Noometry

Model comparison

GLM-4.5V vs GLM-5.3-Flash

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 39.8 on the Noometry Index.

Last verified . 14 shared benchmarks.

GLM-4.5V Z.ai (Zhipu)

39.8

Rank #158 Confirmed

GLM-5.3-Flash Z.ai (Zhipu)

51.8

Rank #41 Confirmed

Summary

  • They share 14 benchmarks with published results for both. GLM-4.5V scores higher in 0 categories and GLM-5.3-Flash in 9 categories; 9 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-5.3-Flash leads 58.4 to 37.5.
  • GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
  • GLM-5.3-Flash accepts more context: 1M tokens versus 64K.

Side by side

GLM-4.5VGLM-5.3-Flash
ProviderZ.ai (Zhipu)Z.ai (Zhipu)
Noometry Index39.851.8
Released2025-08-112026-08-20
WeightsOpenOpen
Context window64K1M
Max output16K131K
Input $ / M tokens$0.60$0.15
Output $ / M tokens$1.80$0.50
Results tracked1540

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Category by category

Coding GLM-5.3-Flash leads

GLM-4.5V: 39.5 (#155), GLM-5.3-Flash: 53.1 (#31)

BenchmarkGLM-4.5VGLM-5.3-Flash
LMArena Coding13471508
DeepSWE—63.4%
FrontierCode—31.8%
CursorBench—36.8%
LMArena WebDev—1609
FrontierSWE—18.1%
SciCode—51.6%
ALE-Bench—303.55

Agentic & Tool Use Not comparable

GLM-4.5V: —, GLM-5.3-Flash: 34.2 (#47)

BenchmarkGLM-4.5VGLM-5.3-Flash
APEX-Agents—52.8%
GDP.pdf—14%

Reasoning GLM-5.3-Flash leads

GLM-4.5V: 27.4 (#119), GLM-5.3-Flash: 48.0 (#42)

BenchmarkGLM-4.5VGLM-5.3-Flash
LMArena Hard Prompts13341491
ARC-AGI-2—65.8%
Kagi LLM Benchmark59.8%—
ARC-AGI-1—91%
CritPt—15.4%
Chess Puzzles—14%
Mystery Game Puzzles—8%
Surface Evolver Bench—52.5%
Bench to the Future 3—0.15
Epoch Capabilities Index—151.88

Math GLM-5.3-Flash leads

GLM-4.5V: 37.4 (#159), GLM-5.3-Flash: 53.3 (#47)

BenchmarkGLM-4.5VGLM-5.3-Flash
LMArena Math13541500
FrontierMath (Tiers 1-3)—55.8%
FrontierMath Tier 4—17.1%
OTIS Mock AIME 2024-2025—93.9%
ProofBench—21%

Knowledge GLM-5.3-Flash leads

GLM-4.5V: 37.5 (#156), GLM-5.3-Flash: 58.4 (#36)

BenchmarkGLM-4.5VGLM-5.3-Flash
LMArena Expert13531513
GPQA Diamond—90.2%

Multimodal GLM-5.3-Flash leads

GLM-4.5V: 34.3 (#92), GLM-5.3-Flash: 42.8 (#27)

BenchmarkGLM-4.5VGLM-5.3-Flash
LMArena Vision11541296

Multilingual GLM-5.3-Flash leads

GLM-4.5V: 44.6 (#177), GLM-5.3-Flash: 56.0 (#25)

BenchmarkGLM-4.5VGLM-5.3-Flash
LMArena Non-English13031462
LMArena Chinese13371527
LMArena Russian12981469
LMArena Spanish13361471
LMArena French—1496
LMArena German—1470
LMArena Japanese—1429
LMArena Korean—1446

Instruction Following GLM-5.3-Flash leads

GLM-4.5V: 69.2 (#175), GLM-5.3-Flash: 77.5 (#20)

BenchmarkGLM-4.5VGLM-5.3-Flash
LMArena Instruction Following13111478

Long Context GLM-5.3-Flash leads

GLM-4.5V: 39.6 (#171), GLM-5.3-Flash: 45.4 (#39)

BenchmarkGLM-4.5VGLM-5.3-Flash
LMArena Longer Query13041482

Writing & Preference GLM-5.3-Flash leads

GLM-4.5V: 52.5 (#170), GLM-5.3-Flash: 65.3 (#50)

BenchmarkGLM-4.5VGLM-5.3-Flash
LMArena Text13331471
LMArena Creative Writing12951442
LMArena Multi-Turn13321467

Frequently asked questions

Is GLM-4.5V better than GLM-5.3-Flash?

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 39.8 on the Noometry Index.

Which is cheaper, GLM-4.5V or GLM-5.3-Flash?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.

Is GLM-4.5V or GLM-5.3-Flash better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 39.5 in the Noometry coding category.

Which has the bigger context window?

GLM-5.3-Flash does, with 1M tokens against 64K.

How many benchmarks do GLM-4.5V and GLM-5.3-Flash share?

14 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and GLM-5.3-Flash has 40.

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