Model comparison

GLM-5 vs Qwen1.5-7B

GLM-5 is the stronger model overall, scoring 46.1 to 31.4 on the Noometry Index.

Last verified . 12 shared benchmarks.

GLM-5 Z.ai (Zhipu)

46.1

Rank #66 Confirmed

Qwen1.5-7B Alibaba (Qwen)

31.4

Rank #273 Confirmed

Summary

  • They share 12 benchmarks with published results for both. GLM-5 scores higher in 8 categories and Qwen1.5-7B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where GLM-5 leads 66.0 to 29.6.

Side by side

GLM-5 and Qwen1.5-7B specifications
GLM-5Qwen1.5-7B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index46.131.4
Released2026-02-112024-02-04
WeightsOpenOpen
Context window205K—
Max output131K—
Input $ / M tokens$1—
Output $ / M tokens$3.20—
Results tracked4513

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Category by category

Coding GLM-5 leads

GLM-5: 49.0 (#52), Qwen1.5-7B: 32.2 (#276)

Coding benchmarks
BenchmarkGLM-5Qwen1.5-7B
LMArena Coding14611107
SWE-bench Verified72.1%—
SWE-bench Verified (bash only)72.8%—
LMArena WebDev1434—
SWE-bench Multilingual69.7%—
WeirdML48.2%—
ALE-Bench765.62—

Agentic & Tool Use Not comparable

GLM-5: 31.1 (#71), Qwen1.5-7B: —

Agentic & Tool Use benchmarks
BenchmarkGLM-5Qwen1.5-7B
Terminal-Bench52.4%—
τ²-bench Airline82.5%—
τ²-bench Banking9.8%—
τ²-bench Retail73.7%—
τ²-bench Telecom86.8%—
Vending-Bench 24,432—

Reasoning GLM-5 leads

GLM-5: 27.6 (#116), Qwen1.5-7B: 20.4 (#240)

Reasoning benchmarks
BenchmarkGLM-5Qwen1.5-7B
LMArena Hard Prompts14521065
ARC-AGI-24.9%—
SimpleBench53.2%—
Kagi LLM Benchmark75%—
NYT Connections (extended)74.8%—
ARC-AGI-144.7%—
Chess Puzzles10%—
Epoch Capabilities Index145.83—
ForecastBench61—

Math GLM-5 leads

GLM-5: 46.4 (#71), Qwen1.5-7B: 31.4 (#224)

Knowledge GLM-5 leads

GLM-5: 52.3 (#64), Qwen1.5-7B: 28.7 (#243)

Knowledge benchmarks
BenchmarkGLM-5Qwen1.5-7B
LMArena Expert14541055
GPQA Diamond87.8%—
Vectara Hallucination Rate10.1%—
MMLU—62.6%

Multilingual GLM-5 leads

GLM-5: 53.7 (#58), Qwen1.5-7B: 28.5 (#271)

Multilingual benchmarks
BenchmarkGLM-5Qwen1.5-7B
LMArena Non-English14301058
LMArena Chinese15111141
LMArena Russian14361006
LMArena French1455—
LMArena German1445—
LMArena Japanese1416—
LMArena Korean1423—
LMArena Spanish1454—

Instruction Following GLM-5 leads

GLM-5: 75.2 (#67), Qwen1.5-7B: 54.1 (#281)

Instruction Following benchmarks
BenchmarkGLM-5Qwen1.5-7B
LMArena Instruction Following14281058

Long Context GLM-5 leads

GLM-5: 44.7 (#60), Qwen1.5-7B: 33.1 (#266)

Long Context benchmarks
BenchmarkGLM-5Qwen1.5-7B
LMArena Longer Query14461090
CL-bench18.7%—

Writing & Preference GLM-5 leads

GLM-5: 66.0 (#38), Qwen1.5-7B: 29.6 (#293)

Writing & Preference benchmarks
BenchmarkGLM-5Qwen1.5-7B
LMArena Text14461083
LMArena Creative Writing14391035
LMArena Multi-Turn14561062
EQ-Bench Creative Writing1601—

Frequently asked questions

Is GLM-5 better than Qwen1.5-7B?

GLM-5 is the stronger model overall, scoring 46.1 to 31.4 on the Noometry Index.

Is GLM-5 or Qwen1.5-7B better for coding?

GLM-5 scores higher on coding benchmarks: 49.0 versus 32.2 in the Noometry coding category.

How many benchmarks do GLM-5 and Qwen1.5-7B share?

12 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Qwen1.5-7B has 13.

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