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GLM 4.5 vs Muse Spark Open

GLM 4.5 costs 1.9x less per output token than Muse Spark Open ($2.20 vs $4.25 per million). Updated October 21, 2026; 6 benchmarks have a result for both.

GLM 4.5Z.ai
Coding
No data
Overall
No data
Output $/M
$2.20
0
Muse Spark OpenMeta
Coding
47.4
Overall
44.9
Output $/M
$4.25
6
vs boards won of 6 shared

GLM 4.5 or Muse Spark Open: which to pick

Only Muse Spark Open has a coding score, 47.4 on the Vibe Coding Index; GLM 4.5 has none, so the two cannot be ranked on coding.

Pick GLM 4.5 for output tokens 1.9x cheaper ($2.20 vs $4.25 per million).

Shared results
6
GLM 4.5 only
55
Muse Spark Open only
22
Like-for-like categories
4 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 4.5 not benchmarked, Muse Spark Open 47.4 from 4 benchmarks.
Widest category
Coding, 41.5 points apart on average over 1 shared benchmark.
Widest single gap
ALE-Bench: GLM 4.5 344.8 against Muse Spark Open 1590.1, 41.5 points apart on a 0 to 100 scale.
Output price
GLM 4.5 $2.20 against Muse Spark Open $4.25 per million output tokens, 1.9x apart.
Context window
GLM 4.5 takes 131K tokens, Muse Spark Open 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 OpenTies
Coding01
Reasoning02
Web design02
General01
All shared boards06
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
Coding benchmarks for GLM 4.5 and Muse Spark Open
BenchmarkGLM 4.5Muse Spark OpenLeader
SWE-bench Verified64.2%Not measuredOnly GLM 4.5 measured
LiveBench codingNot measured73.6%Only Muse Spark Open measured
LMArena WebDevNot measured1528Only Muse Spark Open measured
LiveCodeBench (Vals AI)67.5%Not measuredOnly GLM 4.5 measured
Terminal-Bench 4 (Vals AI)Not measured6.6%Only Muse Spark Open measured
Vibe Code Bench (Vals AI)Not measured76.2%Only Muse Spark Open measured
Code migration (Vals AI)Not measured21.2%Only Muse Spark Open measured
LiveCodeBench (Artificial Analysis run)73.8%Not measuredOnly GLM 4.5 measured
SciCodeNot measured58.4%Only Muse Spark Open measured
Terminal-Bench Hard (Artificial Analysis run)22.0%Not measuredOnly GLM 4.5 measured
WeirdML40.6%Not measuredOnly GLM 4.5 measured
ALE-Bench344.81590.1Muse Spark Open
LMArena Text: coding1434Not measuredOnly GLM 4.5 measured
LMArena Text: software and IT1440Not measuredOnly GLM 4.5 measured
LMArena WebDev: ReactNot measured1529Only Muse Spark Open measured
LMArena WebDev: HTMLNot measured1495Only Muse Spark Open measured
Neither model has a result on the other 23 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for GLM 4.5 and Muse Spark Open
BenchmarkGLM 4.5Muse Spark OpenLeader
AA Intelligence Index12.8Not measuredOnly GLM 4.5 measured
Epoch Capabilities IndexNot measured155.1Only Muse Spark Open measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
Agentic work benchmarks for GLM 4.5 and Muse Spark Open
BenchmarkGLM 4.5Muse Spark OpenLeader
LiveBench researchNot measured80.3%Only Muse Spark Open measured
LiveBench browserNot measured36.0%Only Muse Spark Open measured
LiveBench long agenticNot measured18.9%Only Muse Spark Open measured
Finance Agent (Vals AI)Not measured61.3%Only Muse Spark Open measured
Tax Agent Bench (Vals AI)Not measured26.3%Only Muse Spark Open measured
Legal Research Bench (Vals AI)Not measured38.5%Only Muse Spark Open measured
Harvey's Legal Agent Benchmark (Vals AI)Not measured25.0%Only Muse Spark Open measured
Excel Modeling Benchmark (Vals AI)Not measured53.7%Only Muse Spark Open measured
τ²-Bench Telecom43.0%Not measuredOnly GLM 4.5 measured
APEX-AgentsNot measured33.4%Only Muse Spark Open measured
Neither model has a result on the other 31 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
Reasoning benchmarks for GLM 4.5 and Muse Spark Open
BenchmarkGLM 4.5Muse Spark OpenLeader
GPQA Diamond72.2%Not measuredOnly GLM 4.5 measured
LiveBench reasoningNot measured73.7%Only Muse Spark Open measured
GPQA Diamond (Artificial Analysis run)78.2%89.0%Muse Spark Open
Humanity's Last Exam (Artificial Analysis run)13.0%43.9%Muse Spark Open
LMArena Text: hard prompts1429Not measuredOnly GLM 4.5 measured
LMArena Text: expert1433Not measuredOnly GLM 4.5 measured
LMArena Text: hard prompts in English1429Not measuredOnly GLM 4.5 measured
Kagi LLM Benchmark57.9%Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 14 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Math benchmarks for GLM 4.5 and Muse Spark Open
BenchmarkGLM 4.5Muse Spark OpenLeader
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
LMArena Text: math1427Not measuredOnly GLM 4.5 measured
LMArena Text: mathematical work1430Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 6 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
Knowledge benchmarks for GLM 4.5 and Muse Spark Open
BenchmarkGLM 4.5Muse Spark OpenLeader
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: science1444Not measuredOnly GLM 4.5 measured
LMArena Text: medicine and healthcare1455Not measuredOnly GLM 4.5 measured
LMArena Text: legal and government1438Not measuredOnly GLM 4.5 measured
LMArena Text: business and finance1420Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index02
Web design benchmarks for GLM 4.5 and Muse Spark Open
BenchmarkGLM 4.5Muse Spark OpenLeader
Design Arena: websites11701317Muse Spark Open
Design Arena: UI components1144Not measuredOnly GLM 4.5 measured
Design Arena: game dev11451294Muse Spark Open
Design Arena: data viz1159Not measuredOnly GLM 4.5 measured
Design Arena: 3D1170Not measuredOnly GLM 4.5 measured
Design Arena: all code categories1165Not measuredOnly GLM 4.5 measured
Design Arena: SVG1096Not measuredOnly GLM 4.5 measured
LiveBench designNot measured60.3%Only Muse Spark Open measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
General benchmarks for GLM 4.5 and Muse Spark Open
BenchmarkGLM 4.5Muse Spark OpenLeader
Vals IndexNot measured48.6%Only Muse Spark Open measured
LiveBench data analysisNot measured60.2%Only Muse Spark Open measured
IFBench44.1%Not measuredOnly GLM 4.5 measured
Long context reasoning (AA-LCR)52.7%78.3%Muse Spark Open
Creative writing (Lech Mazur)73.4%Not measuredOnly GLM 4.5 measured
LMArena Text1430Not measuredOnly GLM 4.5 measured
LMArena Text: instruction following1404Not measuredOnly GLM 4.5 measured
LMArena Text: creative writing1395Not measuredOnly GLM 4.5 measured
LMArena Text: English1437Not measuredOnly GLM 4.5 measured
LMArena Text: non-English1417Not measuredOnly GLM 4.5 measured
LMArena Text: Chinese1465Not measuredOnly GLM 4.5 measured
LMArena Text: German1407Not measuredOnly GLM 4.5 measured
LMArena Text: Japanese1415Not measuredOnly GLM 4.5 measured
LMArena Text: Korean1380Not measuredOnly GLM 4.5 measured
LMArena Text: French1418Not measuredOnly GLM 4.5 measured
LMArena Text: Spanish1454Not measuredOnly GLM 4.5 measured
LMArena Text: Russian1413Not measuredOnly GLM 4.5 measured
LMArena Text: Polish1422Not measuredOnly GLM 4.5 measured
LMArena Text: multi-turn1415Not measuredOnly GLM 4.5 measured
LMArena Text: longer queries1412Not measuredOnly GLM 4.5 measured
LMArena Text: writing and language1403Not measuredOnly GLM 4.5 measured
LMArena Text: entertainment and media1404Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 24 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 Open
BenchmarkGLM 4.5Muse Spark OpenLeader
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 Open$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 Open$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 Open$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 Open
SpecificationZ.ai GLM 4.5Meta Muse Spark Open
MakerZ.aiMeta
WeightsOpen weightsOpen weights
ReleasedJuly 25, 2025October 15, 2026
Context window131K1.05M
Input $/Mtok$0.60$1.25
Output $/Mtok$2.20$4.25
Coding indexNot benchmarked47.4
Independent measurements behind it04
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5 better than Muse Spark Open for coding?

Only Muse Spark Open has a coding score, 47.4 on the Vibe Coding Index; GLM 4.5 has none, so the two cannot be ranked on coding. Pick GLM 4.5 for output tokens 1.9x cheaper ($2.20 vs $4.25 per million).

Which is cheaper, GLM 4.5 or Muse Spark Open?

GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; Muse Spark Open 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 Open?

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

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

6 benchmarks have a result for both models as of October 21, 2026. GLM 4.5's coding index rests on 0 independent benchmarks and Muse Spark Open's on 4. Where only one model has a result, the page says so instead of naming a winner.

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