Noometry

Model comparison

GLM-4.5V vs Qwen3.5-Flash

Qwen3.5-Flash is the stronger model overall, scoring 42.5 to 39.8 on the Noometry Index.

Last verified . 13 shared benchmarks.

GLM-4.5V Z.ai (Zhipu)

39.8

Rank #158 Confirmed

Qwen3.5-Flash Alibaba (Qwen)

42.5

Rank #112 Confirmed

Summary

  • They share 13 benchmarks with published results for both. GLM-4.5V scores higher in 1 category and Qwen3.5-Flash in 7 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where Qwen3.5-Flash leads 33.7 to 27.4.
  • Qwen3.5-Flash is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
  • Qwen3.5-Flash accepts more context: 1M tokens versus 64K.
  • GLM-4.5V has downloadable open weights; the other is API-only.

Side by side

GLM-4.5VQwen3.5-Flash
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index39.842.5
Released2025-08-112026-02-23
WeightsOpenProprietary
Context window64K1M
Max output16K66K
Input $ / M tokens$0.60$0.10
Output $ / M tokens$1.80$0.40
Results tracked1532

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Category by category

Coding GLM-4.5V leads

GLM-4.5V: 39.5 (#155), Qwen3.5-Flash: 34.2 (#242)

BenchmarkGLM-4.5VQwen3.5-Flash
LMArena Coding13471412
LMArena WebDev—1244
ALE-Bench—221.8

Agentic & Tool Use Not comparable

GLM-4.5V: —, Qwen3.5-Flash: —

BenchmarkGLM-4.5VQwen3.5-Flash
Vending-Bench 2—462.69

Reasoning Qwen3.5-Flash leads

GLM-4.5V: 27.4 (#119), Qwen3.5-Flash: 33.7 (#72)

BenchmarkGLM-4.5VQwen3.5-Flash
LMArena Hard Prompts13341403
Kagi LLM Benchmark59.8%—
Chess Puzzles—21%
Mystery Game Puzzles—20%
DTBench—82.9%
LMCA—29.1%
Epoch Capabilities Index—143.98

Math Too close to call

GLM-4.5V: 37.4 (#159), Qwen3.5-Flash: 37.4 (#158)

BenchmarkGLM-4.5VQwen3.5-Flash
LMArena Math13541407
FrontierMath (Tiers 1-3)—18.2%
OTIS Mock AIME 2024-2025—84.4%
FrontierMath (Feb 2025 set)—6.2%
FrontierMath Tier 4 (v1)—0%

Knowledge Qwen3.5-Flash leads

GLM-4.5V: 37.5 (#156), Qwen3.5-Flash: 43.2 (#93)

BenchmarkGLM-4.5VQwen3.5-Flash
LMArena Expert13531407
GPQA Diamond—82.3%
SimpleQA Verified—20.3%
Vectara Hallucination Rate—10.5%

Multimodal Not comparable

GLM-4.5V: 34.3 (#92), Qwen3.5-Flash: —

BenchmarkGLM-4.5VQwen3.5-Flash
LMArena Vision1154—

Multilingual Qwen3.5-Flash leads

GLM-4.5V: 44.6 (#177), Qwen3.5-Flash: 50.5 (#121)

BenchmarkGLM-4.5VQwen3.5-Flash
LMArena Non-English13031385
LMArena Chinese13371446
LMArena Russian12981379
LMArena Spanish13361400
LMArena French—1412
LMArena German—1390
LMArena Japanese—1368
LMArena Korean—1344

Instruction Following Qwen3.5-Flash leads

GLM-4.5V: 69.2 (#175), Qwen3.5-Flash: 72.6 (#139)

BenchmarkGLM-4.5VQwen3.5-Flash
LMArena Instruction Following13111374

Long Context Qwen3.5-Flash leads

GLM-4.5V: 39.6 (#171), Qwen3.5-Flash: 42.4 (#124)

BenchmarkGLM-4.5VQwen3.5-Flash
LMArena Longer Query13041392

Writing & Preference Qwen3.5-Flash leads

GLM-4.5V: 52.5 (#170), Qwen3.5-Flash: 57.9 (#122)

BenchmarkGLM-4.5VQwen3.5-Flash
LMArena Text13331397
LMArena Creative Writing12951343
LMArena Multi-Turn13321393

Frequently asked questions

Is GLM-4.5V better than Qwen3.5-Flash?

Qwen3.5-Flash is the stronger model overall, scoring 42.5 to 39.8 on the Noometry Index.

Which is cheaper, GLM-4.5V or Qwen3.5-Flash?

Qwen3.5-Flash is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.

Is GLM-4.5V or Qwen3.5-Flash better for coding?

GLM-4.5V scores higher on coding benchmarks: 39.5 versus 34.2 in the Noometry coding category.

Which has the bigger context window?

Qwen3.5-Flash does, with 1M tokens against 64K.

How many benchmarks do GLM-4.5V and Qwen3.5-Flash share?

13 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Qwen3.5-Flash has 32.

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