder$1.00Fits 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 Wonder
SpecificationZ.ai GLM 5.3Meta Muse Wonder
MakerZ.aiMeta
WeightsOpen weightsNo public weights listed
ReleasedAugust 18, 2026October 12, 2026
Context window1.05M1.05M
Input $/Mtok$1.40$4.00
Output $/Mtok$4.40$20.00
Coding index70.671.9
Independent measurements behind it63
Cost per solved task$0.36Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.3 better than Muse Wonder for coding?

GLM 5.3 and Muse Wonder are level for coding, 70.6 against 71.9 on the Vibe Coding Index, so price and context decide. Pick GLM 5.3 for output tokens 4.5x cheaper ($4.40 vs $20.00 per million) and work measured by IOI (Vals AI) or Tax Agent Bench (Vals AI), where it leads. Pick Muse Wonder for work measured by LMArena WebDev or Terminal-Bench 4 (Vals AI), where it leads.

Which is cheaper, GLM 5.3 or Muse Wonder?

GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; Muse Wonder costs $4.00 and $20.00. Cost per solved task is known for GLM 5.3 only.

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

Neither: both take 1.05M tokens.

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

55 benchmarks have a result for both models as of October 22, 2026. GLM 5.3's coding index rests on 6 independent benchmarks and Muse Wonder's on 3. Where only one model has a result, the page says so instead of naming a winner.

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<iframe src="https://llmbenchmark.ai/embed/compare/glm-5-3-vs-meta-muse-wonder" title="GLM 5.3 vs Muse Wonder, LLMBenchmark.ai" width="100%" height="360" style="border:0;max-width:100%" loading="lazy"></iframe>
<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-wonder">LLMBenchmark.ai, GLM 5.3 vs Muse Wonder</a></p>