Model comparison

GLM-4.7 vs Qwen1.5-110B

GLM-4.7 is the stronger model overall, scoring 42.0 to 34.2 on the Noometry Index.

Last verified . 17 shared benchmarks.

GLM-4.7 Z.ai (Zhipu)

42.0

Rank #124 Confirmed

Qwen1.5-110B Alibaba (Qwen)

34.2

Rank #234 Confirmed

Summary

  • They share 17 benchmarks with published results for both. GLM-4.7 scores higher in 8 categories and Qwen1.5-110B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where GLM-4.7 leads 60.9 to 38.0.

Side by side

GLM-4.7 and Qwen1.5-110B specifications
GLM-4.7Qwen1.5-110B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index42.034.2
Released2025-12-222024-04-25
WeightsOpenOpen
Context window205K—
Max output131K—
Input $ / M tokens$0.60—
Output $ / M tokens$2.20—
Results tracked3620

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Category by category

Coding GLM-4.7 leads

GLM-4.7: 44.0 (#79), Qwen1.5-110B: 33.0 (#264)

Coding benchmarks
BenchmarkGLM-4.7Qwen1.5-110B
LMArena Coding14541184
LMArena WebDev1435—
SciCode45.1%—
BigCodeBench Instruct—35%
BigCodeBench Complete—44.4%
ALE-Bench399.48—

Agentic & Tool Use Not comparable

GLM-4.7: 26.5 (#103), Qwen1.5-110B: —

Agentic & Tool Use benchmarks
BenchmarkGLM-4.7Qwen1.5-110B
Terminal-Bench33.4%—
Vending-Bench 22,377—

Reasoning GLM-4.7 leads

GLM-4.7: 24.3 (#164), Qwen1.5-110B: 22.7 (#189)

Reasoning benchmarks
BenchmarkGLM-4.7Qwen1.5-110B
LMArena Hard Prompts14431168
SimpleBench47.7%—
CritPt1.7%—
Chess Puzzles6%—
Epoch Capabilities Index143.51—
ForecastBench—57.7

Math GLM-4.7 leads

GLM-4.7: 38.6 (#135), Qwen1.5-110B: 33.7 (#201)

Math benchmarks
BenchmarkGLM-4.7Qwen1.5-110B
LMArena Math14231185
OTIS Mock AIME 2024-202583.3%—
ProofBench6%—
FrontierMath (Feb 2025 set)2.4%—
FrontierMath Tier 4 (v1)0%—

Knowledge GLM-4.7 leads

GLM-4.7: 47.0 (#80), Qwen1.5-110B: 31.2 (#219)

Knowledge benchmarks
BenchmarkGLM-4.7Qwen1.5-110B
LMArena Expert14241144
GPQA Diamond83.3%—
SimpleQA Verified32.2%—
Vectara Hallucination Rate11.7%—

Multilingual GLM-4.7 leads

GLM-4.7: 52.8 (#79), Qwen1.5-110B: 33.6 (#250)

Multilingual benchmarks
BenchmarkGLM-4.7Qwen1.5-110B
LMArena Non-English14171142
LMArena Chinese14951206
LMArena French14321151
LMArena German14241123
LMArena Japanese14391074
LMArena Korean13991044
LMArena Russian14231118
LMArena Spanish14341142

Instruction Following GLM-4.7 leads

GLM-4.7: 74.4 (#95), Qwen1.5-110B: 60.3 (#252)

Instruction Following benchmarks
BenchmarkGLM-4.7Qwen1.5-110B
LMArena Instruction Following14111158

Long Context GLM-4.7 leads

GLM-4.7: 42.8 (#116), Qwen1.5-110B: 35.1 (#242)

Long Context benchmarks
BenchmarkGLM-4.7Qwen1.5-110B
LMArena Longer Query14321157
CL-bench15.9%—
CL-bench Life10.9%—

Writing & Preference GLM-4.7 leads

GLM-4.7: 60.9 (#93), Qwen1.5-110B: 38.0 (#255)

Writing & Preference benchmarks
BenchmarkGLM-4.7Qwen1.5-110B
LMArena Text14351175
LMArena Creative Writing14011148
LMArena Multi-Turn14461160
EQ-Bench Creative Writing1413—

Frequently asked questions

Is GLM-4.7 better than Qwen1.5-110B?

GLM-4.7 is the stronger model overall, scoring 42.0 to 34.2 on the Noometry Index.

Is GLM-4.7 or Qwen1.5-110B better for coding?

GLM-4.7 scores higher on coding benchmarks: 44.0 versus 33.0 in the Noometry coding category.

How many benchmarks do GLM-4.7 and Qwen1.5-110B share?

17 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Qwen1.5-110B has 20.

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