Model comparison

GLM-5 vs Qwen1.5 4b Chat

GLM-5 is the stronger model overall, scoring 46.1 to 28.8 on the Noometry Index.

Last verified . 13 shared benchmarks.

GLM-5 Z.ai (Zhipu)

46.1

Rank #66 Confirmed

Qwen1.5 4b Chat Alibaba (Qwen)

28.8

Rank #322 Confirmed

Summary

  • They share 13 benchmarks with published results for both. GLM-5 scores higher in 8 categories and Qwen1.5 4b Chat in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where GLM-5 leads 66.0 to 23.8.

Side by side

GLM-5 and Qwen1.5 4b Chat specifications
GLM-5Qwen1.5 4b Chat
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index46.128.8
Released2026-02-11—
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 4b Chat: 29.1 (#308)

Coding benchmarks
BenchmarkGLM-5Qwen1.5 4b Chat
LMArena Coding1461999
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 4b Chat: —

Agentic & Tool Use benchmarks
BenchmarkGLM-5Qwen1.5 4b Chat
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 4b Chat: 18.5 (#279)

Reasoning benchmarks
BenchmarkGLM-5Qwen1.5 4b Chat
LMArena Hard Prompts1452976
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 4b Chat: 30.4 (#234)

Knowledge GLM-5 leads

GLM-5: 52.3 (#64), Qwen1.5 4b Chat: 26.7 (#255)

Knowledge benchmarks
BenchmarkGLM-5Qwen1.5 4b Chat
LMArena Expert1454980
GPQA Diamond87.8%—
Vectara Hallucination Rate10.1%—

Multilingual GLM-5 leads

GLM-5: 53.7 (#58), Qwen1.5 4b Chat: 24.1 (#290)

Multilingual benchmarks
BenchmarkGLM-5Qwen1.5 4b Chat
LMArena Non-English1430979
LMArena Chinese15111024
LMArena German1445902
LMArena Russian1436952
LMArena French1455—
LMArena Japanese1416—
LMArena Korean1423—
LMArena Spanish1454—

Instruction Following GLM-5 leads

GLM-5: 75.2 (#67), Qwen1.5 4b Chat: 49.0 (#300)

Instruction Following benchmarks
BenchmarkGLM-5Qwen1.5 4b Chat
LMArena Instruction Following1428978

Long Context GLM-5 leads

GLM-5: 44.7 (#60), Qwen1.5 4b Chat: 30.1 (#290)

Long Context benchmarks
BenchmarkGLM-5Qwen1.5 4b Chat
LMArena Longer Query1446988
CL-bench18.7%—

Writing & Preference GLM-5 leads

GLM-5: 66.0 (#38), Qwen1.5 4b Chat: 23.8 (#309)

Writing & Preference benchmarks
BenchmarkGLM-5Qwen1.5 4b Chat
LMArena Text1446997
LMArena Creative Writing1439969
LMArena Multi-Turn1456977
EQ-Bench Creative Writing1601—

Frequently asked questions

Is GLM-5 better than Qwen1.5 4b Chat?

GLM-5 is the stronger model overall, scoring 46.1 to 28.8 on the Noometry Index.

Is GLM-5 or Qwen1.5 4b Chat better for coding?

GLM-5 scores higher on coding benchmarks: 49.0 versus 29.1 in the Noometry coding category.

How many benchmarks do GLM-5 and Qwen1.5 4b Chat share?

13 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Qwen1.5 4b Chat has 13.

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