Noometry

Model comparison

GLM-4.5V vs Qwen3.8 27B

Qwen3.8 27B is the stronger model overall, scoring 46.0 to 39.8 on the Noometry Index.

Last verified . 14 shared benchmarks.

GLM-4.5V Z.ai (Zhipu)

39.8

Rank #158 Confirmed

Qwen3.8 27B Alibaba (Qwen)

46.0

Rank #68 Confirmed

Summary

  • They share 14 benchmarks with published results for both. GLM-4.5V scores higher in 1 category and Qwen3.8 27B in 8 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where Qwen3.8 27B leads 41.0 to 27.4.
  • GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $0.99 / $1.49 for Qwen3.8 27B.
  • Qwen3.8 27B accepts more context: 262K tokens versus 64K.

Side by side

GLM-4.5VQwen3.8 27B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index39.846.0
Released2025-08-112026-08-14
WeightsOpenOpen
Context window64K262K
Max output16K33K
Input $ / M tokens$0.60$0.99
Output $ / M tokens$1.80$1.49
Results tracked1531

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Category by category

Coding Qwen3.8 27B leads

GLM-4.5V: 39.5 (#155), Qwen3.8 27B: 50.5 (#44)

BenchmarkGLM-4.5VQwen3.8 27B
LMArena Coding13471482
LMArena WebDev—1593
SciCode—46.6%

Agentic & Tool Use Not comparable

GLM-4.5V: —, Qwen3.8 27B: 32.9 (#57)

BenchmarkGLM-4.5VQwen3.8 27B
APEX-Agents—47.5%

Reasoning Qwen3.8 27B leads

GLM-4.5V: 27.4 (#119), Qwen3.8 27B: 41.0 (#54)

BenchmarkGLM-4.5VQwen3.8 27B
LMArena Hard Prompts13341460
ARC-AGI-2—42.4%
Kagi LLM Benchmark59.8%—
NYT Connections (extended)—54.5%
ARC-AGI-1—87.5%
CritPt—5.4%
DTBench—88%
LMCA—41.4%
Surface Evolver Bench—45%
Epoch Capabilities Index—149.38

Math Too close to call

GLM-4.5V: 37.4 (#159), Qwen3.8 27B: 37.1 (#161)

BenchmarkGLM-4.5VQwen3.8 27B
LMArena Math13541456
ProofBench—16%

Knowledge Qwen3.8 27B leads

GLM-4.5V: 37.5 (#156), Qwen3.8 27B: 41.6 (#109)

BenchmarkGLM-4.5VQwen3.8 27B
LMArena Expert13531482

Multimodal Qwen3.8 27B leads

GLM-4.5V: 34.3 (#92), Qwen3.8 27B: 41.3 (#37)

BenchmarkGLM-4.5VQwen3.8 27B
LMArena Vision11541271

Multilingual Qwen3.8 27B leads

GLM-4.5V: 44.6 (#177), Qwen3.8 27B: 53.7 (#60)

BenchmarkGLM-4.5VQwen3.8 27B
LMArena Non-English13031430
LMArena Chinese13371504
LMArena Russian12981415
LMArena Spanish13361448
LMArena French—1465
LMArena German—1438
LMArena Japanese—1384
LMArena Korean—1393

Instruction Following Qwen3.8 27B leads

GLM-4.5V: 69.2 (#175), Qwen3.8 27B: 75.8 (#53)

BenchmarkGLM-4.5VQwen3.8 27B
LMArena Instruction Following13111439

Long Context Qwen3.8 27B leads

GLM-4.5V: 39.6 (#171), Qwen3.8 27B: 44.3 (#70)

BenchmarkGLM-4.5VQwen3.8 27B
LMArena Longer Query13041450

Writing & Preference Qwen3.8 27B leads

GLM-4.5V: 52.5 (#170), Qwen3.8 27B: 65.8 (#43)

BenchmarkGLM-4.5VQwen3.8 27B
LMArena Text13331441
LMArena Creative Writing12951384
LMArena Multi-Turn13321441
EQ-Bench Creative Writing—1671

Frequently asked questions

Is GLM-4.5V better than Qwen3.8 27B?

Qwen3.8 27B is the stronger model overall, scoring 46.0 to 39.8 on the Noometry Index.

Which is cheaper, GLM-4.5V or Qwen3.8 27B?

GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; Qwen3.8 27B lists at $0.99 and $1.49.

Is GLM-4.5V or Qwen3.8 27B better for coding?

Qwen3.8 27B scores higher on coding benchmarks: 50.5 versus 39.5 in the Noometry coding category.

Which has the bigger context window?

Qwen3.8 27B does, with 262K tokens against 64K.

How many benchmarks do GLM-4.5V and Qwen3.8 27B share?

14 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Qwen3.8 27B has 31.

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