Noometry

Model comparison

GLM-4.5V vs GLM-4.7

GLM-4.7 is the stronger model overall, scoring 42.0 to 39.8 on the Noometry Index.

Last verified . 13 shared benchmarks.

GLM-4.5V Z.ai (Zhipu)

39.8

Rank #158 Confirmed

GLM-4.7 Z.ai (Zhipu)

42.0

Rank #124 Confirmed

Summary

  • They share 13 benchmarks with published results for both. GLM-4.5V scores higher in 1 category and GLM-4.7 in 7 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-4.7 leads 47.0 to 37.5.
  • GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
  • GLM-4.7 accepts more context: 205K tokens versus 64K.

Side by side

GLM-4.5VGLM-4.7
ProviderZ.ai (Zhipu)Z.ai (Zhipu)
Noometry Index39.842.0
Released2025-08-112025-12-22
WeightsOpenOpen
Context window64K205K
Max output16K131K
Input $ / M tokens$0.60$0.60
Output $ / M tokens$1.80$2.20
Results tracked1536

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Category by category

Coding GLM-4.7 leads

GLM-4.5V: 39.5 (#155), GLM-4.7: 44.0 (#79)

BenchmarkGLM-4.5VGLM-4.7
LMArena Coding13471454
LMArena WebDev—1435
SciCode—45.1%
ALE-Bench—399.48

Agentic & Tool Use Not comparable

GLM-4.5V: —, GLM-4.7: 26.5 (#103)

BenchmarkGLM-4.5VGLM-4.7
Terminal-Bench—33.4%
Vending-Bench 2—2,377

Reasoning GLM-4.5V leads

GLM-4.5V: 27.4 (#119), GLM-4.7: 24.3 (#164)

BenchmarkGLM-4.5VGLM-4.7
LMArena Hard Prompts13341443
SimpleBench—47.7%
Kagi LLM Benchmark59.8%—
CritPt—1.7%
Chess Puzzles—6%
Epoch Capabilities Index—143.51

Math GLM-4.7 leads

GLM-4.5V: 37.4 (#159), GLM-4.7: 38.6 (#135)

BenchmarkGLM-4.5VGLM-4.7
LMArena Math13541423
OTIS Mock AIME 2024-2025—83.3%
ProofBench—6%
FrontierMath (Feb 2025 set)—2.4%
FrontierMath Tier 4 (v1)—0%

Knowledge GLM-4.7 leads

GLM-4.5V: 37.5 (#156), GLM-4.7: 47.0 (#80)

BenchmarkGLM-4.5VGLM-4.7
LMArena Expert13531424
GPQA Diamond—83.3%
SimpleQA Verified—32.2%
Vectara Hallucination Rate—11.7%

Multimodal Not comparable

GLM-4.5V: 34.3 (#92), GLM-4.7: —

BenchmarkGLM-4.5VGLM-4.7
LMArena Vision1154—

Multilingual GLM-4.7 leads

GLM-4.5V: 44.6 (#177), GLM-4.7: 52.8 (#79)

BenchmarkGLM-4.5VGLM-4.7
LMArena Non-English13031417
LMArena Chinese13371495
LMArena Russian12981423
LMArena Spanish13361434
LMArena French—1432
LMArena German—1424
LMArena Japanese—1439
LMArena Korean—1399

Instruction Following GLM-4.7 leads

GLM-4.5V: 69.2 (#175), GLM-4.7: 74.4 (#95)

BenchmarkGLM-4.5VGLM-4.7
LMArena Instruction Following13111411

Long Context GLM-4.7 leads

GLM-4.5V: 39.6 (#171), GLM-4.7: 42.8 (#116)

BenchmarkGLM-4.5VGLM-4.7
LMArena Longer Query13041432
CL-bench—15.9%
CL-bench Life—10.9%

Writing & Preference GLM-4.7 leads

GLM-4.5V: 52.5 (#170), GLM-4.7: 60.9 (#93)

BenchmarkGLM-4.5VGLM-4.7
LMArena Text13331435
LMArena Creative Writing12951401
LMArena Multi-Turn13321446
EQ-Bench Creative Writing—1413

Frequently asked questions

Is GLM-4.5V better than GLM-4.7?

GLM-4.7 is the stronger model overall, scoring 42.0 to 39.8 on the Noometry Index.

Which is cheaper, GLM-4.5V or GLM-4.7?

GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; GLM-4.7 lists at $0.60 and $2.20.

Is GLM-4.5V or GLM-4.7 better for coding?

GLM-4.7 scores higher on coding benchmarks: 44.0 versus 39.5 in the Noometry coding category.

Which has the bigger context window?

GLM-4.7 does, with 205K tokens against 64K.

How many benchmarks do GLM-4.5V and GLM-4.7 share?

13 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and GLM-4.7 has 36.

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