Noometry

Model comparison

GLM-4.5V vs GPT-6 Luna

GPT-6 Luna is the stronger model overall, scoring 53.3 to 39.8 on the Noometry Index.

Last verified . 14 shared benchmarks.

GLM-4.5V Z.ai (Zhipu)

39.8

Rank #158 Confirmed

GPT-6 Luna OpenAI

53.3

Rank #36 Confirmed

Summary

  • They share 14 benchmarks with published results for both. GLM-4.5V scores higher in 0 categories and GPT-6 Luna in 9 categories; 9 gaps are clear of the uncertainty.
  • The widest gap is in math, where GPT-6 Luna leads 76.1 to 37.4.
  • GPT-6 Luna is cheaper at $0.10 / $0.50 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
  • GPT-6 Luna accepts more context: 1.05M tokens versus 64K.
  • GLM-4.5V has downloadable open weights; the other is API-only.

Side by side

GLM-4.5VGPT-6 Luna
ProviderZ.ai (Zhipu)OpenAI
Noometry Index39.853.3
Released2025-08-112026-09-22
WeightsOpenProprietary
Context window64K1.05M
Max output16K128K
Input $ / M tokens$0.60$0.10
Output $ / M tokens$1.80$0.50
Results tracked1542

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Category by category

Coding GPT-6 Luna leads

GLM-4.5V: 39.5 (#155), GPT-6 Luna: 55.5 (#25)

BenchmarkGLM-4.5VGPT-6 Luna
LMArena Coding13471439
DeepSWE—66.6%
FrontierCode—42.4%
LMArena WebDev—1581
SciCode—54.6%
ALE-Bench—1,577

Agentic & Tool Use Not comparable

GLM-4.5V: —, GPT-6 Luna: 33.3 (#54)

BenchmarkGLM-4.5VGPT-6 Luna
APEX-Agents—44.3%
GDP.pdf—23%

Reasoning GPT-6 Luna leads

GLM-4.5V: 27.4 (#119), GPT-6 Luna: 48.2 (#41)

BenchmarkGLM-4.5VGPT-6 Luna
LMArena Hard Prompts13341411
ARC-AGI-2—59.3%
Kagi LLM Benchmark59.8%—
NYT Connections (extended)—68.7%
ARC-AGI-1—86.7%
CritPt—19.4%
Chess Puzzles—31%
Mystery Game Puzzles—7%
DTBench—90.1%
LMCA—44.5%
Epoch Capabilities Index—156.28

Math GPT-6 Luna leads

GLM-4.5V: 37.4 (#159), GPT-6 Luna: 76.1 (#15)

BenchmarkGLM-4.5VGPT-6 Luna
LMArena Math13541416
FrontierMath (Tiers 1-3)—78.9%
FrontierMath Tier 4—56.1%
OTIS Mock AIME 2024-2025—98.9%
ProofBench—64%

Knowledge GPT-6 Luna leads

GLM-4.5V: 37.5 (#156), GPT-6 Luna: 57.0 (#41)

BenchmarkGLM-4.5VGPT-6 Luna
LMArena Expert13531444
GPQA Diamond—90.5%
SimpleQA Verified—41.4%

Multimodal GPT-6 Luna leads

GLM-4.5V: 34.3 (#92), GPT-6 Luna: 42.4 (#30)

BenchmarkGLM-4.5VGPT-6 Luna
LMArena Vision11541217
Blueprint-Bench 2—31.2%
Furniture Assembly—44.2%

Multilingual GPT-6 Luna leads

GLM-4.5V: 44.6 (#177), GPT-6 Luna: 50.5 (#117)

BenchmarkGLM-4.5VGPT-6 Luna
LMArena Non-English13031386
LMArena Chinese13371433
LMArena Russian12981394
LMArena Spanish13361393
LMArena French—1420
LMArena German—1369
LMArena Japanese—1369
LMArena Korean—1360

Instruction Following GPT-6 Luna leads

GLM-4.5V: 69.2 (#175), GPT-6 Luna: 74.3 (#99)

BenchmarkGLM-4.5VGPT-6 Luna
LMArena Instruction Following13111409

Long Context GPT-6 Luna leads

GLM-4.5V: 39.6 (#171), GPT-6 Luna: 43.0 (#111)

BenchmarkGLM-4.5VGPT-6 Luna
LMArena Longer Query13041409

Writing & Preference GPT-6 Luna leads

GLM-4.5V: 52.5 (#170), GPT-6 Luna: 58.3 (#119)

BenchmarkGLM-4.5VGPT-6 Luna
LMArena Text13331391
LMArena Creative Writing12951363
LMArena Multi-Turn13321396

Frequently asked questions

Is GLM-4.5V better than GPT-6 Luna?

GPT-6 Luna is the stronger model overall, scoring 53.3 to 39.8 on the Noometry Index.

Which is cheaper, GLM-4.5V or GPT-6 Luna?

GPT-6 Luna is cheaper. It lists at $0.10 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 GPT-6 Luna better for coding?

GPT-6 Luna scores higher on coding benchmarks: 55.5 versus 39.5 in the Noometry coding category.

Which has the bigger context window?

GPT-6 Luna does, with 1.05M tokens against 64K.

How many benchmarks do GLM-4.5V and GPT-6 Luna share?

14 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and GPT-6 Luna has 42.

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