Noometry

Model comparison

GLM-4.6V vs GLM-5.3-Flash

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 41.3 on the Noometry Index.

Last verified . 12 shared benchmarks.

GLM-4.6V Z.ai (Zhipu)

41.3

Rank #137 Confirmed

GLM-5.3-Flash Z.ai (Zhipu)

51.8

Rank #41 Confirmed

Summary

  • They share 12 benchmarks with published results for both. GLM-4.6V scores higher in 0 categories and GLM-5.3-Flash in 8 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-5.3-Flash leads 58.4 to 38.0.
  • GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.30 / $0.90 for GLM-4.6V.
  • GLM-5.3-Flash accepts more context: 1M tokens versus 128K.

Side by side

GLM-4.6VGLM-5.3-Flash
ProviderZ.ai (Zhipu)Z.ai (Zhipu)
Noometry Index41.351.8
Released2025-12-082026-08-20
WeightsOpenOpen
Context window128K1M
Max output33K131K
Input $ / M tokens$0.30$0.15
Output $ / M tokens$0.90$0.50
Results tracked1240

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Category by category

Coding GLM-5.3-Flash leads

GLM-4.6V: 40.9 (#128), GLM-5.3-Flash: 53.1 (#31)

BenchmarkGLM-4.6VGLM-5.3-Flash
LMArena Coding13901508
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-4.6V: —, GLM-5.3-Flash: 34.2 (#47)

BenchmarkGLM-4.6VGLM-5.3-Flash
APEX-Agents—52.8%
GDP.pdf—14%

Reasoning GLM-5.3-Flash leads

GLM-4.6V: 27.6 (#115), GLM-5.3-Flash: 48.0 (#42)

BenchmarkGLM-4.6VGLM-5.3-Flash
LMArena Hard Prompts13681491
ARC-AGI-2—65.8%
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 Not comparable

GLM-4.6V: —, GLM-5.3-Flash: 53.3 (#47)

BenchmarkGLM-4.6VGLM-5.3-Flash
FrontierMath (Tiers 1-3)—55.8%
FrontierMath Tier 4—17.1%
OTIS Mock AIME 2024-2025—93.9%
ProofBench—21%
LMArena Math—1500

Knowledge GLM-5.3-Flash leads

GLM-4.6V: 38.0 (#149), GLM-5.3-Flash: 58.4 (#36)

BenchmarkGLM-4.6VGLM-5.3-Flash
LMArena Expert13711513
GPQA Diamond—90.2%

Multimodal GLM-5.3-Flash leads

GLM-4.6V: 34.8 (#90), GLM-5.3-Flash: 42.8 (#27)

BenchmarkGLM-4.6VGLM-5.3-Flash
LMArena Vision11641296

Multilingual GLM-5.3-Flash leads

GLM-4.6V: 48.6 (#141), GLM-5.3-Flash: 56.0 (#25)

BenchmarkGLM-4.6VGLM-5.3-Flash
LMArena Non-English13591462
LMArena Chinese14251527
LMArena Russian13401469
LMArena French—1496
LMArena German—1470
LMArena Japanese—1429
LMArena Korean—1446
LMArena Spanish—1471

Instruction Following GLM-5.3-Flash leads

GLM-4.6V: 71.4 (#151), GLM-5.3-Flash: 77.5 (#20)

BenchmarkGLM-4.6VGLM-5.3-Flash
LMArena Instruction Following13521478

Long Context GLM-5.3-Flash leads

GLM-4.6V: 41.3 (#143), GLM-5.3-Flash: 45.4 (#39)

BenchmarkGLM-4.6VGLM-5.3-Flash
LMArena Longer Query13581482

Writing & Preference GLM-5.3-Flash leads

GLM-4.6V: 56.6 (#137), GLM-5.3-Flash: 65.3 (#50)

BenchmarkGLM-4.6VGLM-5.3-Flash
LMArena Text13771471
LMArena Creative Writing13471442
LMArena Multi-Turn13601467

Frequently asked questions

Is GLM-4.6V better than GLM-5.3-Flash?

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 41.3 on the Noometry Index.

Which is cheaper, GLM-4.6V 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-4.6V lists at $0.30 and $0.90.

Is GLM-4.6V or GLM-5.3-Flash better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 40.9 in the Noometry coding category.

Which has the bigger context window?

GLM-5.3-Flash does, with 1M tokens against 128K.

How many benchmarks do GLM-4.6V and GLM-5.3-Flash share?

12 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and GLM-5.3-Flash has 40.

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