Noometry

Model comparison

GLM-4.7 vs Qwen3 32B

GLM-4.7 is the stronger model overall, scoring 42.0 to 39.2 on the Noometry Index.

Last verified . 20 shared benchmarks.

GLM-4.7 Z.ai (Zhipu)

42.0

Rank #124 Confirmed

Qwen3 32B Alibaba (Qwen)

39.2

Rank #172 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; 8 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where GLM-4.7 leads 60.9 to 52.9.
  • 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 Index42.039.2
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: 44.0 (#79), Qwen3 32B: 37.7 (#190)

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: 26.5 (#103), Qwen3 32B: 32.6 (#62)

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: 24.3 (#164), Qwen3 32B: 20.2 (#241)

BenchmarkGLM-4.7Qwen3 32B
CritPt1.7%0.3%
Chess Puzzles6%5%
LMArena Hard Prompts14431334
Epoch Capabilities Index143.51138.51
SimpleBench47.7%—
Kagi LLM Benchmark—54.9%
DTBench—67.5%
LMCA—17.3%

Math Qwen3 32B leads

GLM-4.7: 38.6 (#135), Qwen3 32B: 39.7 (#99)

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 (#80), Qwen3 32B: 40.0 (#125)

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: 52.8 (#79), Qwen3 32B: 45.6 (#167)

BenchmarkGLM-4.7Qwen3 32B
LMArena Non-English14171317
LMArena Chinese14951357
LMArena German14241341
LMArena Russian14231311
LMArena French1432—
LMArena Japanese1439—
LMArena Korean1399—
LMArena Spanish1434—

Instruction Following GLM-4.7 leads

GLM-4.7: 74.4 (#95), Qwen3 32B: 68.9 (#179)

BenchmarkGLM-4.7Qwen3 32B
LMArena Instruction Following14111305

Long Context Too close to call

GLM-4.7: 42.8 (#116), Qwen3 32B: 43.8 (#87)

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: 60.9 (#93), Qwen3 32B: 52.9 (#163)

BenchmarkGLM-4.7Qwen3 32B
LMArena Text14351340
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 42.0 to 39.2 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: 44.0 versus 37.7 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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