Noometry

Model comparison

GLM-4.6 vs Qwen3-VL 235B-A22B

Qwen3-VL 235B-A22B is the stronger model overall, scoring 43.2 to 41.4 on the Noometry Index.

Last verified . 17 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

Qwen3-VL 235B-A22B Alibaba (Qwen)

43.2

Rank #95 Confirmed

Summary

  • They share 17 benchmarks with published results for both. GLM-4.6 scores higher in 5 categories and Qwen3-VL 235B-A22B in 3 categories; 3 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where Qwen3-VL 235B-A22B leads 29.3 to 23.7.
  • GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $0.70 / $2.80 for Qwen3-VL 235B-A22B.
  • GLM-4.6 accepts more context: 205K tokens versus 131K.

Side by side

GLM-4.6Qwen3-VL 235B-A22B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index41.443.2
Released2025-09-302025-04
WeightsOpenOpen
Context window205K131K
Max output131K33K
Input $ / M tokens$0.60$0.70
Output $ / M tokens$2.20$2.80
Results tracked2918

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Category by category

Coding Qwen3-VL 235B-A22B leads

GLM-4.6: 40.1 (#148), Qwen3-VL 235B-A22B: 42.4 (#100)

BenchmarkGLM-4.6Qwen3-VL 235B-A22B
LMArena Coding14491439
SWE-bench Verified (bash only)55.4%—
LMArena WebDev1340—
SciCode38.4%—
ALE-Bench340.82—

Agentic & Tool Use Not comparable

GLM-4.6: 32.3 (#66), Qwen3-VL 235B-A22B: —

BenchmarkGLM-4.6Qwen3-VL 235B-A22B
Terminal-Bench24.5%—
Berkeley Function Calling Leaderboard72.4%—

Reasoning Qwen3-VL 235B-A22B leads

GLM-4.6: 23.7 (#172), Qwen3-VL 235B-A22B: 29.3 (#92)

BenchmarkGLM-4.6Qwen3-VL 235B-A22B
LMArena Hard Prompts14401428
Kagi LLM Benchmark47.4%—
CritPt1.1%—

Math Too close to call

GLM-4.6: 39.1 (#111), Qwen3-VL 235B-A22B: 39.0 (#118)

BenchmarkGLM-4.6Qwen3-VL 235B-A22B
LMArena Math14321426
FrontierMath (Feb 2025 set)3.8%—
FrontierMath Tier 4 (v1)2.1%—

Knowledge Too close to call

GLM-4.6: 40.2 (#124), Qwen3-VL 235B-A22B: 40.3 (#121)

BenchmarkGLM-4.6Qwen3-VL 235B-A22B
LMArena Expert14311442
Vectara Hallucination Rate9.5%—

Multimodal Not comparable

GLM-4.6: —, Qwen3-VL 235B-A22B: 39.8 (#55)

BenchmarkGLM-4.6Qwen3-VL 235B-A22B
LMArena Vision—1247

Multilingual GLM-4.6 leads

GLM-4.6: 53.5 (#66), Qwen3-VL 235B-A22B: 51.9 (#97)

BenchmarkGLM-4.6Qwen3-VL 235B-A22B
LMArena Non-English14261405
LMArena Chinese14991463
LMArena French14591452
LMArena German14471424
LMArena Japanese13931385
LMArena Korean14001394
LMArena Russian14191408
LMArena Spanish14361428

Instruction Following Too close to call

GLM-4.6: 74.3 (#98), Qwen3-VL 235B-A22B: 74.2 (#101)

BenchmarkGLM-4.6Qwen3-VL 235B-A22B
LMArena Instruction Following14101406

Long Context Too close to call

GLM-4.6: 43.4 (#94), Qwen3-VL 235B-A22B: 43.4 (#98)

BenchmarkGLM-4.6Qwen3-VL 235B-A22B
LMArena Longer Query14221420

Writing & Preference Too close to call

GLM-4.6: 61.1 (#90), Qwen3-VL 235B-A22B: 60.2 (#99)

BenchmarkGLM-4.6Qwen3-VL 235B-A22B
LMArena Text14401420
LMArena Creative Writing14111366
LMArena Multi-Turn14271428
EQ-Bench Creative Writing1411—

Frequently asked questions

Is GLM-4.6 better than Qwen3-VL 235B-A22B?

Qwen3-VL 235B-A22B is the stronger model overall, scoring 43.2 to 41.4 on the Noometry Index.

Which is cheaper, GLM-4.6 or Qwen3-VL 235B-A22B?

GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Qwen3-VL 235B-A22B lists at $0.70 and $2.80.

Is GLM-4.6 or Qwen3-VL 235B-A22B better for coding?

Qwen3-VL 235B-A22B scores higher on coding benchmarks: 42.4 versus 40.1 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-VL 235B-A22B share?

17 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Qwen3-VL 235B-A22B has 18.

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