Noometry

Model comparison

GLM-4.7 vs Qwen3 32B

GLM-4.7 is the stronger model overall, scoring 41.7 to 38.8 on the Noometry Index.

Last verified . 20 shared benchmarks.

GLM-4.7 Z.ai (Zhipu)

41.7

Rank #117 Confirmed

Qwen3 32B Alibaba (Qwen)

38.8

Rank #166 Confirmed

Summary

  • They share 20 benchmarks with published results for both. GLM-4.7 scores higher in 6 categories and Qwen3 32B in 3 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where GLM-4.7 leads 61.1 to 53.1.
  • The biggest single-benchmark swing is GPQA Diamond: 83.3% for GLM-4.7 and 65.7% for Qwen3 32B.
  • GLM-4.7 is cheaper at $0.60 / $2.20 per million input/output tokens, against $0.70 / $2.80 for Qwen3 32B.
  • GLM-4.7 accepts more context: 205K tokens versus 131K.

Side by side

GLM-4.7Qwen3 32B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index41.738.8
Released2025-12-222025-04
WeightsOpenOpen
Context window205K131K
Max output131K16K
Input $ / M tokens$0.60$0.70
Output $ / M tokens$2.20$2.80
Results tracked3626

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Category by category

Coding GLM-4.7 leads

GLM-4.7: 43.6 (#74), Qwen3 32B: 37.3 (#184)

BenchmarkGLM-4.7Qwen3 32B
SciCode45.1%35.4%
LMArena Coding14541358
Aider Polyglot—40%
LMArena WebDev1435—
ALE-Bench399.48—

Agentic & Tool Use Qwen3 32B leads

GLM-4.7: 26.4 (#96), Qwen3 32B: 32.3 (#58)

BenchmarkGLM-4.7Qwen3 32B
Terminal-Bench33.4%—
Berkeley Function Calling Leaderboard—48.7%
Vending-Bench 22,377—

Reasoning GLM-4.7 leads

GLM-4.7: 23.7 (#161), Qwen3 32B: 19.6 (#233)

BenchmarkGLM-4.7Qwen3 32B
CritPt1.7%0.3%
Chess Puzzles6%5%
LMArena Hard Prompts14431335
Epoch Capabilities Index143.53138.51
SimpleBench47.7%—
Kagi LLM Benchmark—54.9%
DTBench—67.5%
LMCA—17.3%

Math Too close to call

GLM-4.7: 37.7 (#92), Qwen3 32B: 37.9 (#89)

BenchmarkGLM-4.7Qwen3 32B
OTIS Mock AIME 2024-202583.3%66.9%
LMArena Math14231399
ProofBench6%—
FrontierMath (Feb 2025 set)2.4%—
FrontierMath Tier 4 (v1)0%—

Knowledge GLM-4.7 leads

GLM-4.7: 47.0 (#74), Qwen3 32B: 40.0 (#112)

BenchmarkGLM-4.7Qwen3 32B
GPQA Diamond83.3%65.7%
Vectara Hallucination Rate11.7%5.9%
LMArena Expert14241362
SimpleQA Verified32.2%—

Multilingual GLM-4.7 leads

GLM-4.7: 53.0 (#75), Qwen3 32B: 45.8 (#162)

BenchmarkGLM-4.7Qwen3 32B
LMArena Non-English14171317
LMArena Chinese14951357
LMArena German14261341
LMArena Russian14231311
LMArena French1432—
LMArena Japanese1439—
LMArena Korean1401—
LMArena Spanish1433—

Instruction Following GLM-4.7 leads

GLM-4.7: 74.6 (#90), Qwen3 32B: 69.1 (#174)

BenchmarkGLM-4.7Qwen3 32B
LMArena Instruction Following14111305

Long Context Too close to call

GLM-4.7: 42.8 (#111), Qwen3 32B: 43.7 (#83)

BenchmarkGLM-4.7Qwen3 32B
LMArena Longer Query14321327
Fiction.LiveBench—74.2%
CL-bench15.9%—
CL-bench Life10.9%—

Writing & Preference GLM-4.7 leads

GLM-4.7: 61.1 (#89), Qwen3 32B: 53.1 (#158)

BenchmarkGLM-4.7Qwen3 32B
LMArena Text14361340
LMArena Creative Writing14011297
LMArena Multi-Turn14461331
EQ-Bench Creative Writing1413—

Frequently asked questions

Is GLM-4.7 better than Qwen3 32B?

GLM-4.7 is the stronger model overall, scoring 41.7 to 38.8 on the Noometry Index.

Which is cheaper, GLM-4.7 or Qwen3 32B?

GLM-4.7 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Qwen3 32B lists at $0.70 and $2.80.

Is GLM-4.7 or Qwen3 32B better for coding?

GLM-4.7 scores higher on coding benchmarks: 43.6 versus 37.3 in the Noometry coding category.

Which has the bigger context window?

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

How many benchmarks do GLM-4.7 and Qwen3 32B share?

20 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Qwen3 32B has 26.

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