Noometry

Model comparison

GLM-4.7 vs Qwen3.5 27B

GLM-4.7 and Qwen3.5 27B score almost the same on the Noometry Index (42.0 vs 41.9), so choose on price, context window or the category you care about most.

Last verified . 21 shared benchmarks.

GLM-4.7 Z.ai (Zhipu)

42.0

Rank #124 Confirmed

Qwen3.5 27B Alibaba (Qwen)

41.9

Rank #127 Confirmed

Summary

  • They share 21 benchmarks with published results for both. GLM-4.7 scores higher in 5 categories and Qwen3.5 27B in 3 categories; 5 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-4.7 leads 47.0 to 38.0.
  • Qwen3.5 27B is cheaper at $0.30 / $2.40 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
  • Qwen3.5 27B accepts more context: 262K tokens versus 205K.

Side by side

GLM-4.7Qwen3.5 27B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index42.041.9
Released2025-12-222026-02-23
WeightsOpenOpen
Context window205K262K
Max output131K66K
Input $ / M tokens$0.60$0.30
Output $ / M tokens$2.20$2.40
Results tracked3628

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Category by category

Coding GLM-4.7 leads

GLM-4.7: 44.0 (#79), Qwen3.5 27B: 38.9 (#168)

BenchmarkGLM-4.7Qwen3.5 27B
LMArena WebDev14351358
LMArena Coding14541427
ALE-Bench399.48349.45
SciCode45.1%—
WeirdML—39.5%

Agentic & Tool Use Not comparable

GLM-4.7: 26.5 (#103), Qwen3.5 27B: —

BenchmarkGLM-4.7Qwen3.5 27B
Vending-Bench 22,377201.98
Terminal-Bench33.4%—

Reasoning Qwen3.5 27B leads

GLM-4.7: 24.3 (#164), Qwen3.5 27B: 27.5 (#117)

BenchmarkGLM-4.7Qwen3.5 27B
LMArena Hard Prompts14431414
SimpleBench47.7%—
NYT Connections (extended)—47.9%
CritPt1.7%—
Chess Puzzles6%—
Thematic Generalization—45.5%
DTBench—82.4%
LMCA—34%
Epoch Capabilities Index143.51—

Math Too close to call

GLM-4.7: 38.6 (#135), Qwen3.5 27B: 38.8 (#127)

BenchmarkGLM-4.7Qwen3.5 27B
LMArena Math14231429
MathArena Final-Answer Competitions—56.7%
OTIS Mock AIME 2024-202583.3%—
ProofBench6%—
FrontierMath (Feb 2025 set)2.4%—
FrontierMath Tier 4 (v1)0%—

Knowledge GLM-4.7 leads

GLM-4.7: 47.0 (#80), Qwen3.5 27B: 38.0 (#150)

BenchmarkGLM-4.7Qwen3.5 27B
Vectara Hallucination Rate11.7%12.1%
LMArena Expert14241428
GPQA Diamond83.3%—
SimpleQA Verified32.2%—

Multimodal Not comparable

GLM-4.7: —, Qwen3.5 27B: 39.4 (#59)

BenchmarkGLM-4.7Qwen3.5 27B
LMArena Vision—1241

Multilingual GLM-4.7 leads

GLM-4.7: 52.8 (#79), Qwen3.5 27B: 50.8 (#115)

BenchmarkGLM-4.7Qwen3.5 27B
LMArena Non-English14171390
LMArena Chinese14951478
LMArena French14321410
LMArena German14241393
LMArena Japanese14391345
LMArena Korean13991358
LMArena Russian14231390
LMArena Spanish14341407

Instruction Following Too close to call

GLM-4.7: 74.4 (#95), Qwen3.5 27B: 73.5 (#119)

BenchmarkGLM-4.7Qwen3.5 27B
LMArena Instruction Following14111393

Long Context Too close to call

GLM-4.7: 42.8 (#116), Qwen3.5 27B: 43.1 (#106)

BenchmarkGLM-4.7Qwen3.5 27B
LMArena Longer Query14321413
CL-bench15.9%—
CL-bench Life10.9%—

Writing & Preference GLM-4.7 leads

GLM-4.7: 60.9 (#93), Qwen3.5 27B: 59.3 (#111)

BenchmarkGLM-4.7Qwen3.5 27B
LMArena Text14351409
LMArena Creative Writing14011362
LMArena Multi-Turn14461410
EQ-Bench Creative Writing1413—

Frequently asked questions

Is GLM-4.7 better than Qwen3.5 27B?

GLM-4.7 and Qwen3.5 27B score almost the same on the Noometry Index (42.0 vs 41.9), so choose on price, context window or the category you care about most.

Which is cheaper, GLM-4.7 or Qwen3.5 27B?

Qwen3.5 27B is cheaper. It lists at $0.30 per million input tokens and $2.40 per million output tokens; GLM-4.7 lists at $0.60 and $2.20.

Is GLM-4.7 or Qwen3.5 27B better for coding?

GLM-4.7 scores higher on coding benchmarks: 44.0 versus 38.9 in the Noometry coding category.

Which has the bigger context window?

Qwen3.5 27B does, with 262K tokens against 205K.

How many benchmarks do GLM-4.7 and Qwen3.5 27B share?

21 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Qwen3.5 27B has 28.

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