Noometry

Model comparison

GLM-4.5 vs Qwen3.8 27B

Qwen3.8 27B is the stronger model overall, scoring 46.0 to 42.0 on the Noometry Index.

Last verified . 18 shared benchmarks.

GLM-4.5 Z.ai (Zhipu)

42.0

Rank #122 Confirmed

Qwen3.8 27B Alibaba (Qwen)

46.0

Rank #68 Confirmed

Summary

  • They share 18 benchmarks with published results for both. GLM-4.5 scores higher in 1 category and Qwen3.8 27B in 7 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where Qwen3.8 27B leads 41.0 to 28.6.
  • Qwen3.8 27B is cheaper at $0.04 / $2.30 per million input/output tokens, against $0.60 / $2.20 for GLM-4.5.
  • Qwen3.8 27B accepts more context: 262K tokens versus 131K.

Side by side

GLM-4.5Qwen3.8 27B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index42.046.0
Released2025-07-272026-08-14
WeightsOpenOpen
Context window131K262K
Max output98K33K
Input $ / M tokens$0.60$0.04
Output $ / M tokens$2.20$2.30
Results tracked2731

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Category by category

Coding Qwen3.8 27B leads

GLM-4.5: 41.4 (#125), Qwen3.8 27B: 50.5 (#44)

BenchmarkGLM-4.5Qwen3.8 27B
LMArena Coding14341482
SWE-bench Verified (bash only)54.2%—
LMArena WebDev—1593
SciCode—46.6%
WeirdML40.6%—
ALE-Bench344.82—
AlgoTune1.52—

Agentic & Tool Use Not comparable

GLM-4.5: —, Qwen3.8 27B: 32.9 (#57)

BenchmarkGLM-4.5Qwen3.8 27B
APEX-Agents—47.5%

Reasoning Qwen3.8 27B leads

GLM-4.5: 28.6 (#100), Qwen3.8 27B: 41.0 (#54)

BenchmarkGLM-4.5Qwen3.8 27B
LMArena Hard Prompts14291460
ARC-AGI-2—42.4%
Kagi LLM Benchmark57.9%—
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 GLM-4.5 leads

GLM-4.5: 39.0 (#116), Qwen3.8 27B: 37.1 (#161)

BenchmarkGLM-4.5Qwen3.8 27B
LMArena Math14271456
ProofBench—16%

Knowledge Qwen3.8 27B leads

GLM-4.5: 35.9 (#179), Qwen3.8 27B: 41.6 (#109)

BenchmarkGLM-4.5Qwen3.8 27B
LMArena Expert14331482
Humanity's Last Exam8.3%—
Confabulations11.3%—

Multimodal Not comparable

GLM-4.5: —, Qwen3.8 27B: 41.3 (#37)

BenchmarkGLM-4.5Qwen3.8 27B
LMArena Vision—1271

Multilingual Too close to call

GLM-4.5: 52.8 (#77), Qwen3.8 27B: 53.7 (#60)

BenchmarkGLM-4.5Qwen3.8 27B
LMArena Non-English14171430
LMArena Chinese14651504
LMArena French14181465
LMArena German14071438
LMArena Japanese14151384
LMArena Korean13801393
LMArena Russian14141415
LMArena Spanish14541448

Instruction Following Qwen3.8 27B leads

GLM-4.5: 74.1 (#104), Qwen3.8 27B: 75.8 (#53)

BenchmarkGLM-4.5Qwen3.8 27B
LMArena Instruction Following14041439

Long Context Qwen3.8 27B leads

GLM-4.5: 38.2 (#201), Qwen3.8 27B: 44.3 (#70)

BenchmarkGLM-4.5Qwen3.8 27B
LMArena Longer Query14121450
Fiction.LiveBench58.3%—

Writing & Preference Qwen3.8 27B leads

GLM-4.5: 57.5 (#127), Qwen3.8 27B: 65.8 (#43)

BenchmarkGLM-4.5Qwen3.8 27B
LMArena Text14301441
LMArena Creative Writing13951384
EQ-Bench Creative Writing13431671
LMArena Multi-Turn14151441
Short-Story Creative Writing73.4%—

Frequently asked questions

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

Qwen3.8 27B is the stronger model overall, scoring 46.0 to 42.0 on the Noometry Index.

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

Qwen3.8 27B is cheaper. It lists at $0.04 per million input tokens and $2.30 per million output tokens; GLM-4.5 lists at $0.60 and $2.20.

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

Qwen3.8 27B scores higher on coding benchmarks: 50.5 versus 41.4 in the Noometry coding category.

Which has the bigger context window?

Qwen3.8 27B does, with 262K tokens against 131K.

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

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

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