Noometry

Model comparison

GLM-4.7 vs QwQ-32B

GLM-4.7 is the stronger model overall, scoring 42.0 to 39.8 on the Noometry Index.

Last verified . 22 shared benchmarks.

GLM-4.7 Z.ai (Zhipu)

42.0

Rank #124 Confirmed

QwQ-32B Alibaba (Qwen)

39.8

Rank #159 Confirmed

Summary

  • They share 22 benchmarks with published results for both. GLM-4.7 scores higher in 7 categories and QwQ-32B in 1 category; 6 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where GLM-4.7 leads 60.9 to 50.6.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 83.3% for GLM-4.7 and 59.2% for QwQ-32B.

Side by side

GLM-4.7QwQ-32B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index42.039.8
Released2025-12-222024-11-28
WeightsOpenOpen
Context window205K—
Max output131K—
Input $ / M tokens$0.60—
Output $ / M tokens$2.20—
Results tracked3636

Sponsored placements are available on pages like this one. Advertise on Noometry

Category by category

Coding GLM-4.7 leads

GLM-4.7: 44.0 (#79), QwQ-32B: 35.4 (#226)

BenchmarkGLM-4.7QwQ-32B
LMArena Coding14541333
Aider Polyglot—20.9%
LMArena WebDev1435—
SciCode45.1%—
BigCodeBench Instruct—44.6%
LiveBench Coding—72.2%
BigCodeBench Complete—54.4%
ALE-Bench399.48—

Agentic & Tool Use Not comparable

GLM-4.7: 26.5 (#103), QwQ-32B: —

BenchmarkGLM-4.7QwQ-32B
Terminal-Bench33.4%—
Vending-Bench 22,377—

Reasoning Too close to call

GLM-4.7: 24.3 (#164), QwQ-32B: 23.7 (#174)

BenchmarkGLM-4.7QwQ-32B
Chess Puzzles6%5%
LMArena Hard Prompts14431325
Epoch Capabilities Index143.51137.6
SimpleBench47.7%—
CritPt1.7%—
LiveBench Reasoning—83.5%
LiveBench Data Analysis—65%
ForecastBench—58.3
LiveBench—72%

Math Too close to call

GLM-4.7: 38.6 (#135), QwQ-32B: 38.0 (#143)

BenchmarkGLM-4.7QwQ-32B
OTIS Mock AIME 2024-202583.3%59.2%
LMArena Math14231359
ProofBench6%—
LiveBench Math—77.8%
FrontierMath (Feb 2025 set)2.4%—
FrontierMath Tier 4 (v1)0%—

Knowledge GLM-4.7 leads

GLM-4.7: 47.0 (#80), QwQ-32B: 37.2 (#158)

BenchmarkGLM-4.7QwQ-32B
GPQA Diamond83.3%65.3%
LMArena Expert14241324
SimpleQA Verified32.2%—
Confabulations—15.6%
Vectara Hallucination Rate11.7%—

Multilingual GLM-4.7 leads

GLM-4.7: 52.8 (#79), QwQ-32B: 44.8 (#176)

BenchmarkGLM-4.7QwQ-32B
LMArena Non-English14171305
LMArena Chinese14951378
LMArena French14321336
LMArena German14241313
LMArena Japanese14391262
LMArena Korean13991279
LMArena Russian14231297
LMArena Spanish14341354

Instruction Following GLM-4.7 leads

GLM-4.7: 74.4 (#95), QwQ-32B: 72.6 (#137)

BenchmarkGLM-4.7QwQ-32B
LMArena Instruction Following14111297
LiveBench Instruction Following—81.8%

Long Context QwQ-32B leads

GLM-4.7: 42.8 (#116), QwQ-32B: 49.0 (#11)

BenchmarkGLM-4.7QwQ-32B
LMArena Longer Query14321308
Fiction.LiveBench—83.3%
CL-bench15.9%—
CL-bench Life10.9%—

Writing & Preference GLM-4.7 leads

GLM-4.7: 60.9 (#93), QwQ-32B: 50.6 (#180)

BenchmarkGLM-4.7QwQ-32B
LMArena Text14351329
LMArena Creative Writing14011288
EQ-Bench Creative Writing14131257
LMArena Multi-Turn14461314
Short-Story Creative Writing—80.2%
LiveBench Language—51.4%

Frequently asked questions

Is GLM-4.7 better than QwQ-32B?

GLM-4.7 is the stronger model overall, scoring 42.0 to 39.8 on the Noometry Index.

Is GLM-4.7 or QwQ-32B better for coding?

GLM-4.7 scores higher on coding benchmarks: 44.0 versus 35.4 in the Noometry coding category.

How many benchmarks do GLM-4.7 and QwQ-32B share?

22 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and QwQ-32B has 36.

Related comparisons

Go deeper