Noometry

Model comparison

GLM-5.3-Flash vs Muse Spark

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 50.6 on the Noometry Index.

Last verified . 23 shared benchmarks.

GLM-5.3-Flash Z.ai (Zhipu)

51.8

Rank #41 Confirmed

Muse Spark Meta

50.6

Rank #46 Confirmed

Summary

  • They share 23 benchmarks with published results for both. GLM-5.3-Flash scores higher in 5 categories and Muse Spark in 4 categories; 6 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 35.9.
  • GLM-5.3-Flash has downloadable open weights; the other is API-only.

Side by side

GLM-5.3-FlashMuse Spark
ProviderZ.ai (Zhipu)Meta
Noometry Index51.850.6
Released2026-08-202026-04-08
WeightsOpenProprietary
Context window1M—
Max output131K—
Input $ / M tokens$0.15—
Output $ / M tokens$0.50—
Results tracked4027

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Category by category

Coding GLM-5.3-Flash leads

GLM-5.3-Flash: 53.1 (#31), Muse Spark: 46.2 (#69)

BenchmarkGLM-5.3-FlashMuse Spark
SciCode51.6%51.5%
LMArena Coding15081481
DeepSWE63.4%—
FrontierCode31.8%—
CursorBench36.8%—
LMArena WebDev1609—
FrontierSWE18.1%—
ALE-Bench303.55—

Agentic & Tool Use Not comparable

GLM-5.3-Flash: 34.2 (#47), Muse Spark: —

BenchmarkGLM-5.3-FlashMuse Spark
APEX-Agents52.8%—
GDP.pdf14%—

Reasoning GLM-5.3-Flash leads

GLM-5.3-Flash: 48.0 (#42), Muse Spark: 35.9 (#67)

BenchmarkGLM-5.3-FlashMuse Spark
CritPt15.4%11.3%
LMArena Hard Prompts14911474
Epoch Capabilities Index151.88152.04
ARC-AGI-265.8%—
ARC-AGI-191%—
Chess Puzzles14%—
Mystery Game Puzzles8%—
Surface Evolver Bench52.5%—
Bench to the Future 30.15—

Math GLM-5.3-Flash leads

GLM-5.3-Flash: 53.3 (#47), Muse Spark: 47.8 (#66)

BenchmarkGLM-5.3-FlashMuse Spark
OTIS Mock AIME 2024-202593.9%88.9%
ProofBench21%17%
LMArena Math15001455
FrontierMath (Tiers 1-3)55.8%—
FrontierMath Tier 417.1%—
FrontierMath (Feb 2025 set)—39%
FrontierMath Tier 4 (v1)—14.6%

Knowledge Muse Spark leads

GLM-5.3-Flash: 58.4 (#36), Muse Spark: 65.7 (#13)

BenchmarkGLM-5.3-FlashMuse Spark
GPQA Diamond90.2%89.8%
LMArena Expert15131457
Humanity's Last Exam—40.6%

Multimodal Too close to call

GLM-5.3-Flash: 42.8 (#27), Muse Spark: 43.4 (#24)

BenchmarkGLM-5.3-FlashMuse Spark
LMArena Vision12961306
LMArena Document—1444

Multilingual Too close to call

GLM-5.3-Flash: 56.0 (#25), Muse Spark: 56.1 (#24)

BenchmarkGLM-5.3-FlashMuse Spark
LMArena Non-English14621464
LMArena Chinese15271509
LMArena French14961497
LMArena German14701497
LMArena Korean14461459
LMArena Russian14691466
LMArena Spanish14711472
LMArena Japanese1429—

Instruction Following GLM-5.3-Flash leads

GLM-5.3-Flash: 77.5 (#20), Muse Spark: 75.9 (#51)

BenchmarkGLM-5.3-FlashMuse Spark
LMArena Instruction Following14781442

Long Context GLM-5.3-Flash leads

GLM-5.3-Flash: 45.4 (#39), Muse Spark: 44.4 (#69)

BenchmarkGLM-5.3-FlashMuse Spark
LMArena Longer Query14821451

Writing & Preference Too close to call

GLM-5.3-Flash: 65.3 (#50), Muse Spark: 66.0 (#39)

BenchmarkGLM-5.3-FlashMuse Spark
LMArena Text14711474
LMArena Creative Writing14421459
LMArena Multi-Turn14671477

Frequently asked questions

Is GLM-5.3-Flash better than Muse Spark?

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 50.6 on the Noometry Index.

Is GLM-5.3-Flash or Muse Spark better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 46.2 in the Noometry coding category.

How many benchmarks do GLM-5.3-Flash and Muse Spark share?

23 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Muse Spark has 27.

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