Noometry

Model comparison

GLM-4.6 vs Qwen3 32B

GLM-4.6 is the stronger model overall, scoring 41.4 to 39.2 on the Noometry Index.

Last verified . 18 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

Qwen3 32B Alibaba (Qwen)

39.2

Rank #172 Confirmed

Summary

  • They share 18 benchmarks with published results for both. GLM-4.6 scores higher in 6 categories and Qwen3 32B in 3 categories; 5 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where GLM-4.6 leads 61.1 to 52.9.
  • The biggest single-benchmark swing is Berkeley Function Calling Leaderboard: 72.4% for GLM-4.6 and 48.7% for Qwen3 32B.
  • GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $0.70 / $2.80 for Qwen3 32B.
  • GLM-4.6 accepts more context: 205K tokens versus 131K.

Side by side

GLM-4.6Qwen3 32B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index41.439.2
Released2025-09-302025-04
WeightsOpenOpen
Context window205K131K
Max output131K16K
Input $ / M tokens$0.60$0.70
Output $ / M tokens$2.20$2.80
Results tracked2926

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Category by category

Coding GLM-4.6 leads

GLM-4.6: 40.1 (#148), Qwen3 32B: 37.7 (#190)

BenchmarkGLM-4.6Qwen3 32B
SciCode38.4%35.4%
LMArena Coding14491358
SWE-bench Verified (bash only)55.4%—
Aider Polyglot—40%
LMArena WebDev1340—
ALE-Bench340.82—

Agentic & Tool Use Too close to call

GLM-4.6: 32.3 (#66), Qwen3 32B: 32.6 (#62)

BenchmarkGLM-4.6Qwen3 32B
Berkeley Function Calling Leaderboard72.4%48.7%
Terminal-Bench24.5%—

Reasoning GLM-4.6 leads

GLM-4.6: 23.7 (#172), Qwen3 32B: 20.2 (#241)

BenchmarkGLM-4.6Qwen3 32B
Kagi LLM Benchmark47.4%54.9%
CritPt1.1%0.3%
LMArena Hard Prompts14401334
Chess Puzzles—5%
DTBench—67.5%
LMCA—17.3%
Epoch Capabilities Index—138.51

Math Too close to call

GLM-4.6: 39.1 (#111), Qwen3 32B: 39.7 (#99)

BenchmarkGLM-4.6Qwen3 32B
LMArena Math14321399
OTIS Mock AIME 2024-2025—66.9%
FrontierMath (Feb 2025 set)3.8%—
FrontierMath Tier 4 (v1)2.1%—

Knowledge Too close to call

GLM-4.6: 40.2 (#124), Qwen3 32B: 40.0 (#125)

BenchmarkGLM-4.6Qwen3 32B
Vectara Hallucination Rate9.5%5.9%
LMArena Expert14311362
GPQA Diamond—65.7%

Multilingual GLM-4.6 leads

GLM-4.6: 53.5 (#66), Qwen3 32B: 45.6 (#167)

BenchmarkGLM-4.6Qwen3 32B
LMArena Non-English14261317
LMArena Chinese14991357
LMArena German14471341
LMArena Russian14191311
LMArena French1459—
LMArena Japanese1393—
LMArena Korean1400—
LMArena Spanish1436—

Instruction Following GLM-4.6 leads

GLM-4.6: 74.3 (#98), Qwen3 32B: 68.9 (#179)

BenchmarkGLM-4.6Qwen3 32B
LMArena Instruction Following14101305

Long Context Too close to call

GLM-4.6: 43.4 (#94), Qwen3 32B: 43.8 (#87)

BenchmarkGLM-4.6Qwen3 32B
LMArena Longer Query14221327
Fiction.LiveBench—74.2%

Writing & Preference GLM-4.6 leads

GLM-4.6: 61.1 (#90), Qwen3 32B: 52.9 (#163)

BenchmarkGLM-4.6Qwen3 32B
LMArena Text14401340
LMArena Creative Writing14111297
LMArena Multi-Turn14271331
EQ-Bench Creative Writing1411—

Frequently asked questions

Is GLM-4.6 better than Qwen3 32B?

GLM-4.6 is the stronger model overall, scoring 41.4 to 39.2 on the Noometry Index.

Which is cheaper, GLM-4.6 or Qwen3 32B?

GLM-4.6 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-4.6 or Qwen3 32B better for coding?

GLM-4.6 scores higher on coding benchmarks: 40.1 versus 37.7 in the Noometry coding category.

Which has the bigger context window?

GLM-4.6 does, with 205K tokens against 131K.

How many benchmarks do GLM-4.6 and Qwen3 32B share?

18 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Qwen3 32B has 26.

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