Noometry

Model comparison

GLM-4.6V vs GLM-5.3-Flash

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 40.9 on the Noometry Index.

Last verified . 12 shared benchmarks.

GLM-4.6V Z.ai (Zhipu)

40.9

Rank #133 Confirmed

GLM-5.3-Flash Z.ai (Zhipu)

49.6

Rank #45 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 37.4.
  • 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 Index40.949.6
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 tracked1236

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Category by category

Coding GLM-5.3-Flash leads

GLM-4.6V: 40.2 (#128), GLM-5.3-Flash: 53.7 (#26)

BenchmarkGLM-4.6VGLM-5.3-Flash
LMArena Coding13901506
DeepSWE—63.4%
FrontierCode—31.8%
LMArena WebDev—1614
SciCode—51.6%
ALE-Bench—303.55

Agentic & Tool Use Not comparable

GLM-4.6V: —, GLM-5.3-Flash: 34.3 (#41)

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

Reasoning GLM-5.3-Flash leads

GLM-4.6V: 26.5 (#117), GLM-5.3-Flash: 31.7 (#69)

BenchmarkGLM-4.6VGLM-5.3-Flash
LMArena Hard Prompts13681490
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—151.9

Math Not comparable

GLM-4.6V: —, GLM-5.3-Flash: 53.1 (#38)

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—1499

Knowledge GLM-5.3-Flash leads

GLM-4.6V: 37.4 (#148), GLM-5.3-Flash: 58.4 (#33)

BenchmarkGLM-4.6VGLM-5.3-Flash
LMArena Expert13721512
GPQA Diamond—90.2%

Multimodal GLM-5.3-Flash leads

GLM-4.6V: 34.2 (#83), GLM-5.3-Flash: 42.6 (#25)

BenchmarkGLM-4.6VGLM-5.3-Flash
LMArena Vision11641304

Multilingual GLM-5.3-Flash leads

GLM-4.6V: 48.8 (#135), GLM-5.3-Flash: 56.2 (#24)

BenchmarkGLM-4.6VGLM-5.3-Flash
LMArena Non-English13591462
LMArena Chinese14261529
LMArena Russian13411472
LMArena French—1495
LMArena German—1457
LMArena Japanese—1420
LMArena Korean—1443
LMArena Spanish—1471

Instruction Following GLM-5.3-Flash leads

GLM-4.6V: 71.6 (#145), GLM-5.3-Flash: 77.8 (#17)

BenchmarkGLM-4.6VGLM-5.3-Flash
LMArena Instruction Following13511480

Long Context GLM-5.3-Flash leads

GLM-4.6V: 41.3 (#138), GLM-5.3-Flash: 45.3 (#41)

BenchmarkGLM-4.6VGLM-5.3-Flash
LMArena Longer Query13581479

Writing & Preference GLM-5.3-Flash leads

GLM-4.6V: 56.8 (#132), GLM-5.3-Flash: 65.4 (#49)

BenchmarkGLM-4.6VGLM-5.3-Flash
LMArena Text13771471
LMArena Creative Writing13471440
LMArena Multi-Turn13591471

Frequently asked questions

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

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 40.9 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: 53.7 versus 40.2 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 36.

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