Noometry

Model comparison

GLM-4.7 vs Qwen3.8 27B

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

Last verified . 23 shared benchmarks.

GLM-4.7 Z.ai (Zhipu)

42.0

Rank #124 Confirmed

Qwen3.8 27B Alibaba (Qwen)

46.0

Rank #68 Confirmed

Summary

  • They share 23 benchmarks with published results for both. GLM-4.7 scores higher in 2 categories and Qwen3.8 27B in 7 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where Qwen3.8 27B leads 41.0 to 24.3.
  • The biggest single-benchmark swing is ProofBench: 6% for GLM-4.7 and 16% for Qwen3.8 27B.
  • GLM-4.7 is cheaper at $0.60 / $2.20 per million input/output tokens, against $0.99 / $1.49 for Qwen3.8 27B.
  • Qwen3.8 27B accepts more context: 262K tokens versus 205K.

Side by side

GLM-4.7Qwen3.8 27B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index42.046.0
Released2025-12-222026-08-14
WeightsOpenOpen
Context window205K262K
Max output131K33K
Input $ / M tokens$0.60$0.99
Output $ / M tokens$2.20$1.49
Results tracked3631

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Category by category

Coding Qwen3.8 27B leads

GLM-4.7: 44.0 (#79), Qwen3.8 27B: 50.5 (#44)

BenchmarkGLM-4.7Qwen3.8 27B
LMArena WebDev14351593
SciCode45.1%46.6%
LMArena Coding14541482
ALE-Bench399.48—

Agentic & Tool Use Qwen3.8 27B leads

GLM-4.7: 26.5 (#103), Qwen3.8 27B: 32.9 (#57)

BenchmarkGLM-4.7Qwen3.8 27B
Terminal-Bench33.4%—
APEX-Agents—47.5%
Vending-Bench 22,377—

Reasoning Qwen3.8 27B leads

GLM-4.7: 24.3 (#164), Qwen3.8 27B: 41.0 (#54)

BenchmarkGLM-4.7Qwen3.8 27B
CritPt1.7%5.4%
LMArena Hard Prompts14431460
Epoch Capabilities Index143.51149.38
ARC-AGI-2—42.4%
SimpleBench47.7%—
NYT Connections (extended)—54.5%
ARC-AGI-1—87.5%
Chess Puzzles6%—
DTBench—88%
LMCA—41.4%
Surface Evolver Bench—45%

Math GLM-4.7 leads

GLM-4.7: 38.6 (#135), Qwen3.8 27B: 37.1 (#161)

BenchmarkGLM-4.7Qwen3.8 27B
ProofBench6%16%
LMArena Math14231456
OTIS Mock AIME 2024-202583.3%—
FrontierMath (Feb 2025 set)2.4%—
FrontierMath Tier 4 (v1)0%—

Knowledge GLM-4.7 leads

GLM-4.7: 47.0 (#80), Qwen3.8 27B: 41.6 (#109)

BenchmarkGLM-4.7Qwen3.8 27B
LMArena Expert14241482
GPQA Diamond83.3%—
SimpleQA Verified32.2%—
Vectara Hallucination Rate11.7%—

Multimodal Not comparable

GLM-4.7: —, Qwen3.8 27B: 41.3 (#37)

BenchmarkGLM-4.7Qwen3.8 27B
LMArena Vision—1271

Multilingual Too close to call

GLM-4.7: 52.8 (#79), Qwen3.8 27B: 53.7 (#60)

BenchmarkGLM-4.7Qwen3.8 27B
LMArena Non-English14171430
LMArena Chinese14951504
LMArena French14321465
LMArena German14241438
LMArena Japanese14391384
LMArena Korean13991393
LMArena Russian14231415
LMArena Spanish14341448

Instruction Following Qwen3.8 27B leads

GLM-4.7: 74.4 (#95), Qwen3.8 27B: 75.8 (#53)

BenchmarkGLM-4.7Qwen3.8 27B
LMArena Instruction Following14111439

Long Context Qwen3.8 27B leads

GLM-4.7: 42.8 (#116), Qwen3.8 27B: 44.3 (#70)

BenchmarkGLM-4.7Qwen3.8 27B
LMArena Longer Query14321450
CL-bench15.9%—
CL-bench Life10.9%—

Writing & Preference Qwen3.8 27B leads

GLM-4.7: 60.9 (#93), Qwen3.8 27B: 65.8 (#43)

BenchmarkGLM-4.7Qwen3.8 27B
LMArena Text14351441
LMArena Creative Writing14011384
EQ-Bench Creative Writing14131671
LMArena Multi-Turn14461441

Frequently asked questions

Is GLM-4.7 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-4.7 or Qwen3.8 27B?

GLM-4.7 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.8 27B lists at $0.99 and $1.49.

Is GLM-4.7 or Qwen3.8 27B better for coding?

Qwen3.8 27B scores higher on coding benchmarks: 50.5 versus 44.0 in the Noometry coding category.

Which has the bigger context window?

Qwen3.8 27B does, with 262K tokens against 205K.

How many benchmarks do GLM-4.7 and Qwen3.8 27B share?

23 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Qwen3.8 27B has 31.

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