Noometry

Model comparison

GLM-4.7 vs Qwen2.5-VL 72B Instruct

GLM-4.7 is the stronger model overall, scoring 41.7 to 29.4 on the Noometry Index.

Last verified . 0 shared benchmarks.

GLM-4.7 Z.ai (Zhipu)

41.7

Rank #117 Confirmed

Qwen2.5-VL 72B Instruct Alibaba (Qwen)

29.4

Rank #298 Confirmed

Summary

  • The widest gap is in agentic & tool use, where GLM-4.7 leads 26.4 to 18.4.
  • GLM-4.7 is cheaper at $0.60 / $2.20 per million input/output tokens, against $2.80 / $8.40 for Qwen2.5-VL 72B Instruct.
  • GLM-4.7 accepts more context: 205K tokens versus 131K.

Side by side

GLM-4.7Qwen2.5-VL 72B Instruct
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index41.729.4
Released2025-12-222024-09
WeightsOpenOpen
Context window205K131K
Max output131K8K
Input $ / M tokens$0.60$2.80
Output $ / M tokens$2.20$8.40
Results tracked366

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Category by category

Coding Not comparable

GLM-4.7: 43.6 (#74), Qwen2.5-VL 72B Instruct: —

BenchmarkGLM-4.7Qwen2.5-VL 72B Instruct
LMArena WebDev1435—
SciCode45.1%—
LMArena Coding1454—
ALE-Bench399.48—

Agentic & Tool Use GLM-4.7 leads

GLM-4.7: 26.4 (#96), Qwen2.5-VL 72B Instruct: 18.4 (#136)

BenchmarkGLM-4.7Qwen2.5-VL 72B Instruct
Terminal-Bench33.4%—
OSWorld—5%
Vending-Bench 22,377—

Reasoning GLM-4.7 leads

GLM-4.7: 23.7 (#161), Qwen2.5-VL 72B Instruct: 20.0 (#228)

BenchmarkGLM-4.7Qwen2.5-VL 72B Instruct
SimpleBench47.7%—
Kagi LLM Benchmark—36%
CritPt1.7%—
Chess Puzzles6%—
LMArena Hard Prompts1443—
Epoch Capabilities Index143.53—

Math Not comparable

GLM-4.7: 37.7 (#92), Qwen2.5-VL 72B Instruct: —

BenchmarkGLM-4.7Qwen2.5-VL 72B Instruct
OTIS Mock AIME 2024-202583.3%—
ProofBench6%—
LMArena Math1423—
FrontierMath (Feb 2025 set)2.4%—
FrontierMath Tier 4 (v1)0%—

Knowledge Not comparable

GLM-4.7: 47.0 (#74), Qwen2.5-VL 72B Instruct: —

BenchmarkGLM-4.7Qwen2.5-VL 72B Instruct
GPQA Diamond83.3%—
SimpleQA Verified32.2%—
Vectara Hallucination Rate11.7%—
LMArena Expert1424—

Multimodal Not comparable

GLM-4.7: —, Qwen2.5-VL 72B Instruct: 32.9 (#92)

BenchmarkGLM-4.7Qwen2.5-VL 72B Instruct
LMArena Vision—1107
Video-MME—73.5%
GeoBench—62%
SpatialViz-Bench—33.3%

Multilingual Not comparable

GLM-4.7: 53.0 (#75), Qwen2.5-VL 72B Instruct: —

BenchmarkGLM-4.7Qwen2.5-VL 72B Instruct
LMArena Non-English1417—
LMArena Chinese1495—
LMArena French1432—
LMArena German1426—
LMArena Japanese1439—
LMArena Korean1401—
LMArena Russian1423—
LMArena Spanish1433—

Instruction Following Not comparable

GLM-4.7: 74.6 (#90), Qwen2.5-VL 72B Instruct: —

BenchmarkGLM-4.7Qwen2.5-VL 72B Instruct
LMArena Instruction Following1411—

Long Context Not comparable

GLM-4.7: 42.8 (#111), Qwen2.5-VL 72B Instruct: —

BenchmarkGLM-4.7Qwen2.5-VL 72B Instruct
CL-bench15.9%—
CL-bench Life10.9%—
LMArena Longer Query1432—

Writing & Preference Not comparable

GLM-4.7: 61.1 (#89), Qwen2.5-VL 72B Instruct: —

BenchmarkGLM-4.7Qwen2.5-VL 72B Instruct
LMArena Text1436—
LMArena Creative Writing1401—
EQ-Bench Creative Writing1413—
LMArena Multi-Turn1446—

Frequently asked questions

Is GLM-4.7 better than Qwen2.5-VL 72B Instruct?

GLM-4.7 is the stronger model overall, scoring 41.7 to 29.4 on the Noometry Index.

Which is cheaper, GLM-4.7 or Qwen2.5-VL 72B Instruct?

GLM-4.7 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Qwen2.5-VL 72B Instruct lists at $2.80 and $8.40.

Which has the bigger context window?

GLM-4.7 does, with 205K tokens against 131K.

How many benchmarks do GLM-4.7 and Qwen2.5-VL 72B Instruct share?

0 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Qwen2.5-VL 72B Instruct has 6.

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